极T代谢磁共振全球科研集锦

95

10 of 17 LARSON ET AL.

FIGURE 6 Sensitivity of metabolic rate estimates when using the fitting with input method, evaluating the response to fixing versus fitting the

bolus parameters

These results show that all approaches reduced the sensitivity of kPL estimates to errors in the bolus characteristics. Fitting the arrival time while

fixing the bolus duration, while introducing some increased bias with errors in bolus duration, provided an improvement in the expected variability.

As expected, fitting the bolus duration while fixing the arrival time introduced increased bias with errors in arrival time, but provided a further

improvement in the expected variability. Fitting both Tarrival and Tbolus reduced the sensitivity to Tbolus and Tarrival variations. Interestingly, fitting both

parameters also had little to no penalty on the expected kPL variance across most parameters and performed comparably to fitting only one of these

parameters. There was some bias when fitting both parameters, which was largest with a low SNR (휎 = .01) of −5%, although it was relatively small

in most cases.

3.1.5 kPL fitting for other flip-angle strategies

We have also applied the simulation framework for evaluating various analysis methods to other flip-angle strategies, presented in the Supporting

Information. The strategies presented there include a constant 10◦ for all time and metabolites, as well as a “multiband” 10◦ (pyruvate)–20◦ (lactate)

strategy that has been used in several prostate1 and brain5 human HP pyruvate exams.

When using fixed T1L and bolus characteristics, the inputless kPL fitting and calibrated AUCratio behave very similarly across both of the

constant-in-time flip-angle strategies shown: neither suffers from bias with variations in bolus characteristics or T1P. The inputless method has

极T代谢磁共振全球科研集锦

96

LARSON ET AL. 11 of 17

FIGURE 7 Sample in vivo maps of the metabolic AUCs as well as all fit parameters. T2 prostate lesions are highlighted by the green arrows in the

T2-weighted anatomical reference images. The tSNR and kPL maps are windowed independently for each subject. The three kPL maps are

windowed identically within each subject. All time values (mean time pyr, T1L, Tarrival and Tbolus) are in s and are windowed identically between

subjects. Fit parameters were only computed and shown where tSNRpyruvate > 80 in order to provide reliable fits

极T代谢磁共振全球科研集锦

97

12 of 17 LARSON ET AL.

improved precision slightly. Fitting with input suffers from bias with variations in bolus characteristics or T1P. All methods have bias with variations

in T1L and errors in the RF transmit power.

3.2 Human studies

Based on the simulation results described above, we chose to apply the calibrated AUCratio, inputless kPL fitting using a fixed T1L, inputless kPL fitting

including T1L fitting, and fitting with input using fixed T1L but fitting Tarrival and Tbolus to the human prostate datasets.

Sample parameter maps across a range of patients are shown in Figure 7. In general, there is a strong spatial agreement between the AUCratio and

kPL from inputless fitting, both with fitting and with fixed T1L, which also showed good correspondence with T2 lesions. Mean pyruvate time maps

indicate some variability in pyruvate arrival across the prostate. However, there are noticeable disagreements between the inputless kPL fitting

results (labeled kPL,fixed-T1L and kPL,fit-T1L) and the fitting including bolus input characteristics, kPL,withinput. The maps for bolus arrival and duration, Tarrival

and Tbolus, also showed relatively high heterogeneity, suggesting that the fitting with input was unstable in the human data.

The results of the inputless kPL fitting are summarized in Figure 8. Notably, the relationship between the calibratedAUCratio and kPL is quite variable

across studies. A likely explanation for this difference is variability in pyruvate delivery time between subjects, which is expected from the simulation

results in Figure 4. These showed high sensitivity of the calibrated AUCratio to variations in Tarrival and Tbolus. The mean pyruvate time values support

this explanation, where studies with earlier mean pyruvate times have higher AUCratio versus kPL slope and vice versa.

The results of the inputless kPL fitting including T1L fitting are summarized in Figure 9. The relationship between the calibrated AUCratio and kPL

was also variable across studies with this fitting approach, similarly to Figure 8. The T1L fitting in the prostate had average values within a subject

between 20 and 30 s and a standard deviation of around 10 s in most subjects. This highlights the relative instability of this fitting, which is somewhat

expected based on high variability in the simulation results when fitting T1L (Figure 5). Note that T1L was constrained during fitting to be between

15 and 35 s in an attempt to introduce some stability to these measurements. In the majority of voxels, the fitting hit these limits, as demonstrated

by a standard deviation of around 10 s in most subjects.

FIGURE 8 Summary of in vivo quantifications using the inputless kPL fitting with fixed T1L, where each color represents a different study. (a)

Comparison of the calibrated AUCratio and kPL values from inputless fitting with a fixed T1L across all prostate voxels. Eight representative studies

are shown for clear visualization. Linear fits between these parameters are shown as dashed lines. (b) Normalized histograms of the mean

pyruvate time (T휇,pyr in Equation (13)) for the same subjects as in (a), to provide a measure of pyruvate delivery time variations. (c) Comparison of

the linear fitting between the calibrated AUCratio and kPL versus the average T휇,pyr, for all 17 studies

极T代谢磁共振全球科研集锦

98

LARSON ET AL. 13 of 17

FIGURE 9 Summary of in vivo quantifications using inputless kPL fitting, with fitting T1L. (a) Comparison of the calibrated AUCratio and kPL values

across all prostate voxels for the same eight representative studies as in Figure 8. Linear fits between these parameters are shown as dashed lines.

(b) Mean and standard deviation of fit T1L values in the prostate from voxels with lactate tSNRlac > 20

FIGURE 10 Summary of in vivo quantifications using kPL fitting with input and a fixed T1L. (a) Comparison of the calibrated AUCratio and kPL values

across all prostate voxels for the same eight representative studies as in Figures 8 and 9. Linear fits between these parameters are shown as

dashed lines. (b) Comparison of the Tbolus and Tarrival fits in the prostate across all studies, showing mean and standard deviation of both

parameters. (c) Comparison of average Tbolus and Tarrival fits with the average T휇,pyr in the prostate across all studies

极T代谢磁共振全球科研集锦

99

14 of 17 LARSON ET AL.

TABLE 2 Summary of analysis results in the prostate across all patient studies. The fit parameter values shown are based only

on prostate voxel data. kPL values shown used the input-less fitting with fixed T1L

Polarization Pyr conc. Mean pyr Max Pyr Max Lac Max kPL mean kPL mean mean mean

ID (%) (mM) time (s) tSNR tSNR (1/s) (1/s) T1L (s) Tarrival (s) Tbolus (s)

1 36.2 245 29.4 864.3 185.9 0.024 0.009 25.7 5.7 8.4

2 37.3 235 24.8 314.4 85.0 0.014 0.010 18.3 0.0 6.0

3 47.9 250 28.0 303.1 49.5 0.021 0.008 28.1 1.6 7.0

4 45.4 226 29.8 897.1 52.0 0.009 0.003 27.2 4.9 8.5

5 47 251 32.3 297.8 59.3 0.017 0.007 18.8 9.7 9.8

6 36.7 227 27.8 249.1 69.1 0.033 0.011 28.7 1.7 6.9

7 38.2 221 28.7 1415.0 136.4 0.025 0.009 25.0 3.5 8.6

8 40.9 249 27.5 1257.5 161.4 0.018 0.007 29.1 0.8 6.5

9 39.8 246 29.7 786.7 67.7 0.019 0.006 27.0 3.7 8.6

10 36.3 254 27.0 424.9 111.6 0.019 0.011 21.5 0.0 6.5

11 36.9 244 31.1 1008.5 66.3 0.021 0.007 21.8 7.8 9.5

12 39.1 241 30.6 509.5 61.2 0.024 0.008 20.1 4.7 9.2

13 38.1 225 29.7 859.4 120.2 0.014 0.007 24.4 3.8 8.4

14 38.5 246 29.1 189.2 141.4 0.049 0.018 27.4 3.0 8.1

15 29.8 251 29.5 334.0 62.5 0.023 0.007 28.7 5.3 7.7

16 42 235 28.0 703.5 195.5 0.024 0.011 27.6 1.1 6.7

17 43.6 244 30.0 276.3 64.4 0.030 0.008 26.8 4.5 8.5

Mean 39.6 241 29.0 628.8 99.4 0.023 0.009 25.1 3.6 7.9

Std Dev 4.5 10 1.7 378.1 48.7 0.009 0.003 3.6 2.7 1.1

The results of the kPL fitting with input and fixed T1L are summarized in Figure 10. The relationship between the calibrated AUCratio and kPL was

also variable across studies with this fitting approach, but with more spread of kPL values compared with the inputless fitting results in Figures 8

and 9. This suggests more instability in the fitting with input. The input fit parameters Tarrival and Tbolus were correlated with each other (R2 = 0.836)

and also with the mean pyruvate time metric (R2 = 0.841 between mean pyruvate time and Tarrival, R2 = 0.882 between mean pyruvate time and

Tbolus). Both metrics showed notable intersubject variability in bolus delivery characteristics. There was substantial variation in Tarrival and Tbolus in

the majority of subjects, suggesting relatively unstable fitting, because presumably the bolus delivery would be relatively similar within the prostate.

Note that Tarrival and Tbolus were constrained during fitting to 0–12 s and 6–10 s, respectively, in an attempt to introduce some stability to these

measurements.

Table 2 summarizes the experimental characteristics and fitting values in the prostate across all patients studied. Much like prostate cancer itself,

there is a heterogeneous range of SNR, delivery, and kPL across this group.

4 DISCUSSION

In this work we evaluated approaches for quantification of metabolism in human HP [1-13C]pyruvate studies of prostate cancer patients and

presented normative ranges of experimental parameters, including metabolic conversion rates. We chose to apply three methods—a calibrated

AUCratio and kPL fitting with and without an input function—for quantification of pyruvate-to-lactate metabolic conversion. Part of the motivation for

these methods was their simplicity, which we expected to translate into robustness for low SNR data. These were chosen from amongst numerous

approaches for kinetic modeling of HP MRI data for their anticipated robustness to SNR and potential applicability to various flip-angle schemes.

The AUCratio method24 is extremely appealing in its simplicity of implementation. Under conditions of either constant-in-time flip angles acquired

starting prior to bolus arrival or consistent bolus characteristics, AUCratio is proportional to kPL divided by an effective lactate relaxation rate. This

proportionality breaks down when data acquisition starts after bolus arrival, and with variability in T1L that changes the effective lactate relaxation

rate. To accommodate the variable flip-angle scheme, we used a calibrated AUCratio for comparisons, which was calculated based on the nominal

bolus characteristics and relaxation rates. However, our results showed that this calibrated AUCratio experiences strong variability when bolus characteristics deviate from the assumed values. This was also evident from the analysis of our in vivo data, where the relationship between kPL and

AUCratio was highly variable across patients. Therefore, AUCratio is not suitable for quantifying metabolism in our prostate cancer experiments.

The lactate time-to-peak (TTP) was also recently introduced as a model-free approach for estimating metabolism.14 In this prior work, lactate

TTP performed indistinguishably from the best kinetic model in both in vitro and in vivo datasets. We performed simulations of this, as well as a

“mean lactate time” (shown in Supporting Information), and found that both of these lactate-only model-free approaches performed poorly for the

range of SNR and kPL values found in our human prostate cancer data with several flip-angle strategies. However, when we performed simulations

using higher SNRs and and higher kPL values, as was the case in Daniels et al,14 the TTP and mean lactate time performed comparably to the inputless

极T代谢磁共振全球科研集锦

100

LARSON ET AL. 15 of 17

kPL fitting with fixed T1L and AUCratio, with the mean lactate time performing better than TTP with our variable flip-angle scheme. Based on these

simulations, we believe these metrics are not valuable given the typical SNR and kPL values observed in our prostate cancer experiments.

Fitting of metabolic rates is often performed with either known input function or fitting of bolus characteristics, i.e. the input function

parameters.13,16,20,22 We evaluated this approach in simulations, first assuming a known input function.We found that the kPL fitting was highly sensitive to errors in the assumed input function. Using this approach is also challenging for our data, as we could not consistently identify a region for

estimation of an input function. It may also be more challenging to capture the bolus in our studies, due to the low pyruvate flip angles at the start of

the experiments. Adding some fitting of bolus characteristics reduced the sensitivity to errors in the input function, but substantially increased the

expected variance in the kPL estimates. In vivo fitting results also showed large variance in fit bolus characteristics and associated kPL maps. Therefore, we conclude that fitting including an input function was either too sensitive to errors or not precise enough to quantify metabolism in our

prostate cancer experiments.

The inputless kPL fitting method, inspired by Khegai et al,17 is appealing in that it requires no explicit input function, reducing the number of

parameters to fit. This fitting method only fits one or two parameters, depending on whether T1L is a free parameter. In our simulations, we found

that this approach had a similar precision to both the calibrated AUCratio and kPL fitting including an input function, but with the major advantage

that it was completely insensitive to the bolus characteristics. We included assumptions of fixing the relaxation rates, T1P and T1L, in this method.

Simulations showed that variations in T1P do not affect the fit results. However, the remaining limitation of this approach is sensitivity to variations

in T1L. While simulations indicated that fitting T1L would be an unfavorable trade-off due to the increased expected variance in kPL estimates, the in

vivo results did not show any obvious increases in kPL variance. Another advantage of the inputless approach is that it can be applied readily across

any acquisition strategy, i.e. for any flip angle or timing scheme. Therefore, we conclude that inputless kPL fitting was the most robust method for

quantifying metabolism in our prostate cancer experiments.

We found the major limitation of the inputless kPL fitting method to be sensitivity to T1L. In the model, kPL and T1L are competing effects of the same

order (0.01–0.1 /s), which may make separating these poorly conditioned.35 For example, increased generation of lactate via kPL combined with an

increased decay rate T1L would give somewhat similar dynamics to both rates being decreased. We chose our fixed T1L = 25 s based on modeling

values from our prior prostate cancer human studies1; this was also used by Bankson et al.13 Better estimates of in vivo metabolite relaxation rates

will improve the reliability of kinetic modeling. Another consideration for future work are recent studies suggesting that the intracellular T1 rates of

carboxylic acids, including pyruvate and lactate, may be much shorter than those in the extracellular environment, which were reported as being as

short as 10 s.36 This remains an important factor for this approach and likely many HP 13C kinetic modeling approaches, due to the poor conditioning

of these models.

Another limitation of all approaches for our experimental strategy was sensitivity to flip-angle errors. The simulation results show the inputless

approach is more sensitive to B1 errors compared with the fitting with input approach. One possible explanation is that the inputless fitting suffers

from increased error propagation in the pyruvate signal. B1 error will introduce consistent errors in the calculation of the pyruvate state magnetization, P−

Z [n], P+

Z [n], when it is computed directly from the actual pyruvate signal using the flip-angle compensations in Equations 4-7. The fitting

with input was less sensitive to B1 errors, possibly because the pyruvate magnetization is fitted in the model and not calculated directly from the

actual signal. We also suspect that some fitting methods include fit parameters that can end up compensating for B1 effects. For example, adding T1

fitting to the inputless method (Figure 5) reduces the B1 sensitivity overall. However, this is not strictly true, as when more parameters are added

to the fitting with input (Figure 6), the B1 effects are worse. The bias introduced by flip-angle errors can be eliminated through B1 mapping. Fast

Bloch–Siegert B1 mapping has been demonstrated for HP 13C,37-39 and more recently has been performed in real-time during the HP experiment40

to minimize bias due to inaccurate B1 calibration or unknown B1 field variations.

One potential improvement we did not explore that most certainly warrants future investigation is a tissue model that includes a vascular

compartment within each imaging voxel. This has been recently investigated by Bankson et al,13 and was shown to be a more appropriate tissue

model, as evaluated by the Akaike Information Criteria in preclinical study data. Similarly to this article, their work also assumed a unidirectional

pyruvate-to-lactate model with fixed relaxation rates. They also used a measured vascular input function (VIF) based on pyruvate signal in the heart,

as well as assumed known blood volume fractions from dynamic contrast-enhanced (DCE) MRI and pyruvate extravasation rates. This additional

information likely helps to maintain model stability when including a vascular compartment within each imaging voxel, which requires assumptions

or fitting of additional parameters. One reason we have not yet pursued this model is that we have not found a reliable way to estimate an input

function in our prostate cancer data, but this will be the subject of future work. It may be possible to estimate the input function across all voxels in

order to use a more complex tissue model.

The fitting methods and simulation evaluation framework can readily be extended to other applications beyond prostate cancer and other

metabolic pathways beyond pyruvate to lactate (e.g. pyruvate to bicarbonate and/or alanine), as well as other experimental parameters.We provide

guidance in the Supporting Information on modifying the simulations in the hyperpolarized-mri-toolbox.32 This framework could also be used for

retrospective design of experimental parameters, such as flip angles and TR, to obtain the best estimates of kPL for expected SNR, conversion rates,

relaxation rates, and bolus characteristics.

5 CONCLUSION

We have demonstrated the ability of MRI with hyperpolarized carbon-13 pyruvate to provide quantitative assessments of prostate cancer

metabolism using dynamic imaging and kinetic modeling, and presented normative ranges of bolus delivery, SNR, and metabolic conversion rates in

极T代谢磁共振全球科研集锦

101

16 of 17 LARSON ET AL.

the prostate. This work is all based on dynamic imaging with kinetic modeling methods to provide estimates of metabolism that are independent of

the bolus delivery characteristics. The AUCratio method for quantification of metabolism is robust under conditions of constant-in-time flip angles

and when data are acquired starting before the bolus delivery, but is affected by variability in T1L. The inputless kPL fitting method was shown to

be relatively robust for low SNR data for all flip-angle schemes and bolus characteristics, but is also sensitive to variability in T1L. There were differences of over 10 s in bolus arrival measurements across studies and several fold differences in total SNR within the prostate, and this variability

must be accommodated by the acquisition and analysis methods used in future studies.

ACKNOWLEDGMENT

We thank Mary McPolin, Kimberly Okamoto, Dr Peter Shin and Dr Eugene Milshteyn for their assistance in performing the patient studies.

This work was supported by the National Institutes of Health [grant numbers R01EB017449, R01EB016741, R01CA183071, R01CA211150,

and P41EB013598], as well as receiving research support from GlaxoSmithKline and GE Healthcare.

ORCID

Peder E. Z. Larson http://orcid.org/0000-0003-4183-3634

Hsin-Yu Chen http://orcid.org/0000-0002-2765-1685

REFERENCES

1. Nelson SJ, Kurhanewicz J, Vigneron DB, et al. Metabolic imaging of patients with prostate cancer using hyperpolarized [1-13C]pyruvate. Sci Transl Med.

2013;5:198ra108.

2. Granlund K, Vargas HA, Lyashchenko SK, et al. Metabolic dynamics of hyperpolarized [1-13C] pyruvate in human prostate cancer. In Proceedings of the

World Molecular Imaging Congress. Honolulu, Hawaii; 2015: 1714-15. https://doi.org/10.1007/s11307-016-0968-3

3. Cunningham CH, Lau JYC, Chen AP, et al. Hyperpolarized 13C metabolic MRI of the human heart—Novelty and significance: Initial experience. Circ Res.

2016;119:1177-1182.

4. Aggarwal R, Vigneron DB, Kurhanewicz J. Hyperpolarized 1-[13C]-pyruvate magnetic resonance imaging detects an early metabolic response to

androgen ablation therapy in prostate cancer. European Urol. 2017;72(6):1028-1029.

5. Park I, Larson PEZ, Gordon JW, et al. Development of methods and feasibility of using hyperpolarized carbon-13 imaging data for evaluating brain

metabolism in patient studies. Magn Reson Med. 2018;80:864-873.

6. Miloushev VZ, Granlund KL, Boltyanskiy R, et al. Metabolic imaging of the human brain with hyperpolarized 13C pyruvate demonstrates 13c lactate

production in brain tumor patients. Cancer Res. 2018;78(14):3755-3760.

7. Ardenkjaer-Larsen JH, Leach AM, Clarke N, Urbahn J, Anderson D, Skloss TW. Dynamic nuclear polarization polarizer for sterile use intent.NMR Biomed.

2011;24:927-932.

8. Golman K, Zandt Ri, LercheM, Pehrson R, Ardenkjaer-Larsen JH.Metabolic imaging by hyperpolarized 13C magnetic resonance imaging for in vivo tumor

diagnosis. Cancer Res. 2006;66:10855-10860.

9. Cunningham CH, Chen AP, Lustig M, et al. Pulse sequence for dynamic volumetric imaging of hyperpolarized metabolic products. J Magn Reson.

2008;193:139-146.

10. Larson PEZ, Kerr AB, Chen AP, et al. Multiband excitation pulses for hyperpolarized 13c dynamic chemical-shift imaging. J Magn Reson. 2008;194:

121-7.

11. Albers MJ, Bok R, Chen AP, et al. Hyperpolarized 13c lactate, pyruvate, and alanine: Noninvasive biomarkers for prostate cancer detection and grading.

Cancer Res. 2008;68:8607-15.

12. Bahrami N, Swisher CL, Von Morze C, Vigneron DB, Larson PEZ. Kinetic and perfusion modeling of hyperpolarized (13)C pyruvate and urea in cancer

with arbitrary RF flip angles. Quant Imaging Med Surg. 2014;4:24-32.

13. Bankson JA, Walker CM, Ramirez MS, et al. Kinetic modeling and constrained reconstruction of hyperpolarized [1-13c]-pyruvate offers improved

metabolic imaging of tumors. Cancer Res. 2015;75:4708-17.

14. Daniels CJ, McLean MA, Schulte RF, et al. A comparison of quantitative methods for clinical imaging with hyperpolarized 13c-pyruvate. NMR Biomed.

2016;29:387-399.

15. Harrison C, Yang C, Jindal A, et al. Comparison of kinetic models for analysis of pyruvate-to-lactate exchange by hyperpolarized 13 C NMR.NMR Biomed.

2012;25:1286-94.

16. Kazan SM, Reynolds S, Kennerley A, et al. Kinetic modeling of hyperpolarized (13)C pyruvate metabolism in tumors using a measured arterial input

function. Magn Reson Med. 2013;70:943-53.

17. Khegai O, Schulte RF, Janich MA, et al. Apparent rate constant mapping using hyperpolarized [1-(13)C]pyruvate. NMR Biomed. 2014;27:1256-65.

18. Li LZ, Kadlececk S, Xu HN, et al. Ratiometric analysis in hyperpolarized NMR (I): Test of the two-site exchange model and the quantification of reaction

rate constants. NMR Biomed. 2013;26:1308-20.

19. Mariotti E, Orton MR, Eerbeek O, et al. Modeling non-linear kinetics of hyperpolarized [1-13c] pyruvate in the crystalloid-perfused rat heart. NMR

Biomed. 2016;29:377-386.

20. Sun C, Walker CM, Michel KA, Venkatesan AM, Lai SY, Bankson JA. Influence of parameter accuracy on pharmacokinetic analysis of hyperpolarized

pyruvate. Magn Reson Med. 2018;79:3239-3248.

21. Witney TH, Kettunen MI, Brindle KM. Kinetic modeling of hyperpolarized 13c label exchange between pyruvate and lactate in tumor cells. J Biol Chem.

2011;286:24572-24580.

极T代谢磁共振全球科研集锦

102

LARSON ET AL. 17 of 17

22. Zierhut ML, Yen YF, Chen AP, et al. Kinetic modeling of hyperpolarized 13c1-pyruvate metabolism in normal rats and TRAMP mice. J Magn Reson.

2010;202:85-92.

23. Xu T, Mayer D, Gu M, et al. Quantification of in vivo metabolic kinetics of hyperpolarized pyruvate in rat kidneys using dynamic 13c MRSI. NMR Biomed.

2011;24:997-1005.

24. Hill DK, OrtonMR, Mariotti E, et al. Model free approach to kinetic analysis of real-time hyperpolarized 13c magnetic resonance spectroscopy data. PLoS

One. 2013;e71996:8.

25. Larson PEZ, Hu S, LustigM, et al. Fast dynamic 3DMR spectroscopic imaging with compressed sensing andmultiband excitation pulses for hyperpolarized

13c studies. Magn Reson Med. 2011;65:610-9.

26. Zhao L, Mulkern R, Tseng CH, et al. Gradient-echo imaging considerations for hyperpolarized 129xe MR. J Magn Reson B. 1996;113:179-183.

27. Nagashima K. Optimum pulse flip angles for multi-scan acquisition of hyperpolarized NMR and MRI. J Magn Reson. 2008;190:183-188.

28. Xing Y, Reed GD, Pauly JM, Kerr AB, Larson PEZ. Optimal variable flip angle schemes for dynamic acquisition of exchanging hyperpolarized substrates.

J Magn Reson. 2013;234:75-81.

29. Larson P, Shang H. Spectral-Spatial-RF-Pulse-Design: Version 1.2; 2018.

30. Maidens J, Gordon JW, Arcak M, Larson PEZ. Optimizing flip angles for metabolic rate estimation in hyperpolarized carbon-13 MRI. IEEE Trans Med

Imaging. 2016;35:2403-2412.

31. Larson PEZ, Bok R, Kerr AB, et al. Investigation of tumor hyperpolarized [1-13c]-pyruvate dynamics using time-resolved multiband RF excitation

echo-planar MRSI. Magn Reson Med. 2010;63:582-591.

32. Larson PEZ, Kerr AB, Milshteyn E, Zhu X, Maidens J, Gordon JW. hyperpolarized-mri-toolbox: Version 1.2; 2018.

33. Tropp J, Calderon P, Vigneron DB. Systems, methods and apparatus for an endo-rectal receive-only probe. US Patent 7 945 308; 2011.

34. Kurhanewicz J, Vigneron D, Carroll P, Coakley F. Multiparametric magnetic resonance imaging in prostate cancer: Present and future. Curr Opin Urol.

2008;18:71-7.

35. Swisher CL, Larson PEZ, Kruttwig K, et al. Quantitative measurement of cancer metabolism using stimulated echo hyperpolarized carbon-13 MRS. Magn

Reson Med. 2014;71:1-11.

36. Karlsson M, Jensen PR, Ardenkjær-Larsen JH, Lerche MH. Difference between extra- and intracellular T1 values of carboxylic acids affects the

quantitative analysis of cellular kinetics by hyperpolarized NMR. Angew Chem Int Ed. 2016;55:13567-13570.

37. Sacolick LI, Wiesinger F, Hancu I, Vogel MW. B1 mapping by Bloch–Siegert shift. Magn Reson Med. 2010;63:1315-22.

38. Schulte RF, Sacolick L, Deppe MH, et al. Transmit gain calibration for nonproton MR using the Bloch–Siegert shift. NMR Biomed. 2011;24:1068-72.

39. Lau AZ, Chen AP, Cunningham CH. Integrated Bloch–Siegert B1 mapping and multislice imaging of hyperpolarized 13C pyruvate and bicarbonate in the

heart. Magn Reson Med. 2012;67:62-71.

40. Tang S, Milshteyn E, Reed GD, et al. A regional bolus tracking and real-time B1 calibration method for hyperpolarized 13C MRI. Magn Reson Med. 2018.

In press. https://doi.org/10.1002/mrm.27391

SUPPORTING INFORMATION

Additional supporting information may be found online in the Supporting Information section at the end of the article.

How to cite this article: Larson PEZ, Chen H-Y, Gordon JW, et al. Investigation of analysis methods for hyperpolarized 13C-pyruvate

metabolic MRI in prostate cancer patients. NMR in Biomedicine. 2018;e3997. https://doi.org/10.1002/nbm.3997

极T代谢磁共振全球科研集锦

103

Characterization of serial hyperpolarized

13C metabolic imaging in patients with

glioma

研究背景

研究对象

研究过程

ג极ࣅ༐ 13 (HP-13C) MRI ๟ᅃዖᆩᇀ༑֪స代谢ݥڦ෇ൔႠׯၟरຍLj੗߀฀ଣضҵኢ֪॔ă Ԩ研৯ኼሞ՗ኙথ๴

స዗ୀዎଐ࣒ڦኁዐ૶Ⴤ HP-13C ׯၟڦᅃዂႠLjժඓۨᇑኟ׉ྔ࠵ፇኯ၎ԲLj዗ୀթՎዐ๟ޏ٪ሞᅴ׉代谢ă

3 ఁॳ੃ኾᇼኁࢅ 5 ఁ௜஦৐ාႠঌዊୀ࣒ኁ

ۯༀג极ࣅ༐13(HP-13C)MRIᅙׯ࿮้ํྺظ༑༹֪ా代谢ڦഽۇٴ

रຍă๑ᆩጆோڦᅏഗཚۯࡗༀࢃ极ࣅ)DNP)ຨ้ሺഽ13Cڦࢃ႑

ࡽLjHP-13Cׯ༵ၟࠃକॠ֪Քऻݴጱሞৢஞጀพڦࢫ代谢ገڦࣅీ

૰ă

ሞටసዐLjHP [1-13C] եཛྷ໗ཚࡗԇॾළ໗ྃൠா (LDH) ாٝገࣅྺ

[1-13C] ළ໗Ljཚࡗ၍૭༹եཛྷ໗ྃൠா (PDH) ࢅ༐໗᷿ா (CA) ாٝገ

ࣅ] ྺ13C] ༐໗ൠჸLjܸ༵ٗࠃᅃዖമ໯࿄ᆶڦ༑໭༛ট঴ࢅᄟࣅଡ

໗ࣅ཰০ݛڦ݆ă

६ᇀ཭؜ᅴ׉ҵኢ代谢ڦയ૰Ljঌዊୀ࣒ኁڦ HP-13C ׯၟ༬ኙႴ༬

՚ഽۙă

Ԩ研৯ڦణڦ๟ሞॳ੃ኾᇼኁࢅথ๴หঢ়ঌዊୀዎଐ࣒ڦኁዐLj๑

ᆩۯ૰ბॺఇݛ݆ઠ՗ኙ૶Ⴤۯༀ HP-13C ׯၟăԲড NAWM ాڦ՗

࠵] 1-13C] եཛྷ໗代谢Ljժೠݥࠚጲᇼኁᇑ࣒ኁኮक़ڦՎࣅॠֱLjᅜ

त዗ୀթՎాڦ代谢၎ܔᇀ NAWM ڦՎࣅă

极T代谢磁共振全球科研集锦

104

研究结果

结 论

应用方向

๑ᆩ۠ेሞ T2 े඄ FLAIR ࢅᮕࢫ T1 े඄཮ၟฉڦ kPL ཮၂๖ݣพბۨᅭڦ৊ቛ้ׯڦၟă ࣒ኁ P5 ሞ੣क़ᅴዊႠթՎ

ዐ՗၄؜ืڦߛ kPLLjᇑዜྷڦ T2L ၎ԲLjCEL ాࢅዜྷڦኵ߸ߛ) A)Ǘ ܸ࣒ኁ P2 ၂๖؜਩ሌืڦߛ kPLLjٗ CEL ၠᇺ

܋ჽฦڟ T2LLjසӣ෥ॱཀྵ (B) ໯๖ă ࣒ኁ P4 ዐݘ࠽ืڦߛ kPL ܔᆌᇀᅜ᪓᪋༹ྺዐ႐ժჽฦ዁ፑ֨ܮᄾӣዊࢅೄዊ

DŽӣ෥ॱཀྵDžDŽCDžݥڦۇٴڦሺഽ T2Lă ሞ௅ዖ൧઄ူLjթՎ kPL ۼ཭؜କݣพბ৊ቛă

หঢ়ঌዊୀ࣒ኁڦ HP-13C ဣଚຕ਍ۯڦ૰ბఇ႙၂๖Lj໏୲׉ຕ kPL-NAWM ࢅ kPB-NAWM ሞጻၠฉᅃዂLjᇑॳ੃ኾ

ᇼኁڦ୲໏׉ຕएԨ၎ຼăੇე࠶ิׯवԞ݃ڇຼੇࢭᇑ՗࠵୲໏׉ຕڦኝ༹ืߛᆶ࠲Ljኄ੗ీ๟ᆯᇀ [1-13C] եཛྷ

໗ཚࡗ BBB ڦྔา३ณ໯ዂă ሞदթ৊ቛ࣒ڦኁዐLjᮕሺഽݥࢅሺഽթՎڦ kPL ᄺืߛLjയሞںഽۙକᅃဣଚหঢ়

ঌዊୀჱ႙ڦᅴ׉代谢Ljժኧ׼ HP-13C ׯၟڦၳᆩă

సҵࢅ዗ୀ

极T代谢磁共振全球科研集锦

105

Contents lists available at ScienceDirect

NeuroImage: Clinical

journal homepage: www.elsevier.com/locate/ynicl

Characterization of serial hyperpolarized 13C metabolic imaging in patients

with glioma

Adam W. Autrya, Jeremy W. Gordona, Hsin-Yu Chena, Marisa LaFontainea, Robert Boka,

Mark Van Criekingea, James B. Slatera, Lucas Carvajala, Javier E. Villanueva-Meyera,

Susan M. Changb

, Jennifer L. Clarkeb, Janine M. Lupoa, Duan Xua, Peder E.Z. Larsona,

Daniel B. Vignerona,c, Yan Lia,⁎

a Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA

b Department of Neurological Surgery, University of California San Francisco, San Francisco, USA

c Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, USA

ARTICLE INFO

Keywords:

Hyperpolarized

Carbon-13

Metabolism

Kinetics

Glioma

Bevacizumab

ABSTRACT

Background: Hyperpolarized carbon-13 (HP-13C) MRI is a non-invasive imaging technique for probing brain

metabolism, which may improve clinical cancer surveillance. This work aimed to characterize the consistency of

serial HP-13C imaging in patients undergoing treatment for brain tumors and determine whether there is evidence of aberrant metabolism in the tumor lesion compared to normal-appearing tissue.

Methods: Serial dynamic HP [1-13C]pyruvate MRI was performed on 3 healthy volunteers (6 total examinations)

and 5 patients (21 total examinations) with diffuse infiltrating glioma during their course of treatment, using a

frequency-selective echo-planar imaging (EPI) sequence. HP-13C imaging at routine clinical timepoints overlapped treatment, including radiotherapy (RT), temozolomide (TMZ) chemotherapy, and anti-angiogenic/investigational agents. Apparent rate constants for [1-13C]pyruvate conversion to [1-13C]lactate (kPL) and [13C]

bicarbonate (kPB) were simultaneously quantified based on an inputless kinetic model within normal-appearing

white matter (NAWM) and anatomic lesions defined from 1

H MRI. The inter/intra-subject consistency of kPLNAWM and kPB-NAWM was measured in terms of the coefficient of variation (CV).

Results: When excluding scans following anti-angiogenic therapy, patient values of kPL-NAWM and kPB-NAWM were

0.020 s−1 ± 23.8% and 0.0058 s−1 ± 27.7% (mean ± CV) across 17 HP-13C MRIs, with intra-patient serial

kPL-NAWM/kPB-NAWM CVs ranging 6.8–16.6%/10.6–40.7%. In 4/5 patients, these values (0.018 s−1 ± 13.4% and

0.0058 s−1 ± 24.4%; n = 13) were more similar to those from healthy volunteers (0.018 s−1 ± 5.0% and

0.0043 s−1 ± 12.6%; n = 6) (mean ± CV). The anti-angiogenic agent bevacizumab was associated with global

elevations in apparent rate constants, with maximum kPL-NAWM in 2 patients reaching 0.047 ± 0.001 and

0.047 ± 0.003 s−1 ( ± model error). In 3 patients with progressive disease, anatomic lesions showed elevated

kPL relative to kPL-NAWM of 0.024 ± 0.001 s−1 ( ± model error) in the absence of gadolinium enhancement, and

0.032 ± 0.008, 0.040 ± 0.003 and 0.041 ± 0.009 s−1 with gadolinium enhancement. The lesion kPB in

patients was reduced to unquantifiable values compared to kPB-NAWM.

Conclusion: Serial measures of HP [1-13C]pyruvate metabolism displayed consistency in the NAWM of healthy

volunteers and patients. Both kPL and kPB were globally elevated following bevacizumab treatment, while progressive disease demonstrated elevated kPL in gadolinium-enhancing and non-enhancing lesions. Larger prospective studies with homogeneous patient populations are planned to evaluate metabolic changes following

treatment.

https://doi.org/10.1016/j.nicl.2020.102323

Received 28 April 2020; Received in revised form 15 June 2020; Accepted 21 June 2020

Abbreviations: HP-13C, hyperpolarized carbon-13; DNP, dynamic nuclear polarization; RT, radiotherapy; TMZ, temozolomide; PDH, pyruvate dehydrogenase; LDH,

lactate dehydrogenase; CA, carbonic anhydrase ⁎ Corresponding author at: Department of Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry Street, Ste. 350, San Francisco, CA

94107, USA.

E-mail address: yan.li@ucsf.edu (Y. Li).

1HXUR,PDJH&OLQLFDO

$YDLODEOHRQOLQH-XQH

‹7KH$XWKRUV3XEOLVKHGE\\(OVHYLHU,QF7KLVLVDQRSHQDFFHVVDUWLFOHXQGHUWKH&&%<1&1'OLFHQVH

KWWSFUHDWLYHFRPPRQVRUJOLFHQVHV%<1&1'

极

T代谢磁共振全球科研集锦

106

1. Introduction

Dynamic hyperpolarized carbon-13 (HP-13C) MRI has emerged as a

powerful technique for non-invasively probing in vivo metabolism in

real time. By utilizing specialized instrumentation to transiently enhance the signal of 13C nuclei via dynamic nuclear polarization (DNP),

HP-13C imaging provides the ability to detect metabolic conversion of

labeled molecules following their intravenous injection (ArdenkjaerLarsen et al., 2003).

Among the diverse applications of HP-13C imaging, interrogating

brain metabolism remains a principal investigational interest. After

demonstrating the feasibility of using HP [1-13C]pyruvate as a molecular probe in preclinical animal studies (Golman et al., 2006; Park

et al., 2014); early human trials importantly showed that this molecule

is safely and rapidly transported across the blood brain barrier (BBB)

over experimental timescales (Park et al., 2018; Miloushev et al., 2018).

In the human brain, HP [1-13C]pyruvate undergoes enzymatic conversion to [1-13C]lactate via cytosolic lactate dehydrogenase (LDH) and [

13C]bicarbonate via mitochondrial pyruvate dehydrogenase (PDH)

and carbonic anhydrase (CA), thus providing an unprecedented means

of probing glycolytic and oxidative phosphorylation pathways (Fig. 1)

(Lunt and Vander Heiden, 2011; Saraste, 1999). Recent studies in

healthy volunteers have reported on the regional variation of brain

metabolism, as well as patterns of metabolite production that are

conserved over a wide age range (Grist et al., 2019; Lee et al., 2020).

Given the potential for highlighting aberrant cancer metabolism,

particular emphasis has been placed on characterizing HP-13C imaging

in patients with gliomas (Park et al., 2018; Miloushev et al., 2018).

Diffuse infiltrating gliomas comprise a heterogeneous class of brain

tumors, which are graded according to malignancy using histopathologic and molecular criteria (Louis et al., 2016). While the most

common and aggressive form of this disease is grade IV glioblastoma

(GBM), patients who are initially diagnosed with grade II or III glioma

may undergo malignant transformation to higher grades (Chaichana

et al., 2010). In the case of GBM, standard-of-care treatment currently

includes maximal surgical resection, radiation therapy (RT) and concurrent temozolomide (TMZ) chemotherapy, followed by 6 months of

adjuvant TMZ (Stupp et al., 2005). Since the effects of standard and

adjuvant therapies can often mimic or even mask disease using routine

anatomic

1

H MRI, HP-13C imaging may assist in monitoring response to

treatment (Winter et al., 2019; Da Cruz et al., 2011).

The purpose of the current study was to characterize serial dynamic

HP-13C imaging using a kinetic modeling approach (Larson et al., 2018

)

in healthy volunteers and patients who received treatment for glioma.

Apparent [1-13C]pyruvate metabolism within NAWM was compared in

volunteers versus patients and evaluated for variation across examinations, while metabolism within tumor lesions was assessed for

alterations relative to NAWM.

2. Methods

2.1. 13C hardware and calibration

All experiments were performed on a clinical 3 T whole body

scanner (MR 750; GE Healthcare, Waukesha, WI) equipped with 32-

channel multi-nuclear imaging capability. Details of the 13C receiver

and transmit coil hardware are contained in Supplementary Fig. 1.

Transmit RF power (TG) was calibrated using a 13C FID sequence with a

non-slice selective 90

○ pulse (GE Healthcare) on a head-shaped

phantom containing unenriched ethylene glycol (HOCH

2CH2OH, anhydrous, 99.8%, Sigma Aldrich, St. Louis, MO), doped with 17 g/L

(0.29 M) NaCl to recapitulate physiological loading (Autry et al., 2019).

2.2. Subject population and treatment

Three healthy volunteers andfive patients previously diagnosed

with infiltrating glioma (WHO grades II-IV) were recruited to the IRBapproved study following informed consent at the University of

California, San Francisco (Table 1). While the treatments prior to

HP-13C imaging varied across the patients, all had undergone surgery

(5/5) and a few had received chemoradiotherapy (RT/TMZ) (2/5) as

shown in Table 1. Over the course of serial HP-13C imaging, some patients had additional surgery (2/5), RT/TMZ (1/5), adjuvant RT (3/5),

bevacizumab (2/5), and other therapies further detailed in Table 1.

Supplementary Fig. 2 depicts individual patient treatment timelines and

their intervals of HP-13C imaging.

2.3. Sample polarization and QC

Hyperpolarization of [1-13C]pyruvate was performed on a SPINlab

system (General Electric, Niskayuna, NY) designed for clinical applications (Park et al., 2018). In order to maintain an ISO 5 environment,

pharmacists utilized an isolator (Getinge Group, Getinge, France) and

clean bench laminarflow hood for preparing pharmacy kits. Pharmacy

kitsfilled with a mixture of 1.432 g [1-13C]pyruvic acid (MilliporeSigma, Miamisburg, OH) and 28 mg electron paramagnetic agent

(EPA) (AH111501; GE Healthcare, Oslo, Norway) were loaded into the

SPINlab and polarized for at least 2.5 h with 140 GHz microwave radiation at 5 T and 0.8 K. Following polarization, the pyruvate and trityl

radical solution was rapidly dissolved in sterile water and passed

through afilter under pressure to achieve a residual trityl concentration

of < 3μM. This solution was then collected in a receiver vessel, neutralized, and diluted with a sodium hydroxide tris(hydroxymethyl)

aminomethane/ethylenediaminetetraacetic acid buffer solution. An

integrated quality control (QC) system rapidly measured the resulting

pH, temperature, residual EPA concentration, volume, pyruvate concentration, and polarization level. Upon completing the QC analysis,

the sample underwent terminal sterilization in afilter (0.2 µm; ZenPure, Manassas, VA) before being collected in a MEDRAD syringe

(Bayer HealthCare, Pittsburgh, PA).

Acceptable compounding tolerances for pharmacist release of the

sample were: 1) polarization≥15%; 2) pyruvate concentration,

220–280 mM; 3) EPA concentration≤3.0μM; 4) pH, 5.0–9.0; 5)

temperature, 25–37

○C; 6) volume > 38 mL; and 7) bubble point test

on sterilizingfilter passed at 50 psi. The injected volume of HP [1-13C]

Fig. 1. HP [1-13C]pyruvate brain metabolism. Diagram of HP [1-13C]pyruvate metabolism in the brain, which is characterized by two primary pathways: enzymatic conversion of [1-13C]pyruvate to [1-13C]lactate via cytosolic

lactate dehydrogenase (LDH); and successive conversion of [1-13C]pyruvate to 13CO2 and [13C]bicarbonate via mitochondrial pyruvate dehydrogenase (PDH)

and carbonic anhydrase (CA), respectively. The second-order kinetics of pyruvate-to-bicarbonate conversion are approximated by the rate-limiting step of

PDH, given the rapid CO

2-bicarbonate exchange catalyzed by CA. HP [1-13C]

pyruvate is also reversibly converted to [1-13C]alanine via alanine transaminase

(ALT), but prior studies have shown that conversion to HP [1-13C]alanine occurs outside of the brain (4).

A.W. Autry, et al. 1HXUR,PDJH&OLQLFDO 

极T代谢磁共振全球科研集锦

107

pyruvate was based on a 0.43 mL/kg dosage, delivered at a rate of

5 mL/s, and followed by a 20 mL sterile salineflush at the same rate.

Supplementary Table 1 contains the experimental QC and injection

parameters, summarized as follows by the mean and range of values:

polarization, 41% (36–51%); pyruvate concentration, 238 mM

(216–255 mM); EPA concentration, 1.1μM (0.3–2.4μM); pH, 7.5

(6.1–8.3); temperature, 32

○C (29

–36

○C); volume, 29 mL (20

–40 mL);

time-to-injection, 58 s (49–83 s).

2.4. Serial imaging protocol

After confirming that patient vital signs permitted administration of

HP contrast, an intravenous catheter was placed in the antecubital vein. T2-weighted fast spin echo (FSE) images (TR/TE = 4000/60 ms,

FOV = 26 cm, 192× 256 matrix, 5 mm slice thickness, and NEX = 2)

acquired with the

1

H body coil or dual-tuned 13C/

1

H hardware configuration served as an anatomic reference for prescribing 13C sequences. An embedded 1 mL 8 M 13C-urea sample in the receiver array

provided in vivo frequency referencing for [1-13C]pyruvate: fpyruvate = furea + 270 Hz. Following pharmacist approval of sample

safety, patients were injected with the HP [1-13C]pyruvate and dynamic

HP-13C echo-planar imaging (EPI) data were acquired beginning 5 s

after the end of the salineflush to allow for cerebral bolus arrival.

A frequency-selective 2D multislice EPI sequence (TR/

TE = 62.5 ms/21.7 ms, 24× 24 cm

2 FOV, 1032 µs echospacing, ± 10 kHz BW, 8 slices, 20 timepoints, 3 s temporal resolution,

60 s total acquisition time) with 2–8 cm

3 spatial resolution (3.38 cm3

for 76% of scans) was acquired for each subject (Gordon et al., 2017;

Gordon et al., 2019). Individual [1-13C]pyruvate, [1-13C]lactate, and [

13C]bicarbonate resonances were sequentially excited using a singleband spectral-spatial (SPSP) RF pulse (130 Hz FWHM, 868 Hz stopband

peak-to-peak) over interleaved acquisitions with a variableflip angle

scheme that was constant through time (67% of scans utilized [αpyr

,

αlac,αbic] = [20

○, 30○, 30○]). The [1-13C]alanine resonance was not

acquired as prior studies have shown that it is only present in subcutaneous tissue and muscle outside the brain (Park et al., 2018).

Supplementary Table 2 contains the complete set of acquisition parameters for each EPI scan. Noise-only data were acquired separately

prior to each HP injection. To measure relative metabolite frequencies

for quality assurance, non-localized spectra (TR = 3 s,θ = 60

○, 8 time

points) were immediately acquired after thefirst scan with a 500μs

hard pulse.

After this scan, post-injection vital signs were measured and patients

received a routine

1

H MR examination using the same dual-tuned

hardware configuration (8-channel) or a 32-channel

1

H coil (Nova

Medical Inc., Wilmington, MA). This exam included pre- and post-gadolinium contrast 3D T1-weighted IRSPGR images (TR/TE/TI = 6636/

2468/450 ms, resolution = 1.5×1× 1 mm

3

, 25.6 cm FOV,

256× 256 matrix) and 3DT2-weighted FLAIR images (TR/TE/

TI = 6250/138/1702 ms, resolution = 1.5×1× 1 mm

3

, 25.6 cm

FOV, 256× 256 matrix).

2.5. ROI segmentation

For each

1

H exam, white matter was segmented on the pre-contrast

T1-weighted images using the FSL FAST algorithm (Zhang et al., 2001),

and the FLAIRT2-hyperintense lesion (T2L) and post-gadolinium contrast-enhancing lesion (CEL) were manually segmented by a trained

researcher with 3D Slicer software (Menze et al., 2015). A normal-appearing white matter (NAWM) mask was generated by subtracting the

T2L from the segmented white matter. In cases where data was not

acquired with dual-tuned

1

H/13C hardware,

1

H images and ROIs were

aligned to the body coilT2-weighted FSE images acquired during the 13C exam using FSL FLIRT (Jenkinson and Smith, 2001). Individual 13C

data voxels were exclusively categorized as NAWM or T2L/CEL when at

least 30% of their volume contained the

1

Table 1Subject populationH ROIs. This was

. Subject demographics, clinical characterization, and lesion volume for healthy volunteers (HV) and patients (P). IDH, isocitrate dehydrogenase; GBM, glioblastoma; NA, not applicable; Sx, surgery;

CCNU, lomustine; RT, radiation therapy; TMZ, temozolomide.

Subject ID Diagnosis Prior disease status Age (yr),

Sex

No. serial scans (totaltimespan)

Prior treatment Treatment at the time of imaging T2L, CEL volume (cm3)

HV1 NA NA 41 M 1 NA NA NAHV2 NA NA 59 M 2 (30 min) NA NA NAHV3 NA NA 40F 3 (174 dy) NA NA NA

P1 IDH mutant anaplastic

oligodendroglioma

Recurrent 52 M 3 (578 dy) 2 Sx, RT/TMZ, TMZ, CCNU Sx, RT 8–16, < 1

P2 IDH mutant GBM Recurrent 30F 9 (512 dy) 3 Sx RT/TMZ, bevacizumab, pembrolizumab, CCNU,

carboplatin

17–124, < 1–6

P3 IDH mutant GBM Recurrent 42 M 3 (301 dy) 2 Sx, RT/TMZ, TMZ,

bevacizumab

Bevacizumab 27–47, 6–12

P4 IDH mutant oligodendroglioma Non-Recurrent 49F 2 (224 dy) Sx None 44–87, 0P5 IDH wildtype GBM Recurrent 55F 4 (225 dy) Sx, RT/TMZ, veliparib/placebo Sx, RT pembrolizumab 26–175, 2–12

A.W. Autry, et al. 1HXUR,PDJH&OLQLFDO 

极T代谢磁共振全球科研集锦

108

accomplished by subtracting the designated 13C voxels of the lesion

(T2L/CEL) from those of the NAWM.

2.6. Post-processing of 13C data

Raw dynamic EPI data were routinely processed by first determining the phase coefficients for removing ghosting artifacts (Wang

et al., 2017). Noise decorrelation via Cholesky decomposition was

employed to improve channel decoupling and the signal-to-noise ratio

(SNR) prior to EPI reconstruction (Pruessmann et al., 2001). Coil

combination was performed using complex weights derived from the

pyruvate data (Zihan et al., 2019). The data were finally phased for

each metabolite to provide zero-mean Gaussian noise for model-based

fitting (Crane et al., 2020). Software used in this study are available

online via the “Hyperpolarized MRI Toolbox” on Github (Crane et al.,

2020).

2.7. Kinetic modeling

Apparent rate constants for pyruvate-to-lactate (kPL) and pyruvateto-bicarbonate (kPB) conversion were quantified using an “inputless”

model (Larson et al., 2018), which approximated first-order kinetics.

While accounting for differences in applied flip angles, this model simultaneously fit phased dynamic data from [1-13C]lactate and [13C]

bicarbonate signals that had been summed over NAWM and T2L/CEL

ROIs. Error in reported kPL and kPB values was estimated from nonlinear

least squares residuals of the associated fitting and thresholded above

25%. Dynamic EPI data were also fit in a voxel-wise fashion to generate

apparent rate constant maps for visualization, when the total SNR

was > 3 for each metabolite. Illustrative kPL and kPB maps from a patient with GBM are shown together with corresponding dynamic traces

and ROIs in Fig. 2.

2.8. Analysis

To evaluate the consistency of kPL-NAWM and kPB-NAWM values across

serial scans and subjects, the coefficients of variation (CVs; SD/mean)

were calculated and expressed as percentages. Rate constant values

were qualitatively compared between NAWM and T2L/CEL.

3. Results

3.1. Healthy volunteers

Fig. 3A presents example dynamic EPI kPL and kPB maps from a

healthy volunteer, which demonstrate the spatial variation of apparent

HP [1-13C]pyruvate metabolism. By comparison to the cortex, cuneus

and deep gray structures, white matter displayed relatively lower apparent conversion rates, as modeled by both kPL and kPB (Fig. 3A).

While coverage of the kPB map was limited by the SNR of [13C]bicarbonate deep within the brain, kPL and kPB values were seen to spatially trend together (Fig. 3A). The 3 healthy volunteers (1 female, 2

male) who ranged 40–59 years-of-age (Table 1) collectively showed

similar kinetic profiles within NAWM over a total of 6 scans: kPLNAWM = 0.018 s−1 ± 5.0% (0.016–0.018 s−1

) and kPBNAWM = 0.0043 s−1 ± 12.6% (0.0035–0.0049 s−1

) [mean ± CV

(range)] (Fig. 3B; Table 2). Despite differences in receiver hardware

over scan intervals ranging from 30 min to 107 days, healthy volunteers

HV2 and HV3 further demonstrated consistency in serial kPL and kPB

values (Fig. 3B; Table 2).

3.2. Serial patients

A total of 21 serial HP-13C imaging exams were performed on the 5

patients (3 female, 2 male) who ranged 30–55 years-of-age. Table 1

presents a summary of diagnosis, clinical features, and treatment

history for each patient. Infiltrating gliomas in this cohort spanned the

range of disease aggressiveness and represented diverse histopathology:

1 IDH-mutant grade II oligodendroglioma; 1 IDH-mutant anaplastic

oligodendroglioma; and 3 grade IV GBM (2 IDH-mutant/1 IDH-wildtype) (Table 1) (Yan et al., 2019). Over the course of imaging, 3 patients

received RT and/or TMZ and 2 patients received the anti-angiogenic

agent bevacizumab as part of their treatment.

Table 2 provides a summary overview of the serial HP-13C kinetic

data for each patient, as quantified within the NAWM, T2L, and CEL,

together with the associated serial CVs. When excluding scans that

overlapped anti-angiogenic therapy, the mean kPL-NAWM and kPB-NAWM

over the remaining 17 of 21 scans were 0.020 s−1 ± 23.8%

(0.015–0.029 s−1

) and 0.0058 s−1 ± 27.7% (0.0037–0.0078 s−1)

[mean ± CV(range)], and intra-patient serial kPL-NAWM/kPB-NAWM demonstrated consistency with CVs ranging 6.8–16.6%/10.6–40.7%

(Table 2). In particular, for patients P1-4, values of kPLNAWM = 0.018 s−1 ± 13.4% (0.015–0.022 s−1

) and kPBNAWM = 0.0058 s−1 ± 24.4% (0.0037–0.0078 s−1

) [n = 13;

mean ± CV(range)] were more similar to those of healthy volunteers,

while patient P5 displayed consistently higher kPLNAWM = 0.028 s−1 ± 7.1% (mean ± CV) over 4 scans spanning

225 days (Table 2). When the acquired spatial resolution was 1.5 cm

isotropic or lower and the flip angle scheme was maintained over serial

imaging, kPL-NAWM and kPB-NAWM CVs were as low as 6.8% and 10.8%,

respectively, for 6 scans (patient P2).

In the two patients with limited to no treatment during imaging (P4

and P1), the mean apparent rate constants in NAWM were comparable

to that of healthy volunteers. Patient P4, who only received surgical

treatment prior to serial HP-13C imaging, showed kPLNAWM = 0.017 s−1 ± 16.6% and kPB-NAWM = 0.0045 s−1 ± 25.1%

(mean ± CV) over 2 scans spanning 224 days. Despite focal RT and

surgery between the second and last timepoints, patient P1 also demonstrated similar values of kPL-NAWM = 0.017 s−1 ± 7.3% and kPBNAWM = 0.0053 s−1 ± 34.0% (mean ± CV), which were consistent

over 3 scans spanning 525 days.

3.3. Effects of radiation and chemotherapy (TMZ)

In the 3 patients who received RT and/or TMZ during serial imaging

as part of treatment, values of kPL-NAWM were comparable across scans.

With adjuvant RT, patients P1 and P5 showed pre/post-radiotherapy

kPL-NAWM = 0.016 ± 0.001/0.016 ± 0.001 s−1 and 0.028 ± 0.001/

0.025 ± 0.002 s−1 ( ± error), with the last scans being 39 and 33 days

after RT, respectively. For two separate RT + TMZ treatments, patient

P2 showed pre/post-chemoradiotherapy kPL-NAWM = 0.021 ± 0.001/

0.020 ± 0.001 s−1 and 0.019 ± 0.001/0.022 ± 0.001 s−1

( ± error), with the last scans being 78 and 15 days after therapy, respectively.

3.4. Effects of anti-angiogenic therapy

Patient P2 presented an interesting case study on the effects of

treatment and the associated evolution of kinetic profiles within presumed tumor regions. In Fig. 4A, the kPL-NAWM, kPL-T2L and kPL-CEL are

shown together as colored traces for each of 9 scans that spanned

512 days, alongside a treatment axis detailing clinical management.

Over the first 6 exams, the kPL-NAWM indicated by the blue trace was

longitudinally consistent within 6.8% (CV), despite treatment with RT/

TMZ. However, kPL-T2L and kPL-CEL, shown as red and orange traces,

respectively, displayed elevations above kPL-NAWM and dynamic changes

during the same treatment interval. With the development of a new

enhancing lesion at the fifth timepoint (TP5) that persisted through the

next scan (TP6), there is a corresponding increase in kPL-T2L (8–11%)

and kPL-CEL (82–92%) above kPL-NAWM. Upon initiation of the anti-angiogenic agent bevacizumab, the enhancing lesion resolved at the seventh timepoint (TP7), while kPL increased globally. The elevation in

A.W. Autry, et al. 1HXUR,PDJH&OLQLFDO



极T代谢磁共振全球科研集锦

109

kPL-NAWM reached a maximum of 0.047 ± 0.001 s−1 ( ± error) at

62 days (TP8) after the first bevacizumab infusion, a 134% increase

over mean kPL-NAWM prior to treatment with bevacizumab. An analogous global increase in kPB-NAWM showed a maximum value of

0.011 ± 0.001 s−1 at the same time.

Fig. 4B shows the corresponding kPL maps overlaid on T1-weighted

images for timepoints 5–8, which demonstrated elevated kPL within the

new enhancing lesion and the drastic global change in kPL following

bevacizumab treatment. Approximately the same maximum value of

kPL-NAWM (0.047 ± 0.003 s−1

) was observed in patient P3 62 days

after bevacizumab treatment, representing a 198–234% elevation

above prior scans. Fig. 4C depicts the dynamic EPI traces for each

metabolite in NAWM from Fig. 4A,B before and after bevacizumab.

While the SNR was overall lower at the post-bevacizumab timepoint,

signal from both [1-13C]lactate and [13C]bicarbonate was proportionally higher relative to [1-13C]pyruvate.

3.5. Profiles of progression

Patients P2, P4, and P5 developed radiological progression over the

course of serial imaging, which was characterized by elevation of kPLT2L, and kPL-CEL in particular, relative to kPL-NAWM. Patient P2 exhibited

a mean ratio of kPL-CEL/kPL-NAWM = 1.77 from 3 to 6 cm3 of multi-focal

gadolinium-enhancing lesions, with individual lesion kPL-CEL ranging

from 0.032 ± 0.008 s−1 to 0.041 ± 0.009 s−1 ( ± error) over 3 scans

(Table 2). In the case of patient P5, the 12 cm3 pre-surgical enhancement showed a ratio of kPL-CEL/kPL-NAWM = 1.42 and kPLCEL = 0.040 ± 0.003 s−1 (Table 2). The mean ratio of kPL-T2L/kPLNAWM was 1.16 and 0.95 for patients P2 and P5, respectively (Table 2).

In contradistinction with other patients who progressed, patient P4

manifested an entirely non-enhancing lesion (87 cm2

) with kPL-T2L/kPLNAWM = 1.29 and kPL-T2L = 0.024 ± 0.001 s−1

. Progression timepoints for these patients are depicted in Fig. 5 with kPL maps overlaid on

T2-weighted FLAIR and post-gadolinium T1-weighted images. For patient P5, the kPL within the lesion was elevated relative to NAWM, and

also displayed spatial heterogeneity: the highest values were in and

Fig. 2. Example HP-13C kinetic maps. Regions of interest from a patient with GBM: NAWM (green) and T2L (red) overlaid on T1-weighted IRSPGR and T2-weighted

FLAIR images, respectively (A). Maps of kPL and kPB based on kinetic modeling of dynamic HP-13C EPI data overlaid on the same T1-weighted images (B, top).

Corresponding dynamic traces of HP [1-13C]pyruvate, [1-13C]lactate, and [13C]bicarbonate signal within NAWM are shown alongside kinetic model fits (B, bottom).

(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

A.W. Autry, et al. 1HXUR,PDJH&OLQLFDO



极T代谢磁共振全球科研集锦

110

around the CEL, while the surrounding T2L kPL was comparatively

lower (Fig. 5A). Patient P2 likewise demonstrated an anatomical lesion

with elevated kPL that, in this example, extended beyond the CEL

margins into the non-enhancing T2L (Fig. 5B). The entirely non-enhancing T2L of patient P4 displayed corpus collosum involvement that

extended to the left frontal cortex and white matter, along with diffusely elevated kPL in the same region (Fig. 5C).

3.6. Quantifying lesion kPB

While kPL-T2L and kPL-CEL were quantifiable, estimates of lesion kPB

demonstrated errors exceeding 25% in all but one scan due to the low

13C bicarbonate signal within the T2L relative to NAWM.

Supplementary Fig. 3 illustrates the characteristically reduced metabolite signal in a radiologically stable T2L compared to the contralateral hemisphere for summed metabolite data with SNR > 3. These

data from patient P2 display less conversion of [1-13C]pyruvate to [13C]

bicarbonate within the T2L, but are below the noise floor and thus

cannot inform on the relative metabolic rate, kPB.

3.7. Evaluating exam quality

Analysis of the experimental parameters for patient P2, who demonstrated low CVs for kPL-NAWM (6.8%) and kPB-NAWM (10.6%) over 6

scans, provided a framework to evaluate exam quality. QC parameters

maintained for these 6 scan injections were: polarization > 33%, pyruvate concentration > 224 mM, and time-to-injection ≤55 s. Within

NAWM, the median total voxel SNR ranged 9.7–24.4 ([1-13C]lactate)

and 5.5–7.9 ([13C]bicarbonate) for 3.38–8 cm3 spatial resolution, and

allowed for rate constant errors of 2–6% (kPL-NAWM) and 7–9% (kPBNAWM). Because [13C]bicarbonate is the resolution-limiting metabolite,

it was determined that achieving a median voxel SNR ≥5.5 for [13C]

bicarbonate enabled ideal quantification error (kPB-NAWM error < 7%)

with the 1.5 cm isotropic resolution (3.38 cm3

).

Fig. 3. Volunteer HP-13C kinetic data. Maps of kPL and kPB from the third scan of healthy volunteer HV3 overlaid on T1-weighted images, which illustrate the spatial

variation of apparent HP [1-13C]pyruvate metabolism: 1, cortex/grey matter; 2, white matter; 3, cuneus; and 4, putamen/deep grey matter (A). Healthy volunteer

values of kPL-NAWM (B) and kPB-NAWM (C) are shown together with nonlinear least squares fitting error for 3 subjects over scan intervals of 30 min (a), 107 days (b) and

67 days (c).

Table 2

HP-13C kinetic data. Rate constants modeled from serial HP-13C data are shown for healthy volunteers (HV) and patients (P) within regions of interest.

SubjectID kPL-NAWM (s−1)

median (range)

kPL-T2L (s−1)

median (range)

kPL-CEL(s−1)

median (range)

kPB-NAWM (s−1)

median (range)

Mean kPL-T2L/kPL-NAWM Mean kPL-CEL/kPL-NAWM kPL-NAWM, kPB-NAWM CV (%)

HV1-3 0.018

(0.016–0.018)

NA NA 0.0043

(0.0035–0.0049)

NA NA 5.0, 12.6

P1 0.016

(0.016–0.018)

0.013

(0.013–0.013)

NA 0.0047

(0.0039–0.0074)

0.76 NA 7.3, 34.0

P2 0.021

(0.019–0.047)

0.025

(0.018–0.036)

0.035

(0.032–0.041)

0.0076

(0.0062–0.011)

1.16 1.77 6.8*, 10.6*

P3 0.016

(0.014–0.047)

0.021

(0.012–0.51)

0.018

(0.012–0.051

0.0064

(0.0042–0.0069)

1.09 1.01 8.0*, 29.4*

P4 0.017

(0.015–0.019)

0.022

(0.020–0.024)

NA 0.0045

(0.0037–0.0053)

1.30 NA 16.6, 25.1

P5 0.029

(0.025–0.029)

0.027

(0.021–0.031)

0.04 0.0059

(0.0033–0.0079)

0.95 1.42 7.1, 40.7

*CVs for 6 (P2) and 2 (P3) scans without bevacizumab treatment.

A.W. Autry, et al. 1HXUR,PDJH&OLQLFDO



极T代谢磁共振全球科研集锦

111

4. Discussion

This study characterized serial dynamic HP-13C imaging of the brain

in healthy volunteers and patients who underwent treatment for infiltrating glioma. Kinetic modeling of apparent [1-13C]pyruvate metabolism within NAWM demonstrated relatively consistent values of rate

constants across subjects over multiple exams and extended intervals.

While patients displayed longitudinal consistency in kPL-NAWM irrespective of various treatments, the initiation of anti-angiogenic therapy

coincided with the global elevation of rate constants. In cases of progressive disease, anatomic lesions showed elevated kPL relative to that

in the NAWM, which may reflect aberrant metabolism.

The consistency of NAWM kinetics in healthy volunteers is

noteworthy, given the importance of establishing an appropriate reference for defining pathological changes in [1-13C]pyruvate metabolism. These findings also agree with results from a prior study utilizing a

model-based spectroscopic imaging approach: the mean kPL-NAWM and

kPB-NAWM in 4 healthy subjects was 0.012 s−1 ± 6% and

0.002 s−1 ± 100% ( ± CV), respectively, compared to

0.018 s−1 ± 5.0% and 0.0043 s−1 ± 12.6% reported here (Grist

et al., 2019). While kinetic parameter values can vary according to the

echo time and signal weighting of the imaging sequence, the relative

ranges of kPL-NAWM in both studies provided evidence of inter-subject

consistency (Chen et al., 2019). Because [13C]bicarbonate detection

relies on the effective SNR that can be achieved within the experimental

framework, a variety of factors may have influenced the variability in

Fig. 4. Effects of bevacizumab. Serial kPL data within NAWM and presumed tumor regions are shown for patient P2 over 9 scans spanning 512 days, along with

clinical treatment information (A). Values of kPL-NAWM (blue) remained consistent until the administration of bevacizumab, whereupon a global increase in kPL

occurred, as seen at timepoint 8 (TP8) (A). Both kPL-T2L (red) and kPL-CEL (orange) are seen to be elevated relative to kPL-NAWM, particularly at the time of progression.

Corresponding kPL maps for timepoints TP5-TP8 overlaid on T1-weighted images illustrate the emergence of a new gadolinium-enhancing lesion with elevated kPL

(red arrows), which disappeared following treatment with bevacizumab, and subsequent global elevation of kPL (B). Kinetic traces from pre- and post-bevacizumab

scans demonstrate lower overall HP signal with bevacizumab, but proportionally greater [1-13C]lactate and [13C]bicarbonate signal relative to that of [1-13C]

pyruvate (C). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

A.W. Autry, et al. 1HXUR,PDJH&OLQLFDO



极T代谢磁共振全球科研集锦

112

quantification of kPB-NAWM from the prior study, including the use of a

single channel volume coil versus a multi-channel phased array.

Based on the kinetic modeling of apparent metabolism over the

course of serial HP-13C imaging, patients also demonstrated consistent

rate constants within NAWM. In the subject (P1) who received chemoradiotherapy prior to and RT during serial imaging, kPL-NAWM varied

only slightly between scans, with the mean value being similar to that

of healthy volunteers. Furthermore, interval changes in patient (P1, P2,

P5) kinetics following limited RT or TMZ were not apparent within a

routine 8 week window for clinical follow-up. These data, taken together with the overall consistency of kPL-NAWM observed in patients,

suggest that NAWM may be a suitable reference region against which to

compare alterations in [1-13C]pyruvate metabolism. This is promising

for potential applications geared toward monitoring response to

therapy, since standard 1

H MRI cannot adequately distinguish

treatment-induced changes from tumor (Winter et al., 2019; Da Cruz

et al., 2011).

Although serial scans generally demonstrated longitudinal consistency, anti-angiogenic therapy appeared to produce global alterations in the rates constants. For two patients, the most pronounced

increase in kPL and kPB followed an approximately 2-month interval

from the initiation of bevacizumab. As a monoclonal antibody designed

to normalize tumor vasculature through anti-VEGF activity, bevacizumab has become a tool for managing refractory brain edema and

salvage therapy (Cuncannon et al., 2019). Because of its effective reduction of blood–brain barrier (BBB) permeability, gadolinium-enhancing lesions, which are the radiological hallmark of GBM, can often

completely resolve, thus challenging clinical interpretations of tumor

progression (Villanueva-Meyer et al., 2017). With regard to HP-13C

imaging, the data suggest that [1-13C]pyruvate extravasation is reduced

Fig. 5. Profiles of progression. Imaging at the time

of radiologically-defined progression shown using kPL

maps overlaid on T2-weighted FLAIR and post-gadolinium T1-weighted images. Patient P5 demonstrated

elevated kPL in the lesion that was spatially heterogeneous, with higher values in and around the CEL

compared to the surrounding T2L (A); whereas patient P2 showed uniformly elevated kPL that extended

distally from the CEL into the T2L, as indicated by the

white arrow (B). Diffusely elevated kPL in patient P4

corresponded with a large non-enhancing T2L centered in the corpus callosum and extending to the left

frontal white matter and cortex (white arrows) (C). In

each case, the lesion kPL highlighted radiological

progression.

A.W. Autry, et al. 1HXUR,PDJH&OLQLFDO



极T代谢磁共振全球科研集锦

113

as a result of vascular changes induced by bevacizumab, which cause

conversion to downstream metabolites to appear more rapid by relative

proportions. This is a plausible mechanism, given that [1-13C]pyruvate

is much smaller than the gadolinium chelates utilized by contrast

agents, thereby allowing greater transport across the BBB under conditions of reduced permeability (Gerstner et al., 2019).

A critical finding in the three patients with progressive disease was

that anatomic lesions manifested elevated kPL compared to the surrounding NAWM. In each instance of gadolinium-enhancement, the

CEL showed a regional kPL maximum over the entire T2L, potentially

indicating aberrant metabolism associated with disease, i.e., the

Warburg effect and up-regulation of LDHA expression (Warburg, 1956;

Valvona et al., 2016). Although there is evidence of gadolinium deposition after contrast-enhanced MRI (McDonald et al., 2015), signal

loss due to potential deposition over serial exams is not a concern for

HP imaging because the relaxivity of commercial gadolinium-based

contrast agents is substantially lower on 13C than water (Gabellieri and

Leach, 2009; Smith et al., 2012). The heterogeneity of kPL observed

across the anatomic lesion in patient P5 may be reflective of the underlying features of GBM metabolism and the spatial extent of disease

within the T2L, which cannot be distinguished from vasogenic edema

on routine imaging. Such edema reduces normal perfusion (Bastin

et al., 2006) in a manner that appeared to lower [1-13C]pyruvate signal

and apparent conversion in this study, and thus requires careful evaluation. Patient P2 additionally demonstrated a non-enhancing T2-hyperintense lesion with elevated kPL-T2L during the initial progression,

which may have indicated infiltrative disease. Even the entirely nonenhancing lesion of patient P4 displayed diffuse elevation of kPL-T2L that

provided evidence of metabolic abnormality in low-grade disease.

These results were obtained in the challenging post-treatment context,

where lesions typically present with less volume and gadolinium-enhancement than newly diagnosed disease. Nonetheless, the findings

support the potential utility of HP-13C imaging in differentiating

treatment-induced changes from progressive enhancing or non-enhancing tumor.

While kPB was readily quantified in NAWM, estimates of lesion values were challenging owing to the lower [13C]bicarbonate signal. This

comparatively lower signal in the T2L at a voxel-wise level provided

evidence of less conversion from [1-13C]pyruvate and reduced TCA

cycle metabolism (Martinez-Reyes and Chandel, 2020), which is itself

an important observation concerning the influence of edema and tissue

environment. Whether certain gliomas display measurably reduced kPB

as part of a malignant metabolic phenotype remains to be determined.

Using the data from patient P2,who demonstrated low CVs over

serial scans, it was possible to provide experimental guidelines for

achieving adequate SNR. From a QC standpoint, maintaining a time-toinjection ≤55 s preserved enough hyperpolarization to obtain reliable

data at higher resolutions. Furthermore, the isotropic spatial resolution

of 1.5 cm (3.38 cm3

) demonstrated sufficient SNR (median voxel SNR

≥5.5 in NAWM) for quantification, based on the model fitting error for

[

13C]bicarbonate, which limits resolution. This resolution also represented a practical tradeoff with model error to maintain repeatability, and not necessarily an absolute limit.

In this study, a first-order, single compartment model was able to

consistently quantify kPL and kPB in high-quality datasets, however it is

worth noting the potential influence of certain biological factors. The

measured signals are a combination of vascular and intracellular/extracellular metabolite pools and depend on compartmentalized 13Clabel exchange (Bankson et al., 2015). Thus, highly perfused tumor can

appear to have lower rate constants by comparison to regions that

display normal perfusion when vascular [1-13C]pyruvate is not excluded. Additionally, there are a number of the factors influencing 13Clabel exchange via enzymatic conversion: while kPL has been shown to

correlate with LDH, pre-clinical studies have shown that values of kPL

can also be influenced by the pyruvate delivery rate, vascular/cellular

compartment permeability, monocarboxylate transporter (MCT)

expression/activity, metabolite pool sizes, LDH expression/activity,

associated cofactor nicotinomide adenine dinucleotide (NADH) levels,

and cellularity (Bankson et al., 2015; Hurd et al., 2010). Although

overexpression of MCT4 in GBM (Miranda-Goncalves et al., 2013) may

facilitate lactate efflux, the “pool size effect” of excess endogenous

lactate from LDH overexpression/hyperactivity likely promotes elevated kPL relative to healthy tissue (Day et al., 2007). Since this study

did not account for nonlinear enzyme reaction velocities described by

Michaelis-Menten kinetics under conditions of sufficiently high substrate concentration, deviations related to pyruvate saturation effects

were possible (Xu et al., 2011).

Further interrogating [1-13C]pyruvate metabolism in patients with

glioma will entail a variety of tactics that are aimed at improving both

analysis and methodology. From an analysis standpoint, the application

of the inputless kinetic model provided regionally consistent measures

of apparent rate constants. While proving largely robust to variations in

receiver hardware, it may have been affected by changes in the acquired spatial resolution and flip angle scheme over the developmental

phase of the study, owing to partial volume effects in the first case and

model limitations in the second. Most of the intra-subject variability

likely resulted from lower SNR in the data with higher than 1.5 cm

isotropic resolution or changing flip angle schemes between serial

scans. With a variable resolution (Gordon et al., 2018) acquisition

scheme, resolution could be defined independently for each metabolite

on the basis of SNR, thereby improving [13C]bicarbonate measurement

and kPB quantification. Being able to extend quantification with an

accounting of the vascular contributions of [1-13C]pyruvate, and to a

lesser extent [1-13C]lactate (Chaichana et al., 2010), will enhance the

ability to detect aberrant metabolism and potentially monitor response

to treatment. Because of the strong dependence on SNR for quantifying

HP data and resolving finer regions of metabolism, additional hardware

and reconstruction improvements will be critical to clinically translating this technology (Crane et al., 2020). Implementing atlas-based

prescription routines for HP acquisitions would also augment the ability

to compare data longitudinally on a voxel-wise basis (Bian et al., 2018).

Furthermore, by enrolling larger cohorts of patients with newly diagnosed disease and age-matched controls the range of population differences can be characterized along with metabolism associated with

disease.

5. Conclusion

Kinetic modeling of serial HP-13C data from patients with glioma

demonstrated consistent values of rate constants kPL-NAWM and kPBNAWM longitudinally, which were mostly similar to those of healthy

volunteers. The anti-angiogenic agent bevacizumab appeared to be

associated with a global elevation of apparent rate constants that may

have resulted from reduced extravasation of [1-13C]pyruvate through

the BBB. In patients with progressive disease, the kPL was also elevated

in both gadolinium-enhancing and non-enhancing lesions, potentially

highlighting aberrant metabolism across a range of glioma subtypes and

supporting the utility of HP-13C imaging.

CRediT authorship contribution statement

Adam W. Autry: Conceptualization, Methodology, Formal analysis,

Writing - original draft. Jeremy W. Gordon: Conceptualization,

Methodology, Writing - review & editing. Hsin-Yu Chen: Methodology,

Writing - review & editing. Marisa LaFontaine: Writing - review &

editing. Robert Bok: Writing - review & editing. Mark Van Criekinge:

Resources, Writing - review & editing. James B. Slater: Resources,

Writing - review & editing. Lucas Carvajal: Resources, Writing - review

& editing. Javier E. Villanueva-Meyer: Conceptualization, Writing -

review & editing. Susan M. Chang: Conceptualization, Funding acquisition, Writing - review & editing. Jennifer L. Clarke:

Conceptualization, Funding acquisition, Writing - review & editing.

A.W. Autry, et al. 1HXUR,PDJH&OLQLFDO

极T代谢磁共振全球科研集锦

114

Janine M. Lupo: Conceptualization, Funding acquisition, Writing -

review & editing. Duan Xu: Conceptualization, Writing - review &

editing. Peder E.Z. Larson: Conceptualization, Methodology, Writing -

review & editing. Daniel B. Vigneron: Conceptualization,

Methodology, Funding acquisition, Writing - review & editing. Yan Li:

Conceptualization, Supervision, Funding acquisition, Writing - review &

editing.

Acknowledgements

This work is dedicated to the late professor Sarah J Nelson, and was

supported by NIH Grants R01 CA127612, P01 CA118816, P41

EB0341598, P50 CA097257, and T32 CA151022, together with the

Glioblastoma Precision Medicine Program.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://

doi.org/10.1016/j.nicl.2020.102323.

References

Ardenkjaer-Larsen, J.H., Fridlund, B., Gram, A., et al., 2003. Increase in signal-to-noise

ratio of > 10,000 times in liquid-state NMR. PNAS 100, 10158–10163.

Autry, A.W., Gordon, J.W., Carvajal, L., Mareyam, H., Chen, H.Y., Park, I., Mammoli, D.,

Vareth, M., Chang, S.M., Wald, L.L., Xu, D., Vigneron, D.B., Nelson, S.J., Li, Y., 2019.

Comparison between 8- and 32-channel phased-array receive coils for in vivo hyperpolarized 13 C imaging of the human brain. Magn. Reson. Med. 82, 833–841.

Bankson, J.A., Walker, C.M., Ramirez, M.S., et al., 2015. Kinetic modeling and constrained reconstruction of hyperpolarized [1-13C]-pyruvate offers improved metabolic imaging of tumors. Cancer Res. 75 (22), 4708–4717.

Bastin, M.E., Carpenter, T.K., Armitage, P.A., Sinha, S., Wardlaw, J.M., Whittle, I.R.,

2006. Effects of dexamethasone on cerebral perfusion and water diffusion in patients

with high-grade glioma. AJNR 27 (2), 402–408.

Bian, W., Li, Y., Crane, J.C., Nelson, S.J.N., 2018. A fully automated atlas based method

for prescribing 3D PRESS MR spectroscopic imaging: towards robust and reproducible metabolite measurements in human brain. Magn. Reson. Med. 79 (2),

636–642.

Chaichana, K.L., McGirt, M.J., Laterra, J., Olivi, A., Quinnones-Hinojosa, A., 2010.

Recurrence and malignant degeneration after resection of adult hemispheric lowgrade gliomas. J. Neurosurg. 112 (1), 10–17.

Chen, H.Y., Gordon, J.W., Bok, R., Cao, P., et al., 2019. Pulse sequence considerations for

quantification of pyruvate-to-lactate conversion kPL in hyperpolarized 13C imaging.

NMR Biomed. 32 (2), e4052.

Crane, J.C., Gordon, J.W., Chen, H,Y., Autry, A.W., et al., 2020. Hyperpolarized 13C MRI

data acquisition and analysis in prostate and brain at University of California, San

Francisco. NMR Biomed. 2020; e4280.

Cuncannon, M., Wong, M., Jayamanne, D., Guo, L., Cove, N., Wheeler, H., Back, M., 2019.

Role of delayed salvage bevacizumab at symptomatic progression of chemorefractory

glioblastoma. BMC Cancer 445.

Da Cruz, L.C.H., Rodriguez, I., Domingues, R.C., Gasparetto, E.L., Sorensen, A.G., 2011.

Pseudoprogression and pseudoresponse: imaging challenges in the assessment of

posttreatment glioma. AJNR 32 (11), 1978–1985.

Day, S.E., Kettunen, M.I., Gallagher, F.A., et al., 2007. Detecting tumor response to

treatment using hyperpolarized 13C magnetic resonance imaging and spectroscopy.

Nat. Med. 13, 1382–1387.

Gabellieri, C., Leach, M.O., Eykyn, 2009. Modulating the relaxivity of hyperpolarized

substrates with gadolinium contrast agents. Contrast Media Mol. Imaging 4, 143–147.

Gerstner, E., Emblem, K.E., Chang, K., et al., 2019. Bevacizumab reduces permeability

and concurrent temozolomide delivery in a subset of patients with recurrent glioblastoma. Clin. Cancer Res. 26, 206–212.

Golman, K., Zandt, R.I., Lerche, M., Pehrson, R., Ardenkjaer-Larsen, J.H., 2006. Metabolic

imaging by hyperpolarized 13C magnetic resonance imaging for in vivo tumor diagnosis. Cancer Res. 66, 10855–10860.

Gordon, J.W., Chen, H.Y., Autry, A., Park, I., Van Criekinge, M., Mammoli, D., et al.,

2019. Translation of carbon-13 EPI for hyperpolarized MR molecular imaging of

prostate and brain cancer patients. Magn. Reson. Med. 81, 2702–2709.

Gordon, J.W, Milshteyn, E., Vigneron Daniel, B., Larson Peder, E.Z., 2018. Variable

Resolution Echo-Planar Imaging for Improved Quantification of Hyperpolarized 13C

Metabolism. In Proceedings of the 26th Annual Meeting of ISMRM, Paris, France,

2018 (Abstract#3053).

Gordon, J.W., Vigneron, D.B., Larson, P., 2017. Development of a symmetric echo planar

imaging framework for clinical translation of rapid dynamic hyperpolarized 13C

imaging. Magn. Reson. Med. 77, 826–832.

Grist, J.T., McLean, M.A., Fiemer, F., Schulte, R.F., Deen, S.S., Zaccagna, F., et al., 2019.

Quantifying normal human brain metabolism using hyperpolarized [1-13C]pyruvate

and magnetic resonance imaging. NeuroImage 189, 171–179.

Hurd, R.E., Yen, Y.F., Tropp, J., et al., 2010. Cerebral dynamics and metabolism of hyperpolarized [1-(13)C]pyruvate using time-resolved MR spectroscopic imaging. J.

Cereb. Blood Flow Metab. 30 (10), 1734–1741.

Jenkinson, M., Smith, S.M., 2001. A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5, 143–156.

Larson, P.E.Z., Chen, H.Y., Gordon, J.W., Korn, N., Maidens, J., Arcak, M., Tang, S.,

Criekinge, M., Carvajal, L., Mammoli, D., Bok, R., Aggarwal, R., Ferrone, M., Slater,

J.B., Nelson, S.J., Kurhanewicz, J., Vigneron, D.B., 2018. Investigation of analysis

methods for hyperpolarized 13C-pyruvate metabolic MRI in prostate cancer patients.

NMR Biomed. 31 (11), e3997.

Lee, C.Y., Soliman, H., Geraghty, B.J., Chen, A.P., Connelly, K.A., Endre, R., Perks, W.J.,

Heyn, C., Black, S.E., Cunningham, C.H., 2020. Lactate topography of the human

brain using hyperpolarized 13C-MRI. NeuroImage 204, 116202.

Louis, D.N., Perry, A., Reifenberger, G., von Deimling, A., Figarella-Branger, D., Cavenee,

W.K., Ohgaki, H., Wiestler, O.D., Kleihues, P., Ellison, D.W., 2016. The 2016 World

Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 31 (6), 803–820. https://doi.org/10.1007/s00401-016-

1545-1.

Lunt, Y.L., Vander Heiden, M.G., 2011. Aerobic glycolysis: meeting the metabolic requirements of cell proliferation. Annu. Rev. Cell Dev. Biol. 27, 441–464.

Martinez-Reyes, I., Chandel, N.S., 2020. Mitochondrial TCA cycle metabolites control

physiology and disease. Nat. Commun. 11 (102).

McDonald, R.J., McDonald, J.S., Kallmess, D.F., et al., 2015. Intracranial gadolinium

deposition after contrast-enhanced MR imaging. Radiology 275 (3), 772–782.

Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., et al., 2015.

The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans.

Med. Imaging 34 (10), 1993–2024.

Miloushev, V.Z., Granlund, K.L., Boltyanskiy, R., et al., 2018. Metabolic imaging of the

human brain with hyperpolarized 13C pyruvate demonstrates 13C lactate production

in brain tumor patients. Cancer Res. 78, 3755–3760.

Miranda-Goncalves, V., Honavar, M., Pinheiro, C., et al., 2013. Monocarboxylate transporters (MCTs) in gliomas: expression and exploitation as therapeutic targets. Neuro

Oncol. 15 (2), 172–188.

Park, I., Larson, P.E.Z., Tropp, L.T., et al., 2014. Dynamic hyperpolarized carbon-13 MR

metabolic imaging of nonhuman primate brain. Magn. Reson. Med. 71 (1), 19–25.

Park, I., Larson, P.E.Z., Gordon, J.W., et al., 2018. Development of methods and feasibility

of using hyperpolarized carbon-13 imaging data for evaluating brain metabolism in

patient studies. Magn. Reson. Med. 80, 864–873.

Pruessmann, K.P., Wieger, M., Boernert, P., Boesiger, P., 2001. Advances in sensitivity

encoding with arbitrary k-space trajectories. Magn. Reson. Med. 46, 638–651.

Saraste, M., 1999. Oxidative phosphorylation at the fin de siècle. Science 283 (5407),

1488–1493.

Smith, M.R., Peterson, E.T., Gordon, J.W., et al., 2012. In vivo imaging and spectroscopy

of dynamic metabolism using simultaneous 13C and 1

H MRI. IEEE Trans. Biomed.

Eng. 59 (1), 45–49.

Stupp, R., Mason, W.P., van den Bent, M.J., Weller, M., Fisher, B., Taphoorn, M.J., et al.,

2005. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma.

N. Engl. J. Med. 352 (10), 987–996.

Valvona, C.J., Fillmore, H.L., Nunn, P.B., Pilkington, G.J., 2016. The regulation and

function of lactate dehydrogenase A: therapeutic potential in brain tumor. Brain

Pathol. 26 (1), 3–17.

Villanueva-Meyer, J.E., Mabray, M.C., Cha, S., et al., 2017. Current clinical brain tumor

imaging. Neurosurgery 81 (3), 397–415.

Wang, J., Wright, A.J., Hesketh, R.L., Hu, D., Brindle, K.M., 2017. A referenceless Nyquist

ghost correction workflow for echo planar imaging of hyperpolarized [1-13C]pyruvate and [1-13C]lactate. NMR Biomed. 31 (2), e3866.

Warburg, O., 1956. On respiratory impairment in cancer cells. Science 124 (3215),

269–270.

Winter, S.F., Loebel, F., Loeffler, J., Batchelor, T.T., Martinez-Lage, M., Vajkoczy, P.,

Dietrich, J., 2019. Treatment-induced brain tissue necrosis: a clinical challenge in

neuro-oncology. Neuro Oncol. noz048.

Xu, T., Mayer, D., Gu, M., et al., 2011. Quantification of in vivo metabolite kinetics of

hyperpolarized pyruvate in rat kidneys using dynamic 13C MRSI. NMR Biomed. 24

(8), 997–1005.

Yan, H., Parsons, D.W., Jin, G., McLendon, R., Rasheed, B.A., Yuan, W., Kos, I., BatinicHaberle, I., Jones, S., Riggins, G.J., Friedman, H., Friedman, A., Reardon, D.,

Herndon, J., Kinzler, K.W., Velculescu, V.E., Vogelstein, B., Bigner, D.D., 2019. IDH1

and IDH2 mutations in gliomas. N. Engl. J. Med. 360, 765–773.

Zhang, Y., Brady, M., Smith, S., 2001. Segmentation of brain MR images through a hidden

random field model and the expectation maximization algorithm. IEEE Trans. Med.

Imaging 20, 45–57.

Zihan, Z., Zhu, X., Ohliger, M.A., Tang, S., Cao, P., Carvajal, L., et al., 2019. Coil combination methods for multi-channel hyperpolarized 13C imaging data from human

studies. J. Magn. Reson. 301, 73–79.

A.W. Autry, et al. 1HXUR,PDJH&OLQLFDO



极T代谢磁共振全球科研集锦

115

Hyperpolarized 13C-pyruvate MRI detects

real-time metabolic flux in prostate cancer

metastases to bone and liver:

a clinical feasibility study

研究背景

研究对象

研究过程

ג极ࣅ) HP) 13C- եཛྷ໗ MRI ๟ᅃዖ࿘ۨཞ࿋໎ݴጱׯၟఇ๕Lj੗༵ࠃ้ํೠࠚටૌമଚ၇ҵዐཚࡗ༛ট঴཰০ڦ代

谢໏୲ăങৃྺኹኄዖׯၟఇ๕ᅙںࠀׯᆩᇀമଚ၇ҵሞਆևदթዐă޿๬ۅଣض研৯ֱۙକ HP 13C- եཛྷ໗ MR 代

谢ׯၟሞമଚ၇ҵገᅎ࣒ኁDŽሞࠡỞࢅాሤഄ໱ഗڦ࠳ገᅎDžዐڦ੗ႜႠׯࢅၟႠă

6 ఁ࣒ᆶገᅎႠඁ๞ڸੇႠമଚ၇ҵ࣒ڦኁ

ገᅎඁ๞ڸੇႠമଚ၇ҵDŽmCRPCDž๟ፌዂంڦदթႚ๕Ljெࡔ౎௅ᆶ31000ටຶᇀ޿दթă

ג极ࣅ 13C MRI (HP 13C MRI) ๟ᅃዖ࿘ۨཞ࿋໎Lj༑֪եཛྷ໗ڟළ໗ݴڦጱׯၟݛ݆ăᆯ LDH ாٝฉۙ঻ڦڞ代谢ए

ᇀ Warburg ၳᆌܸሞҵኢዐڦऄႠă

13C-եཛྷ໗MRIᅙࠀׯᆌᆩᇀਆևമଚ၇ҵăኄዖׯၟݛ๕ᆶᆩᇀॠ֪ࣅܔბघ໎ڦ代谢ݒᆌዎଐᇱ݀Ⴀമଚ၇ҵLj

၎Բᇑدཥܠڦ֖ຕ MRI ၎ԲLj੗ᅜሞ߸ራڦ้क़বۅ৊ႜዎଐă

ሞړമڦ๬ׯۅၟ研৯ዐLj࿢்ኼሞકۇٴHP 13C-եཛྷ໗ MRI ܔገᅎႠCRPCݔڦྷยዃLjࠡỞࢅాሤڦ኱থ੗๫ࣅገ

ᅎLjᅜ༵ࠃ้ํೠࠚ዗ୀ代谢ܔࢅዎଐڦ代谢ݒᆌă

极T代谢磁共振全球科研集锦

116

研究结果

结 论

应用方向

࣒ኁ 2DŽ57 ໠Džԥኑ܏ྺ CRPCLjᅙገᅎ዁ߊሤă࣒޿ኁኮ

മথ๴ࡗܷሗ୛Ӄࢅᅃዖଣض৊ቛڦ研৯ᄱ࿿DŽBET ᅞ዆

वLjI ೺Džዎଐăਸ਼մܠࢅဇ໱ෘࣅڦଐሞए၍ HP 13C ෢௮

ࢫሀ 1 ߲ሆਸ๔Ljժሞዎଐਸ๔ࢫ 2 ߲ሆ৊ႜໜݡăa) ሞࣅ

ଐ 2 ߲ሆ࠵ࢫִڟեཛྷ໗ڟළ໗ገࣅ ୲kPL ٗ 0.026 ইڟگ

0.015 (s-1)ă ጀᅪጀพࢫ 50-60 ௱եཛྷ໗ࢅළ໗ᆶ໯ሺेă

ኄ࢔ీ੗ዷᄲ๟ᆯᇀઠጲڦڢ׌ეࠋ࠶၅ăb) ਸ๔ዎଐࢫ 2

߲ሆڦໜݡ݀၄Ljߵ ਍RECIST ՔጚLjթሲۇٴၭ३ၭDŽ19.3-

11.8 ࡹ௝Dž՗௽ዎଐݒᆌăc) ُྔLjეൣ PSA ٗ 38-13.4 ng/

ml ူইᄺ՗௽ዎଐݒᆌă

޿๬ۅ研৯ೠࠚକ޿๬ۅ研৯ೠࠚକܔገᅎႠ࣒ኁ৊ႜ HP13C MRI 研৯മଚ၇ҵڟࠡỞࢅాሤገᅎҾ全Ⴀࢅ੗ႜႠLj

ኄ代՗മଚ၇ҵڦፌံ৊ࢅፌዂంڦႚ๕ă ݛ݆ԥॠֱࢅॺ૬ᅏഗยዃLj዆ᄱ዆ሰĂ཮ၟ֑集ࢅۨଉݴဆăҾ全Ⴀ

ڟڥኤ௽ժ࠵ִڟեཛྷ໗ڟළ໗ገࣅ kPL ൔလႠࠡߊࢅገᅎă क़߰݀၄ᅃఁথ๴૴ࢇᆩᄱ࣒ڦኁ kPL ইࣅگଐLjӀቷ

ࡀ׉ଣضิׯࢅࣅ࿿ิၟՔኾ࿿ă ኄၵ݀၄ԍኤକ HP 13C- եཛྷ໗ MRI ڦ৊ᅃօ݀ቛڦҾ全ႠࢅᆶၳႠLj੗ᅜሞ߸

ڦۇٴമቒႠఴႠፇዐڦገᅎႠമଚ၇ҵዐ৊ႜศ෇研৯ă

ገᅎႠඁ๞ڸੇႠമଚ၇ҵ

极T代谢磁共振全球科研集锦

117

Prostate Cancer and Prostatic Diseases

https://doi.org/10.1038/s41391-019-0180-z

ARTICLE

Clinical Research

Hyperpolarized 13C-pyruvate MRI detects real-time metabolic flux

in prostate cancer metastases to bone and liver: a clinical

feasibility study

Hsin-Yu Chen 1 ●Rahul Aggarwal2 ●Robert A. Bok1 ●Michael A. Ohliger1 ●Zi Zhu1 ●Philip Lee1 ●

Jeremy W. Gordon1 ●Mark van Criekinge1 ●Lucas Carvajal1 ●James B. Slater1 ●Peder E. Z. Larson 1 ●

Eric J. Small2 ●John Kurhanewicz1 ●Daniel B. Vigneron1

Received: 21 August 2019 / Revised: 10 October 2019 / Accepted: 18 October 2019

This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2019.

Abstract

Background Hyperpolarized (HP) 13C-pyruvate MRI is a stable-isotope molecular imaging modality that provides real-time

assessment of the rate of metabolism through glycolytic pathways in human prostate cancer. Heretofore this imaging

modality has been successfully utilized in prostate cancer only in localized disease. This pilot clinical study investigated the

feasibility and imaging performance of HP 13C-pyruvate MR metabolic imaging in prostate cancer patients with metastases

to the bone and/or viscera.

Methods Six patients who had metastatic castration-resistant prostate cancer were recruited. Carbon-13 MR examination

were conducted on a clinical 3T MRI following injection of 250 mM hyperpolarized 13C-pyruvate, where pyruvate-to-lactate

conversion rate (kPL) was calculated. Paired metastatic tumor biopsy was performed with histopathological and RNA-seq

analyses.

Results We observed a high rate of glycolytic metabolism in prostate cancer metastases, with a mean kPL value of 0.020 ±

0.006 (s−1) and 0.026 ± 0.000 (s−1) in bone (N = 4) and liver (N = 2) metastases, respectively. Overall, high kPL showed

concordance with biopsy-confirmed high-grade prostate cancer including neuroendocrine differentiation in one case. Interval

decrease of kPL from 0.026 at baseline to 0.015 (s−1

) was observed in a liver metastasis 2 months after the initiation of taxane

plus platinum chemotherapy. RNA-seq found higher levels of the lactate dehydrogenase isoform A (Ldha,15.7 ± 0.7)

expression relative to the dominant isoform of pyruvate dehydrogenase (Pdha1, 12.8 ± 0.9).

Conclusions HP 13C-pyruvate MRI can detect real-time glycolytic metabolism within prostate cancer metastases, and can

measure changes in quantitative kPL values following treatment response at early time points. This first feasibility study

supports future clinical studies of HP 13C-pyruvate MRI in the setting of advanced prostate cancer.

Introduction

Metastatic castration-resistant prostate cancer (mCRPC) is

the most lethal form of the disease, accounting for 31,000

deaths/year in the United States [1]. More than 90% of

patients with mCRPC develop osseous metastases and

nearly half have bone as the only site of the disease [2, 3].

Visceral metastases occur in 10–15% of mCRPC patients

and are associated with high disease burden and poor

prognosis [4, 5]. Despite the emergence of multiple therapies that have been shown to prolong overall survival,

including androgen pathway inhibitors, immunotherapy,

radiopharmaceuticals, and chemotherapeutics, there is an

unmet need for novel therapies to further improve treatment

outcomes [3, 6, 7].

* Daniel B. Vigneron

dan.vigneron@ucsf.edu

1 Department of Radiology and Biomedical Imaging, University of

California, San Francisco, CA, USA

2 Department of Medicine, University of California, San Francisco,

CA, USA

Supplementary information The online version of this article (https://

doi.org/10.1038/s41391-019-0180-z) contains supplementary material,

which is available to authorized users.

1234567890();,:1234567890();,:

极T代谢磁共振全球科研集锦

118

A limitation to the development of novel systemic therapies in mCRPC, especially with bone predominance without

measurable disease by conventional imaging criteria, is the

lack of validated imaging biomarkers to provide real-time

response monitoring. Automated bone indices of radionuclide bone scans have not been sufficiently prospectively

validated, and provide minimal information with respect to

direct tumor metabolic activity. Also, changes in bone scintigraphy with response to therapy can be slow to occur and

are complicated by flare phenomena and differences in

uptake between sclerotic versus lytic lesions [8]. Newer PET

analogs, including agents targeting prostate-specific membrane antigen, have shown promise as a diagnostic tool, but

have limited and conflicting data to support their use to

monitor therapeutic response and resistance [9, 10].

Hyperpolarized 13C MRI (HP 13C MRI) is a stable-isotope

molecular imaging approach that probes pyruvate-to-lactate

metabolism mediated by the upregulation of LDH enzymatic

activity in cancer due to the Warburg effect [11–14] (Fig. 1).

High glycolytic activity and rapid pyruvate-to-lactate conversion are signatures of aggressive cancer [13, 15, 16].

There is also a broad consensus that the pharmacologic

action of chemotherapy is tightly coupled with metabolic

pathways, and responses to chemotherapy might be reflected

as modulations of cancer metabolism [17–19]. Heretofore HP 13C-pyruvate MRI has been successfully utilized in prostate

cancer only in localized disease. This imaging modality has

been used to detect metabolic responses to chemohormonal

therapy in primary prostate cancer [20], at earlier time points

than conventional multiparametric MRI.

In the current pilot imaging study, we aimed to broaden

the scope of HP 13C-pyruvate MRI to the metastatic CRPC

setting, with direct visualization of skeletal and visceral

metastases, in order to provide real-time assessment of

tumor metabolism and metabolic response to therapy.

Methods

Patient selection

Key eligibility criteria included histologic evidence of

prostate cancer, progressive mCRPC by PCWG2 criteria

[7], ECOG performance status of 0 or 1, and adequate end

organ function. All patients underwent restaging CT and

bone scans prior to enrollment, and had at least one identified lesion amenable to HP 13C MRI. Patient recruitment

and HP 13C-pyruvate studies were conducted in compliance

with an IRB-approved protocol (NCT02911467), and all

patients provided written informed consent.

HP 13C patient MRI studies

GMP [1−13C]pyruvic acid (Sigma-Aldrich Isotec, Miamisburg OH) was prepared and loaded in pharmacy kits in

accordance with the IRB- and FDA IND-approved stableisotope manufacturing process. The pyruvic acid was

polarized in a 5T SPINLab (GE Healthcare, Chicago IL)

clinical trial polarizer for 2.5–3 h. Dissolutions yielded

237 ± 10 mM sterile pyruvate with 37.1 ± 3.2% polarization,

0.6 ± 0.4 μM residual radical and 31.0 ± 0.6 °C temperature,

7.5 ± 0.3 pH, 63 ± 4 s dissolution-to-injection time. A

pharmacist oversaw the automatic quality control and

integrity of the sterilization filter, and released the dose

for injection once sterility and safety criteria were met

[21, 22].

All studies were conducted on a clinical 3T MRI

(MR750, GE Healthcare) equipped with multinuclear

spectroscopy capabilities. A custom surface coil with figureeight configuration was applied for both 13C transmit and

receive. A 16-channel abdominal array (GE Healthcare) was

used for proton imaging.

Follow-up 13C-pyruvate MRI was optional after the

initiation of systemic therapy for the treatment of mCRPC.

Data acquisition and analysis

The HP-13C acquisition was conducted using a 2D dynamic

MR spectroscopic imaging pulse sequence with a sliceselective spectral-spatial excitation, followed by phaseencode and echo-planar spectroscopic imaging readout [11].

Pulse sequence parameters were as follows: 130 ms/3.5 ms

TR/TE, 2–3 cm slice thickness, 1.2–1.5 cm in plane spatial

and 3 s temporal resolutions, 60 s acquisition window, 545

Hz bandwidth, constant flip angle through time with pyruvate 10°, and lactate 20°. Scan started 5 s following the

end of the injection. Patients were asked to hold their breath

as long as possible, after which they were instructed to

breathe gently and resume breath holding as tolerated.

Conventional proton T1-weighted spoiled gradient-echo

(TR/TE = 4.3 ms/1.9 ms) images were acquired for anatomic reference. Dynamic HP 13C MRI datasets were processed by applying even–odd lobe phasing, B0-shift

correction, tensor-low-rank signal enhancement [23], spectral baseline correction [24], followed by a phase-sensitive

peak quantification. The pyruvate-to-lactate conversion rate,

Fig. 1 An illustration of LDH-mediated aerobic glycolysis and relevant metabolic pathways

H.-Y. Chen et al.

极T代谢磁共振全球科研集锦

119

kPL, was evaluated using an inputless single-compartment

two-site exchange model [25], and the value reported was

the maximum over ROI of the lesion identified on proton

MRI. Total carbon signal-to-noise ratio (SNR) was reported

as summed SNR of 13C-labeled tracers averaged over time.

The image processing tools are located under—SIVIC

Image Processing/Display: https://sourceforge.net/projects/

sivic, Hyperpolarized MRI Toolbox: https://github.com/La

rsonLab/hyperpolarized-mri-toolbox.

Metastatic tumor biopsy acquisition and analysis

CT-guided metastatic tumor biopsies following HP MRI

acquisition were obtained in five out of the six patients

enrolled in the study (Table 1). Tumor biopsies were

obtained for both fresh frozen processing and formalin fixed

paraffin embedded (FFPE) processing. FFPE tissues were

used for histologic diagnosis, while frozen tissue underwent

Laser Capture Microdissection for RNA-seq profiling as

previously described [6]. Expression levels reported as log

(1 + (TPM × 106

)). Processed RNA-seq data are located in

the Supplementary Materials.

Results

Patient characteristics

Six patients were enrolled in this pilot feasibility study. The

baseline characteristics of the patients are shown in Table 1.

All patients had progressive mCRPC at study entry. Five of

the patients underwent CT-guided metastatic tumor biopsy

of the target lesions following completion of baseline 13Cpyruvate MRI. No adverse events were reported throughout

this study.

HP 13C-pyruvate MRI detects high kPL in bone and

liver metastases

The rate of conversion of pyruvate to lactate (kPL) from

target lesions in each patient is listed in Table 2. There was

high kPL in both bone and liver metastases, with mean kPL of

(0.020 ± 0.006 s−1) and (0.026 ± 0.000 s−1), respectively

[26]. Regions of high kPL were consistent with CT and MRI

radiographic findings of metastatic disease presence, as

shown in the representative kPL image overlays for the target

lesions (Fig. 2a, Supplementary Figs. 1–4).

The liver mass of patient six showed considerable

intratumoral heterogeneity (Supplementary Fig. 4b). Maximum kPL = 0.025 (s−1

) was found in viable tumor, whereas

kPL = 0.004 (s−1

) was observed in a necrotic-appearing

region identified both on CT and the delayed phase of

contrast T1-weighted images.

Table 1 A summary of clinically relevant information from each patientPatient Agea Target metastatic site Gleason scoreb Serum PSA (ng/ml)a Metastatic tumor biopsy pathology Most recent prior systemic therapy

1 75 Left iliac wing 4

+ 5 171.7 Adenocarcinoma

+ Small cell neuroendocrine carcinoma (SCNC) Enzalutamide

+ Investigational agent

2 57 Liver 4 + 4 Baseline: 38 Follow-up: 13.4 High-grade adenocarcinoma Investigational agent3 83 Rib 4 + 4 89.6 No biopsy performed Enzalutamide4 72 Right posterior ilium 4

+ 5 89.2 High-grade adenocarcinoma Docetaxel

+ Ribociclib

5 70 Left posterior ilium 4

+ 5 1482 High-grade adenocarcinoma Docetaxel

6 82 Liver 4 + 3 1439 High-grade adenocarcinoma Docetaxel

+ Carboplatin

aAt study entrybAt initial diagnosis

Hyperpolarized 13C-pyruvate MRI detects real-time metabolic flux in prostate. . .

极T代谢磁共振全球科研集锦

120

Table 2 also summarizes the total carbon SNR for each

study. The total carbon SNR was 117 ± 126 in bone

metastases (N = 4), and 85 ± 7 between liver involvements

(N = 2). In general, the SNR in all cases was adequate for

reliable kPL fitting (standard error metric σkPL = 0.005 ±

0.003) [27].

In all five patients with paired 13C-pyruvate MRI and

CT-guided biopsy of the target lesions, the histological

evidence of metastatic prostate cancer was detected. In four

of the five cases, the histology demonstrated poorly differentiated adenocarcinoma. In one patient (Patient 1), the

paired metastatic tumor biopsy demonstrated discrete

regions of adenocarcinoma and treatment-emergent small

cell neuroendocrine differentiation (Fig. 2b) [6].

Higher levels of gene expression of the lactate dehydrogenase isoform A (Ldha,15.7 ± 0.7) relative to the

dominant isoform of pyruvate dehydrogenase (Pdha1, 12.8

± 0.9) were detected on RNA-seq of the target metastatic

biopsies (Table 2), consistent with enhanced aerobic glycolysis detected in the rate of conversion of pyruvate to

lactate on HP 13C MRI. No significant difference in Ldha or

Pdha1 expression was observed in the patients imaged in

this study compared with a previously published cohort of

metastases from 200 men with mCRPC (Ldha: 15.1 ± 1.1,

p > 0.17; Pdha1: 12.0 ± 0.9, p > 0.06, Wilcoxon ranked sum

test) [6].

HP 13C MRI detected a metabolic rate decrease in a

metastasis following chemotherapy

Patient 2 had mCRPC with liver metastases and low serum

PSA level. Carboplatin + docetaxel chemotherapy was

started 24 days after the baseline HP 13C MRI study

(Fig. 3c). Follow-up HP MRI study 62 days after the

Table 2 Findings from HP 13C MRI including kPL, and RNA

expression of key genes

Patient kPL of target

lesion (s−1

)

Ldha/Pdha1

expression (in log)

SNR tCarb

1 0.013 15.4/12.3 290.3 ± 248.5

2 Baseline: 0.026

Follow-upa

: 0.015

16.2/13.8 Baseline: 89.7 ± 40.9

Follow-upa: 77.7

3 0.017 Not acquired 131.4 ± 10.2

4 0.026 14.7/11.6 27.4 ± 9.9

5 0.023 16.4/12.6 19.5 ± 3.6

6 0.025 15.6/13.7 88.2 ± 26.1

SNR tcarb: summed SNR of 13C-labeled tracers averaged over time

a

Follow-up was 2 months after initiation of carboplatin + docetaxel

Fig. 2 a Patient 1 (75 years old) was diagnosed with metastatic

castration-resistant prostate cancer with several large osteoblastic

lesions throughout the left hemipelvis and involving left femur. CT

identified a relatively osteolytic lesion in left ilium (Green arrows),

measuring 9.9 × 4.1 cm. The lesion was infiltrative, causing destruction

of the bone cortex and extension into the surrounding soft tissues. T1-

weighted (T1w) spoiled gradient-echo MRI was used to target the same

lesion observed on CT for the HP 13C MR acquisition. Regions of high

pyruvate-to-lactate conversion rate (kPL) correlated with the osseous

lesion on CT and hypointensity on T1w MRI. kPL was estimated 0.013

(s−1

). b The paired bone biopsy demonstrated discrete regions of

adenocarcinoma and treatment-emergent small cell neuroendocrine

differentiation

H.-Y. Chen et al.

极T代谢磁共振全球科研集锦

121

initiation of treatment demonstrated a 42% decrease in

pyruvate-to-lactate conversion rate kPL, from 0.026 to 0.015

s

−1

, in the target liver lesion (Fig. 3a). This was accompanied by interval decrease of the lesion size (Fig. 3b,

19.3–11.8 mm, 39%) based on RECIST 1.1 criteria, along

with serum PSA decline of >50% from baseline (38–13.4

ng/ml), consistent with systemic treatment response.

Discussion

This work reports the results of the first-ever pilot imaging

study of prostate cancer metastases using HP 13C-pyruvate

MRI. This study demonstrates the feasibility of detecting

real-time metabolic activity of metastases and capture

therapeutic response with this emerging stableisotope molecular imaging method. Correlation with

paired metastatic biopsy demonstrated high-grade prostate

adenocarcinoma, including in one case, evidence of neuroendocrine differentiation.

The high pyruvate-to-lactate conversion rate, kPL, via

upregulated LDH activity in cancer, known as Warburg

effect, reflects cancer aggressiveness, and decrease in kPL

can reflect therapeutic response [14, 20]. Overall, the

pyruvate-to-lactate conversion rate kPL found in bone

(0.020 ± 0.006 s−1) and liver (0.026 ± 0.000 s−1) lesions was

either higher than or comparable with that of high-grade

primary prostate cancer (0.013 ± 0.003 s−1

) in a cohort

imaged prior to radical prostatectomy with whole mount

section pathologic correlation [26]. These high kPL values

were correlated with the metastatic biopsy findings of highgrade adenocarcinoma or mixed high-grade adenocarcinoma and small cell neuroendocrine phenotypes in the

patients studied in this report.

Elevated Ldha expression in prostate cancer is known to

be associated with aggressive phenotypes and resistant to

Fig. 3 Patient 2 (57 years old)

was diagnosed with CRPC that

metastasized to liver. The patient

was previously treated with

enzalutamide and an

investigational agent (BET

inhibitor, phase I) with clinical

progression. Chemotherapy of

carboplatin and docetaxel started

~1 month post baseline HP 13C

scan, and follow-up was

2 months after initiation of

therapy. a A decrease in

pyruvate-to-lactate conversion

rate kPL was observed from

0.026 to 0.015 (s−1

) after

2 months of chemotherapy. Note

the increase in pyruvate and

lactate at 50–60 s post injection.

Most likely this is predominately

due to vascular contributions

coming from intestines. b

Follow-up 2 months after

initiation of therapy found a

decrease in lesion size

(19.3–11.8 mm) indicating

therapeutic response based on

RECIST criteria. c In addition,

serum PSA decreased from

38–13.4 ng/ml also indicating

therapeutic response. HA arterial

phase, PV portal venous phase

Hyperpolarized 13C-pyruvate MRI detects real-time metabolic flux in prostate. . .

极T代谢磁共振全球科研集锦

122

therapy [28–30]. The high Ldha expression relative to

Pdha1 in this study was consistent with a larger published

mCRPC cohort [6], reflective of enhanced aerobic glycolysis, whereas in normal prostate epithelial cells the glucose

metabolism favors oxidative phosphorylation, and Pdha1

expression should predominate (Fig. 1). This suggested that

the metabolic features observed in this study using HP 13Cpyruvate MRI could potentially serve as a representative

cross-section of a much larger patient population with different metastatic sites and types of cell morphology.

A previous study in primary prostate cancer indicated

that kPL reflected early response and resistance to androgen

pathway inhibition [20]. In this communication we

observed a correlation between decreased kPL and clinical

response to the combination of platinum plus taxane chemotherapy in a patient with mCRPC. Although preliminary,

these findings suggest that imaging metabolic signatures

using HP 13C-pyruvate MR could potentially report

responses to a broader range of oncogenic pathway inhibition and drug targets, and may be less susceptible to the

upregulation of membrane protein expression secondary to

ADT [31, 32]. These data suggest that the prospective

evaluation of HP 13C-pyruvate as a response biomarker in

mCRPC patients treated with AR-targeting and cytotoxic

chemotherapy is warranted.

Spatially, regions of high kPL showed good alignment

with radiographic findings of metastases using bone scan,

CT, and proton MRI. Temporally, the time-to-peak of

pyruvate bolus was 29 ± 3 s in pelvic bone cases, 23 s in the

rib case, 34 ± 4 s among the liver cases. The bolus delivery

timing was generally consistent with contrast CT/MRI [33],

and are deemed reasonable in light of hemodynamic variations between subjects, and also the vitals of individual

subject at the time of the scan. The inputless kPL model

applied in this study is relatively immune to variations in

bolus characteristics [25].

Differential kPL was observed between viable and

necrotic-appearing regions of Patient 6’s liver lesion (Supplementary Fig. 4b). These findings are consistent with

other emerging reports of intra- and inter-tumoral heterogeneity in mCRPC [34] and highlight the potential utility of

this imaging tool to clarify tumor biology with real-time

metabolic monitoring [34–37]. The kPL heterogeneity

between metastatic sites/individual patients and its biological underpinning calls for future investigation.

This study also demonstrated that this technology can

provide quantitative metrics of the delivery/uptake of the

injected hyperpolarized carbon isotope by measuring the

total carbon SNR summing the 13C signal observed from

the hyperpolarized pyruvate bolus and downstream metabolic products. Conceptually similar to SUV in PET, total

carbon SNR is a metric of delivery and uptake. Of the

pelvic bone involvements, patient 1, whose lesion appeared

relatively more lytic on CT (Fig. 2a), had higher mean total

SNRPatient 1 = 290 versus the other two cases (SNRPatient 4 =

27.4, SNRPatient 5 = 19.5) with more sclerotic appearances

(Supplementary Figs. 1 and 3). This presents an intriguing

concordance with PET literature in which osteolytic lesions

have shown higher FDG uptake compared with osteoblastic

ones [38–40] and glucose metabolism is known to be differently regulated in sclerotic versus lytic diseases [41].

Several key limitations should be identified for this pilot

study. The correlation between metabolic biomarker kPL and

total carbon SNR is yet to be elucidated in the mCRPC

setting. In addition, the test–retest repeatability data are also

needed moving forward. While the total carbon SNR reports

tracer pharmacokinetics at each metastatic site, its quantitative accuracy can be further enhanced using automatic B1

calibration and correction for QC parameters. This study

utilized a 2D single-slice imaging strategy. Future

advancement in array receiver hardware [42] and MR

acquisition sequences [21, 43] will enable full 3D coverage

of the abdomen/pelvis and seamless integration with

standard-of-care restaging scans. Dissemination of this

technology, in terms of infrastructure and instruments,

requires a clinical 13C polarizer and specialized MRI hardware. The on-site pharmaceutical manufacturing follows the

same standard as PET, allowing for shared facility [14].

These capabilities can readily be instated in high-volume

tertiary centers who manages the majority of the advanced

prostate cancer cohort. Overall, future developments are

warranted to address the technical needs including hardware, image acquisition and quantitative analyses, and the

clinical inquiries deserve to be powered by a larger

cohort study.

These preliminary results highlight the future need to

metabolically characterize lymphadenopathy using HP 13Cpyruvate MRI, as management of nodal disease could be

essential in the realm of biochemically recurrent and oligometastatic PCa [44–47]. For these cohorts of patients,

opportunities for curative treatment are more available, and

clinical outcomes are generally better than those with bone

involvement and thus higher disease burden. Such future

studies could be enabled by the aforementioned technical

advancements to achieve higher resolution and sensitivity,

and new pharmacy QC procedures that reduce HP 13Cpyruvate time-to-injection and thereby improving

SNR [14].

Conclusions

This pilot study evaluated the safety and feasibility to

conduct HP 13C MRI studies of patients with metastatic

prostate cancer to the skeleton and viscera, which represents

the most advanced and lethal form of the disease. Methods

H.-Y. Chen et al.

极T代谢磁共振全球科研集锦

123

were examined and established for instrumentation setup,

pharmacy manufacturing, image acquisition, and quantitative analysis. Safety was demonstrated and highly upregulated pyruvate-to-lactate conversion kPL was observed on

aggressive osseous and hepatic metastases. Interval

decrease of kPL was found for one patient receiving combination chemotherapy, in concordance with conventional

clinical biochemical and imaging biomarkers. These findings warrant further development and investigation of HP

13C-pyruvate MRI in a larger prospective group of men with

metastatic CRPC.

Acknowledgements This work was supported by grants from the NIH

(R01 CA183071, U01EB026412, R01CA215694, R01CA166655,

U01CA232320, and P41EB013598).

Compliance with ethical standards

Conflict of interest The authors declare that they have no conflict of

interest.

Publisher’s note Springer Nature remains neutral with regard to

jurisdictional claims in published maps and institutional affiliations.

Open Access This article is licensed under a Creative Commons

Attribution 4.0 International License, which permits use, sharing,

adaptation, distribution and reproduction in any medium or format, as

long as you give appropriate credit to the original author(s) and the

source, provide a link to the Creative Commons license, and indicate if

changes were made. The images or other third party material in this

article are included in the article’s Creative Commons license, unless

indicated otherwise in a credit line to the material. If material is not

included in the article’s Creative Commons license and your intended

use is not permitted by statutory regulation or exceeds the permitted

use, you will need to obtain permission directly from the copyright

holder. To view a copy of this license, visit http://creativecommons.

org/licenses/by/4.0/.

References

1. Society AC. Cancer facts and figures 2019. Atlanta, GA: American Cancer Society; 2019.

2. Padhani AR, Lecouvet FE, Tunariu N, Koh DM, De Keyzer F,

Collins DJ, et al. Rationale for modernising imaging in advanced

prostate cancer. Eur Urol Focus. 2017;3:223–39.

3. James ND, Spears MR, Clarke NW, Dearnaley DP, De Bono JS,

Gale J, et al. Survival with newly diagnosed metastatic prostate

cancer in the “Docetaxel Era”: data from 917 patients in the

control arm of the STAMPEDE trial (MRC PR08, CRUK/06/

019). Eur Urol. 2015;67:1028–38.

4. Goodman OB Jr., Flaig TW, Molina A, Mulders PF, Fizazi K,

Suttmann H, et al. Exploratory analysis of the visceral disease

subgroup in a phase III study of abiraterone acetate in metastatic

castration-resistant prostate cancer. Prostate Cancer Prostatic Dis.

2014;17:34–9.

5. Pezaro C, Omlin A, Lorente D, Rodrigues DN, Ferraldeschi R,

Bianchini D, et al. Visceral disease in castration-resistant prostate

cancer. Eur Urol. 2014;65:270–3.

6. Aggarwal R, Huang J, Alumkal JJ, Zhang L, Feng FY, Thomas

GV, et al. Clinical and genomic characterization of treatmentemergent small-cell neuroendocrine prostate cancer: a multiinstitutional prospective study. J Clin Oncol. 2018;36:2492–503.

7. Scher HI, Halabi S, Tannock I, Morris M, Sternberg CN, Carducci

MA, et al. Design and end points of clinical trials for patients with

progressive prostate cancer and castrate levels of testosterone:

recommendations of the prostate cancer clinical trials working

group. J Clin Oncol. 2008;26:1148–59.

8. O’Sullivan GJ, Carty FL, Cronin CG. Imaging of bone metastasis:

an update. World J Radiol. 2015;7:202–11.

9. Hope TA, Truillet C, Ehman EC, Afshar-Oromieh A, Aggarwal R,

Ryan CJ, et al. 68Ga-PSMA-11 PET imaging of response to

androgen receptor inhibition: first human experience. J Nucl Med.

2017;58:81–4.

10. Seitz AK, Rauscher I, Haller B, Krönke M, Luther S, Heck MM,

et al. Preliminary results on response assessment using 68 GaHBED-CC-PSMA PET/CT in patients with metastatic prostate

cancer undergoing docetaxel chemotherapy. Eur J Nucl Med Mol

Imaging. 2018;45:602–12.

11. Nelson SJ, Kurhanewicz J, Vigneron DB, Larson PEZ, Harzstark

AL, Ferrone M, et al. Metabolic imaging of patients with prostate

cancer using hyperpolarized [1-C-13]Pyruvate. Sci Transl Med.

2013;5:198ra108

12. Ardenkjaer-Larsen JH, Fridlund B, Gram A, Hansson G, Hansson

L, Lerche MH, et al. Increase in signal-to-noise ratio of >10,000

times in liquid-state NMR. Proc Natl Acad Sci USA.

2003;100:10158–63.

13. Albers MJ, Bok R, Chen AP, Cunningham CH, Zierhut ML,

Zhang VY, et al. Hyperpolarized 13C lactate, pyruvate, and alanine: noninvasive biomarkers for prostate cancer detection and

grading. Cancer Res. 2008;68:8607–15.

14. Kurhanewicz J, Vigneron DB, Ardenkjaer-Larsen JH, Bankson

JA, Brindle K, Cunningham CH, et al. Hyperpolarized (13)C

MRI: path to clinical translation in oncology. Neoplasia.

2018;21:1–16.

15. Hirschhaeuser F, Sattler UG, Mueller-Klieser W. Lactate: a

metabolic key player in cancer. Cancer Res. 2011;71:6921–5.

16. Kim JW, Dang CV. Cancer’s molecular sweet tooth and the

Warburg effect. Cancer Res. 2006;66:8927–30.

17. Gonzalez PS, O’Prey J, Cardaci S, Barthet VJA, Sakamaki JI,

Beaumatin F, et al. Mannose impairs tumour growth and enhances

chemotherapy. Nature. 2018;563:719–23.

18. Zhou M, Zhao Y, Ding Y, Liu H, Liu Z, Fodstad O, et al. Warburg

effect in chemosensitivity: targeting lactate dehydrogenase-A resensitizes taxol-resistant cancer cells to taxol. Mol Cancer.

2010;9:33.

19. Timm KN, Kennedy BW, Brindle KM. Imaging tumor metabolism to assess disease progression and treatment response. Clin

Cancer Res. 2016;22:5196–203.

20. Aggarwal R, Vigneron DB, Kurhanewicz J. Hyperpolarized 1-

[(13)C]-pyruvate magnetic resonance imaging detects an early

metabolic response to androgen ablation therapy in prostate cancer. Eur Urol. 2017;72:1028–9.

21. Chen HY, Larson PEZ, Gordon JW, Bok RA, Ferrone M, van

Criekinge M, et al. Technique development of 3D dynamic CSEPSI for hyperpolarized (13) C pyruvate MR molecular imaging

of human prostate cancer. Magn Reson Med. 2018;80:2062–72.

22. Park I, Larson PEZ, Gordon JW, Carvajal L, Chen HY, Bok R,

et al. Development of methods and feasibility of using hyperpolarized carbon-13 imaging data for evaluating brain metabolism in

patient studies. Magn Reson Med. 2018;80:864–73.

23. Brender JR, Kishimoto S, Merkle H, Reed G, Hurd RE, Chen AP,

et al. PET by MRI: Glucose Imaging by 13C-MRS without

dynamic nuclear polarization by noise suppression through tensor

decomposition rank reduction. bioRxiv. 2018; preprint 15 February 2018; https://doi.org/10.1101/265793.

Hyperpolarized 13C-pyruvate MRI detects real-time metabolic flux in prostate. . .

极T代谢磁共振全球科研集锦

124

24. Nelson SJ, Brown TR. The accuracy of quantification from 1d

nmr-spectra using the piqable algorithm. J Magn Reson.

1989;84:95–109.

25. Larson PEZ, Chen HY, Gordon JW, Korn N, Maidens J, Arcak M,

et al. Investigation of analysis methods for hyperpolarized 13Cpyruvate metabolic MRI in prostate cancer patients. NMR

Biomed. 2018;31:e3997.

26. Korn N, Larson PEZ, Chen H-Y, Gordon JW, Bok RA, Van

Criekinge M, et al. The rate of hyperpolarized [1-13C] pyruvate to

[1-13C] lactate conversion distinguishes high-grade prostate cancer from low-grade prostate cancer and normal peripheral zone

tissue in patients. Proceedings of the 26th Annual Meeting of

ISMRM, Paris France. 2018; International Society for Magnetic

Resonance in Medicine.

27. Mammoli D, Gordon J, Autry A, Larson PEZ, Li Y, Chen HY,

et al. Kinetic modeling of hyperpolarized carbon-13 pyruvate

metabolism in the human brain. IEEE Trans Med Imaging.

2019; e-pub ahead of print 2 July 2019; https://doi.org/10.1109/

TMI.2019.2926437.

28. Bok R, Lee J, Sriram R, Keshari K, Sukumar S, Daneshmandi S,

et al. The role of lactate metabolism in prostate cancer progression

and metastases revealed by dual-agent hyperpolarized (13)C

MRSI. Cancers. 2019;11:257.

29. Hiew K, Hart CA, Ali A, Elliott T, Ramani V, Sangar V, et al.

Primary mutational landscape linked with pre-docetaxel lactate

dehydrogenase levels predicts docetaxel response in metastatic

castrate-resistant prostate cancer. Eur Urol Focus. 2019;5:

831–41.

30. Koukourakis MI, Giatromanolaki A, Panteliadou M, Pouliliou SE,

Chondrou PS, Mavropoulou S, et al. Lactate dehydrogenase 5

isoenzyme overexpression defines resistance of prostate cancer to

radiotherapy. Br J Cancer. 2014;110:2217–23.

31. Aggarwal R, Wei X, Kim W, Small EJ, Ryan CJ, Carroll P, et al.

Heterogeneous flare in prostate-specific membrane antigen positron emission tomography tracer uptake with initiation of androgen pathway blockade in metastatic prostate cancer. Eur Urol

Oncol. 2018;1:78–82.

32. Schwarzenboeck SM, Rauscher I, Bluemel C, Fendler WP, Rowe

SP, Pomper MG, et al. PSMA Ligands for PET imaging of

prostate cancer. J Nucl Med. 2017;58:1545–52.

33. Heiken JP, Bae KT. Contrast medium administration and scan

timing for MDCT. In: Marchal G, Vogl TJ, Heiken JP, Rubin GD

(eds). Multidetector-Row Computed Tomography, Springer: New

York 2005, pp 13–20.

34. Davnall F, Yip CS, Ljungqvist G, Selmi M, Ng F, Sanghera B,

et al. Assessment of tumor heterogeneity: an emerging imaging

tool for clinical practice? Insights Imaging. 2012;3:573–89.

35. Hockel M, Vaupel P. Tumor hypoxia: definitions and current

clinical, biologic, and molecular aspects. J Natl Cancer Inst.

2001;93:266–76.

36. Beltran H, Rickman DS, Park K, Chae SS, Sboner A, MacDonald

TY, et al. Molecular characterization of neuroendocrine prostate

cancer and identification of new drug targets. Cancer Discov.

2011;1:487–95.

37. Lalonde E, Ishkanian AS, Sykes J, Fraser M, Ross-Adams H,

Erho N, et al. Tumour genomic and microenvironmental heterogeneity for integrated prediction of 5-year biochemical recurrence

of prostate cancer: a retrospective cohort study. Lancet Oncol.

2014;15:1521–32.

38. Beheshti M, Vali R, Waldenberger P, Fitz F, Nader M, Loidl W,

et al. Detection of bone metastases in patients with prostate cancer

by 18F fluorocholine and 18F fluoride PET-CT: a comparative

study. Eur J Nucl Med Mol Imaging. 2008;35:1766–74.

39. Nakai T, Okuyama C, Kubota T, Yamada K, Ushijima Y, Taniike

K, et al. Pitfalls of FDG-PET for the diagnosis of osteoblastic

bone metastases in patients with breast cancer. Eur J Nucl Med

Mol Imaging. 2005;32:1253–8.

40. Cook GJ, Houston S, Rubens R, Maisey MN, Fogelman I.

Detection of bone metastases in breast cancer by 18FDG PET:

differing metabolic activity in osteoblastic and osteolytic lesions. J

Clin Oncol. 1998;16:3375–9.

41. Todenhofer T, Stenzl A, Hofbauer LC, Rachner TD. Targeting

bone metabolism in patients with advanced prostate cancer: current options and controversies. Int J Endocrinol. 2015;2015:

838202.

42. Autry AW, Gordon JW, Carvajal L, Mareyam A, Chen HY, Park

I, et al. Comparison between 8‐and 32‐channel phased‐array

receive coils for in vivo hyperpolarized 13C imaging of the human

brain. Magn Reson Med. 2019;82:833–41.

43. Gordon JW, Chen HY, Autry A, Park I, Van Criekinge M,

Mammoli D, et al. Translation of Carbon-13 EPI for hyperpolarized MR molecular imaging of prostate and brain cancer patients.

Magn Reson Med. 2019;81:2702–9.

44. Rigatti P, Suardi N, Briganti A, Da Pozzo LF, Tutolo M, Villa L,

et al. Pelvic/retroperitoneal salvage lymph node dissection for

patients treated with radical prostatectomy with biochemical

recurrence and nodal recurrence detected by [11C]choline positron

emission tomography/computed tomography. Eur Urol.

2011;60:935–43.

45. Ost P, Reynders D, Decaestecker K, Fonteyne V, Lumen N, De

Bruycker A, et al. Surveillance or metastasis-directed therapy for

oligometastatic prostate cancer recurrence: a prospective, randomized, multicenter phase II trial. J Clin Oncol. 2018;36:446–53.

46. Foster CC, Weichselbaum RR, Pitroda SP. Oligometastatic prostate cancer: reality or figment of imagination? Cancer. 2019;125:

340–52.

47. Cornford P, Bellmunt J, Bolla M, Briers E, De Santis M, Gross T,

et al. EAU-ESTRO-SIOG guidelines on prostate cancer. part II:

treatment of relapsing, metastatic, and castration-resistant prostate

cancer. Eur Urol. 2017;71:630–42.

H.-Y. Chen et al.

极T代谢磁共振全球科研集锦

126

极T代谢磁共振全球科研集锦

127

心血管篇

极T代谢磁共振全球科研集锦

极T代谢磁共振全球科研集锦

128

Hyperpolarized 13C Metabolic MRI of the

Human Heart: Initial Experience

研究背景

研究结论

应用方向

研究结果

研究对象

႐ሤీଉ߀Վሞ႐ປ৊ቛዐഐڟዘᄲፕᆩăକ঴ٗࠀీցڟ׋代׋ۙ฿ڦ႐ປײࡗዐዐक़代谢ᅴڦ׉ፕᆩLj๟研৯

ႎዎଐݛ݆ڦᆶയ૰ࢅထྭڦ཰০ă༹ా႐ሤ代谢ᅙᆶڦೠݛࠚ݆Ԉઔኟۉጱ݀พ܏֫ฝᆖDŽPETDžࢅ磁共振հ೷

DŽMRSDžăMRS ๴၌ᇀডڦگଳ௺܈Ljॠ֪代谢࿿ڦ૰ీᆶ၌ǗPET ৈీೠࠚ೯༥༛৊෇ဦԇײࡗڦLj࿮݆ܔ೯༥

༛ࢫჄ༛ট঴ዐڦ代谢ୟ০त代谢࿿ඁၠ৊ႜೠࠚLjཞ้ PET ๖ጷव٪ሞᅃۨवଉۉڦ૗ޖพLjܔႴᄲዘް๑ᆩڦ

൧઄ᆶ໯၌዆ă

ג极ࣅ] 1-13C]pyruvate ׯ़ၟᆶߛᇀ MRS ຕ߲ຕଉपڦ႑ࡽሺഽLjཞ้੗ᅜܔ༛ট঴ዐक़ׂ࿿եཛྷ໗ڦփཞ代谢ඁ

ၠ৊ႜॠ֪Ljᄺ࿮ۉ૗ޖพڦᆖၚLj๟ႎڦᆶയ૰႐ሤ代谢ׯၟरຍă

๯ْሞටૌ႐Ԩ研৯ሤฉቛਸג极ࣅ] 1-13C]pyruvate 代谢ׯၟLj༑໭եཛྷ໗႐ሤ代谢ײࡗዐ代谢࿿ॠ֪ڦ੗ႜႠᅜ

त၎ᆌ代谢཰০٣ࣅாڦऄႠă

໯ᆶ๴๬ኁۼీڦࡻ࢔ఱ๴ג极ࣅ] 1-13C]pyruvate ׯၟײࡗLj࿮փଆݒᆌăሞᅃْׯၟዐ຺ዖா٣ݒࣅᆌ๟੗ᅜॠ

֪ڦڟǖեཛྷ໗ྃൠா (PDC)Ăեҽ໗ገҽா (ALT)Ăළ໗ྃൠா (LDH) ࢅ༐໗᷿ா (CA)ă႐कా [

13C] ༐໗ൠჸ႑ࡽ

ᇑ঻ڞեཛྷ໗ཚࡗ၍૭༹ఈڦ PDC ாऄႠׯԲ૩Ljُ݀၄੗ీᆶዺᇀएᇀ PDC ऄႠۙবڦ႐ሤ代谢ૌदթዎଐݛ

ӄڦ研৯ăኄ՗௽Lj૧ᆩణമڦ 13C ג极ࣅरຍ੺໏ĂҾ全Ăఱ๴Ⴀଆںࡻೠࠚեཛྷ໗ሞට༹ాڦ代谢๟੗ႜڦă

ኟ׉႐ሤ代谢ऐ዆ǗथႠඍე / ም࠺ጀ໦ฅࢫ႐कऄ૰ೠࠚǗ႐ປራ೺ࢅ೺ྟ代谢ՎࣅǗ႐ປ代谢Ӝၠዎଐ研৯Ǘ

༛ౖթ႐कթೠڪࠚă

ኟ׉ኾᇼኁ႐ሤ[1-13C]pyruvateLj[

13C]༐໗ൠჸLj[13C]ළ

໗၂ၟă[ 13C]༐໗ൠჸ႑ࡽዷᄲਆ၌ᇀፑ๪႐कǗ[1-

13C]pyruvate႑ࡽዷᄲሞ႐ሤഹ๪DŽე׾DžLjփሞ႐क

ዐǗ[

13C]ළ໗ཞ้ሞ႐๪ࢅ႐कዐ਩ᆶă

4ఁॳ੃ኾᇼኁǖኟ׉႐क代谢ऐ዆

极T代谢磁共振全球科研集锦

129

DOI: 10.1161/CIRCRESAHA.116.309769 1

BRIEF ULTRARAPID COMMUNICATION

Hyperpolarized 13C Metabolic MRI of the Human Heart: Initial Experience

Charles H. Cunningham1,2, Justin Y.C. Lau1,2, Albert P. Chen3, Benjamin J. Geraghty1,2, William J.

Perks4

, Idan Roifman5, Graham A. Wright1,2,5 Kim A. Connelly6

1

Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada; 2Medical Biophysics, University

of Toronto, Toronto, ON, Canada; 3GE Healthcare, Toronto, ON, Canada; 4Pharmacy, Sunnybrook Health

Sciences Centre, Toronto, ON, Canada; 5Schulich Heart Program, Sunnybrook Health Sciences Centre,

Toronto, ON, Canada, and; 6

Cardiology, St. Michael’s Hospital, Toronto, ON, Canada.

Running title: Hyperpolarized MRI of the Human Heart

Subject Terms:

Metabolism

Translational Studies

Magnetic Resonance Imaging (MRI)

Heart Failure

Hypertrophy

Address correspondence to:

Charles H. Cunningham

M7-613

2075 Bayview Ave.

Toronto, ON

M4N 3M5

Canada

Tel: 416-480-5021

Fax: 416-480-5718

chuck@sri.utoronto.ca

In August 2016, the average time from submission to first decision for all original research papers submitted

to Circulation Research was 13.98 days.

极T代谢磁共振全球科研集锦

130

DOI: 10.1161/CIRCRESAHA.116.309769 2

ABSTRACT

Rationale: Altered cardiac energetics is known to play an important role in the progression towards heart

failure. A non-invasive method for imaging metabolic markers that could be used in longitudinal studies

would be useful for understanding therapeutic approaches that target metabolism.

Objective: To demonstrate the first hyperpolarized 13C metabolic magnetic resonance imaging (MRI) of the

human heart.

Methods and Results: Four healthy subjects underwent conventional proton cardiac MRI followed by 13C

imaging and spectroscopic acquisition immediately following intravenous administration of a 0.1 mmol/kg

dose of hyperpolarized [1-13C]pyruvate. All subjects tolerated the procedure well with no adverse effects

reported up to one month post-procedure. The [1-13C]pyruvate signal appeared within the chambers but not

within the muscle. Imaging of the downstream metabolites showed 13C-bicarbonate signal mainly confined

to the left ventricular myocardium whereas the [1-13C]lactate signal appeared both within the chambers and

in the myocardium. The mean 13C image signal-to-noise ratio was 115 for [1-13C]pyruvate, 56 for 13Cbicarbonate, and 53 for [1-13C]lactate.

Conclusions: These results represent the first 13C images of the human heart. The appearance of 13Cbicarbonate signal after administration of hyperpolarized [1-13C]pyruvate was readily detected in this

healthy cohort (N=4). This shows that assessment of pyruvate metabolism in vivo in humans is feasible

using current technology.

ClinicalTrials.gov Identifier: NCT02648009

https://clinicaltrials.gov/ct2/show/NCT02648009?term=hyperpolarized+pyruvate&rank=4

Keywords: metabolism, DNP, hyperpolarized, carbon-13, pyruvate, magnetic resonance imaging,

magnetic resonance spectroscopy, metabolic imaging, heart failure, 13C, mitochondria.

Nonstandard Abbreviations and Acronyms:

CMR cardiac magnetic resonance

DNP dynamic nuclear polarization

PDC pyruvate dehydrogenase complex

RF radiofrequency

极T代谢磁共振全球科研集锦

131

DOI: 10.1161/CIRCRESAHA.116.309769 3

INTRODUCTION

Understanding the role of altered intermediary metabolism in driving the transition from functional

compensation to decompensated heart failure remains a promising avenue for the development of new

therapies [1,2,3,4]. The foundations of cardiac metabolism research are built on experiments in vitro and

in the isolated perfused heart [5,6]. However, to reproduce the effects of insulin and other hormones, plasma

substrate levels, and workload/energy demand, in vivo assessment of cardiac metabolism is of paramount

importance.

Existing clinical imaging modalities for studying cardiac metabolism include positron emission

tomography (PET) and magnetic resonance spectroscopy (MRS). Whilst providing unique insights into

metabolism, both techniques suffer limitations. MRS is only able to detect a limited range of biochemical

reactions due to the inherent low sensitivity of this technique and PET gives no information about the

metabolic fate of the substrate beyond its cellular uptake. Furthermore, PET tracers deliver a dose of

ionizing radiation, thus limiting repeated application. New methods for non-invasively probing the

dynamics of cardiac metabolism in patients are still needed to augment the information currently available

to the clinician.

Hyperpolarized carbon-13 (13C) magnetic resonance imaging (MRI) is promising in this regard,

since it can give images showing the uptake of metabolic substrates and subsequent intracellular conversion

into downstream products [7,8,9,10]. The method relies on rapid dissolution dynamic nuclear polarization

(DNP), which can provide a signal enhancement of more than four orders of magnitude [7]. Measurements

based on these images may give new information about the metabolic state of the heart in individual

patients. In addition to its scientific value as a powerful tool for investigating normal cardiac metabolism

[11], developments in preclinical models support the potential clinical value of 13C MRI in evaluating

pathologies of the human heart including: myocardial viability following acute ischemia/reperfusion injury

[12], early- and late-onset metabolic changes in the failing heart [13], and diabetic cardiomyopathy [14].

Using the substrate [1-13C]pyruvate, which is an important intermediate of cellular metabolism, this

study demonstrates the feasibility of observing, in a single exam, the following four different single-step

enzyme-catalyzed reactions: pyruvate dehydrogenase (PDC), alanine aminotransferase (ALT), lactate

dehydrogenase (LDH), and carbonic anhydrase (CA). Of particular interest is the 13C-bicarbonate signal

that can be measured within the myocardium after an intravenous injection of [1-13C]pyruvate, which is

proportional to flux of pyruvate through PDC on the mitochondrial membrane [8]. Since 13C MRI can be

integrated into a conventional cardiovascular magnetic resonance (CMR) workup with only a small addition

to the scan duration (e.g. 10 minutes), the translation of this new form of MRI to patient studies is readily

achievable, particularly where cardiac MRI is already used clinically. The first images of hyperpolarized 13C MRI in the human heart are presented in this pilot study in four healthy volunteers.

极T代谢磁共振全球科研集锦

132

DOI: 10.1161/CIRCRESAHA.116.309769 4

METHODS

Healthy subjects (N=4) were recruited and gave written informed consent under a protocol approved by the

Sunnybrook Research Ethics Board and approved by Health Canada as a Clinical Trial Application.

Subjects were instructed to eat as they normally would, and an oral carbohydrate load was administered

approximately 1 hour before the pyruvate injection as 35 g of GatoradeTM powder in water, containing 34

g of sucrose and dextrose, to be consistent with a protocol previously established for preclinical cardiac

imaging [15]. A 20 gauge intravenous catheter was placed in the left arm before each subject was positioned

supine and feet first within a 13C volume transmit coil system (GE Healthcare, Cleveland OH) installed on

a GE MR750 3.0 Tesla MRI scanner (GE Healthcare, Waukesha WI). The 13C receiver coil system

consisted of two separate paddles each containing four receiver elements (channels) [16]. One paddle was

positioned on the anterior chest wall over the heart, with the other paddle under the upper left back. The

left arm was fully extended, positioned directly by the side of the subject and supported with padding to

prevent direct contact with the transmit coil. A pulse oximeter was placed on the left index finger for

cardiac triggering.

The investigational product, designated with the generic name “Hyperpolarized (13)C Pyruvate Injection”,

was prepared using a General Electric SPINLabTM system [17] equipped with the Quality Control (QC)

module, which provides automated measurement and display of release parameters. Each dose was

compounded from 1.47 ± 0.05 g of [1-13C]pyruvic acid (Sigma Aldrich, St. Louis MO) combined with

AH111501 [Tris(8-carboxy-2,2,6,6 (tetra(methoxyethyl) benzo-[1,2-4,5ƍ]bis-(1,3)dithiole-4- yl)methyl

sodium salt] (Syncom, Groningen, The Netherlands) in a 49:1 weight by weight ratio, respectively. Once

frozen in the polarizer, each sample was exposed to microwave irradiation for approximately 3 hours to

achieve maximum steady-state polarization. The other components within the sterile fluid pathway were

19.0 ± 0.5 mL of sterile water for injection, which is heated and used to rapidly dissolve the frozen sample,

and a buffered base and chelating solution in the receiver vessel made by mixing 17.5 ± 0.5 mL of a stock

solution (600 mmol/L NaOH, 333 mmol/L Tris base, 333 mg/L disodium EDTA) with 19.0 ± 0.5 mL of

sterile water for injection.

Short-axis cardiac-gated CINE images were acquired from slices covering the left ventricle using the body

coil for both transmit and receive, with a separate breath-hold performed for each slice. After completion

of the anatomical scanning, a pre-scan calibration of the 13C receive frequency and transmit power was

performed using the signal from a 1.5 cm diameter spherical phantom containing an ~8 mol/L solution of 13C-urea, which was fixed on top of the anterior receiver coil housing. The dissolution of the polarized

sample was initiated by the operator, and the QC parameters were evaluated by the study pharmacist to

ensure the parameters were within specifications. Upon release, the dose syringe was rapidly loaded onto

a Spectris Solaris power injector (Medrad, Indianola PA) and the volume corresponding to a 0.1 mmol/kg

dose was injected at 5 mL/s followed by a 25 mL saline flush at 5 mL/s.

The 13C imaging data acquisition was initiated at the end of the saline flush, and was preceded by the same

automated breath-hold instructions as used for the anatomical scanning. The data acquisition consisted of

slice-selective spectral-spatial excitation of the 13C-bicarbonate resonance in 6 slices of 1 cm thickness

covering the left ventricle [15]. As a cardiac-gated sequence, the heart rate affects the scan time and sets

an upper limit on the number of slices that can be acquired per heartbeat; the breath-hold duration sets the

lower limit. To maintain a reasonable breath-hold duration, three slices were sequentially excited and

imaged within each cardiac cycle (taking two cardiac cycles to complete all 6), with the cardiac trigger

delay set such that the acquisition window was aligned with diastole. These six slices were then imaged at

the [1-13C]lactate and [1-13C]pyruvate resonances and the whole process was repeated three times, taking

18 cardiac cycles to complete. The nominal spatial resolution was 8.8 mm × 8.8 mm in-plane with a 48 cm

field-of-view.

极T代谢磁共振全球科研集锦

133

DOI: 10.1161/CIRCRESAHA.116.309769 5

Immediately following the 13C imaging, the residual hyperpolarized magnetization was used to acquire MR

spectroscopic data from the whole heart. For the first subject, a slice-selective radiofrequency (RF) pulse

was used (nominal flip angle = 30 degrees, 10 cm axial slab covering the heart) and the acquisition was not

cardiac gated (repetition time of 3 s). For the three subsequent subjects, a 200 µs non-selective RF pulse

was used (nominal flip angle = 18 degrees) and the acquisition was cardiac gated so the repetition times

were approximately 3 s (3 to 4 inter-beat intervals, depending on heart rate). The shorter RF pulse was

intended to excite the 13CO2 resonance, which was not observable in the data from the first subject. Wholeheart spectroscopy was used to provide adequate signal, given that much of the magnetization had likely

been consumed by the preceding 13C imaging.

RESULTS

The four subjects recruited were male with no known prior cardiac history. Their mean age was 41

(range 28 to 48). The mean left ventricular (LV) ejection fraction was 61% (range 55% to 66%). The LV

end diastolic and end systolic volumes indexed to body surface area were 104 (range 82 to 115) and 41

(range 30 to 51) mL/m2

respectively. The mean LV mass (indexed to BSA) was 84 (range 62 to 93) g/m2

(see Table 1).

Of the 4 doses injected, the mean polarization was 28% (range 17% to 35%), pH was 7.35 (range

7.3 to 7.4), temperature was 36.4°C (range 36.0°C to 37.0°C). The duration from dissolution to the start of

imaging ranged from 66 to 71 seconds. Imaging scan duration ranged from 14 to 21 secs. The injected

volume of [1-13C]pyruvate ranged from 29 to 37 mL to achieve 0.1 mmol/kg dose.

All subjects tolerated the procedure well. Two subjects noted a “sweet” taste following [1- 13C]pyruvate injection which dissipated shortly afterwards. No serious adverse effects were noted. During

injection there was no change in heart rate and no reported change in respiration. Non-invasive blood

pressure measured prior to the CMR examination and post [1-13C]pyruvate injection did not vary

significantly. No subject reported any adverse effects up to one month post [1-13C]pyruvate injection. For

subject 04, the 13C imaging acquisition failed due to a scanner malfunction, but the subsequent 13C

spectroscopy acquisition was successful.

Figure 1 shows time-integrated [1-13C]pyruvate, 13C-bicarbonate and [1-13C]lactate images from

two of the three normal healthy volunteers with successful imaging acquisitions. These images were

reconstructed from only the four channels on the anterior chest wall, as the posterior channels gave

insignificant signal. From the spatial distributions observed in the metabolite images, the 13C-bicarbonate

signal appeared mainly confined to the LV myocardium while the [1-13C]pyruvate appeared within the

chambers (in the blood pool) but not within the muscle. In contrast, the [1-13C]lactate signal appeared both

within the chambers and in the myocardium.

The maximum image signal-to-noise ratio (SNR) for each 13C-labelled metabolite across the

subjects is plotted in Figure 2. This was calculated as the maximum pixel value for the corresponding

metabolite image divided by the standard deviation of the noise in the image background, and serves as a

benchmark for the image SNR that can be obtained in humans for this particular spatial resolution and at

the polarization achievable currently. However, since the SNR in MRI varies linearly with the voxel

volume (holding other parameters equal) this benchmark will be useful for the design of future experiments

in patients. The modest spatial resolution used here (8.8 × 8.8 mm in-plane, 10 mm through plane) resulted

in readily detectable metabolic conversion within the tissue and enabled observations about the spatial

distribution of the substrate and products. The bicarbonate signal-to-noise in the right ventricular

极T代谢磁共振全球科研集锦

134

DOI: 10.1161/CIRCRESAHA.116.309769 6

myocardium was insufficient for reliable assessment with the chosen spatial resolution and coil

configuration.

Representative dynamic 13C spectra from one of the subjects are shown in Figure 3. Despite starting

the spectroscopic acquisition following the completion of 13C imaging (and at least 90 s after dissolution)

major metabolites from [1-13C]pyruvate were all observed in the spectra (Figure 3a,b). The peaks observed

correspond to [1-13C]lactate (185 ppm), [1-13C] alanine (179 ppm), [1-13C]pyruvate (172 ppm), and 13Cbicarbonate (162 ppm). From one of the data sets using the non-selective RF pulse, 13CO2 was also observed

and the pH calculated from the 13C-bicarbonate and 13CO2 signals using the Henderson-Hasselbalch

equation was 7.1. For three subjects with cardiac-gated spectroscopic acquisitions, the bicarbonate-topyruvate ratios remained relatively stable during the time points with sufficient SNR for quantification,

ranging from 20 to 70 seconds. In contrast, the lactate-to-pyruvate ratio increased over a similar time frame

for all subjects (Figure 3c-d).

DISCUSSION

To the best of the authors’ knowledge, these results represent the first hyperpolarized 13C images

of the human heart. [1-13C]pyruvate metabolism in the LV myocardium, as indicated by the generation of

the 13C-bicarbonate signal was readily detected in this healthy cohort. This suggests that that this

technology may one day allow a direct measure of flux through the pyruvate dehydrogenase complex in the

myocardium in vivo in humans. The opposing patterns observed in Figure 1 for the [1-13C]pyruvate

substrate and the 13C-bicarbonate product are consistent with rapid [1-13C]pyruvate flux through the LV

myocardial PDC (consuming the [1-13C]pyruvate in the muscle). The diffuse appearance of the [1- 13C]lactate signal was consistent with previous data from a porcine model, where the [1-13C]lactate signal

appeared diffuse in both the blood and the tissue prior to ischemia [12]. Understanding the more diffuse

appearance of the [1-13C]lactate signal would require further experiments, but it is clear that the largest

component is in the blood pool (at the time points imaged here) and that any interpretation of whole-heart 13C spectra must take this into account.

Rationalization of the temporal evolution of the lactate-to-pyruvate and bicarbonate-to-pyruvate

ratios observed in Fig. 3(c) and 3(d) requires some degree of speculation. These data were acquired

approximately one minute after the initial bolus, so the spatial distribution of metabolites seen in Figure 1

had likely changed. A steady state was not observed for the lactate-to-pyruvate ratio, perhaps as a result of

LDH mediated label exchange between the 13C-enriched pyruvate pool and the larger pre-existing lactate

pool [18], resulting in the [1-13C]lactate to [1-13C]pyruvate ratio continuing to approach the endogenous

lactate to pyruvate ratio. Unlike the reversible label exchange between pyruvate and lactate, the labeling

of 13C-bicarbonate is the result of an irreversible forward flux, resulting in enrichment of 13CO2 and 13Cbicarbonate. Through the normal physiology of aerobic respiration, intracellular 13CO2 is continually

exported and transported away via the blood, draining the 13C-bicarbonate pool. This “negative feedback”

on the 13C-bicarbonate pool may account for the more stable trend in the 13C-bicarbonate to [1-13C]pyruvate

ratio over this timeframe.

Importantly, no adverse events were recorded (except for the taste experienced by two subjects)

and the injection and 13C imaging were well tolerated. With this tool to assess in vivo PDC flux in a rapid,

safe and well tolerated manner, longitudinal studies in humans incorporating this metabolic assessment of

the heart become feasible. The metabolic information comes along with the detailed assessment of cardiac

structure and function from the conventional CMR assessment done during the same scanning session. This

augmented form of CMR is anticipated to provide novel insights into how metabolic changes relate to the

process of functional decompensation leading to heart failure.

极T代谢磁共振全球科研集锦

135

DOI: 10.1161/CIRCRESAHA.116.309769 7

There are several limitations to the current technique and to this study. To apply this method,

specialised equipment in the form of the SPINLabTM and 13C cardiac coils are required, limiting the number

of sites that are currently able to do these studies. With the current polarization level and imaging method,

spatial resolution was limited due to a trade-off with SNR (these will certainly improve in the future as

polarization levels increase and coils are improved). Thus imaging the ischemic heart to assess myocardial

viability, or other structures in the heart, such as the right ventricle, remain technically challenging. Lastly,

this study only included normal volunteers, so the feasibility in a population with disease remains to be

shown.

The clinical potential for this technique remains considerable. As demonstrated by our previous

work in porcine models of heart failure [12], as well as work by other groups [19], the ability to perform

repeated assessments of PDC flux will provide important insights into disease pathogenesis that can

potentially facilitate treatment strategies in the form of PDC modulators. A significant body of work

implicates PDC activity as an important determinant of cardiac function, particularly in states where insulin

resistance occurs [20]. For instance, in insulin-resistant ob/ob mice, enhanced fatty acid metabolism at the

expense of glucose oxidation is associated with impaired contractile function [21]. Indeed, there is a

growing body of evidence to suggest that improving contractile function may be associated with the

normalization of PDC flux. Changes in PDC activity may not only be a marker for abnormal cellular

metabolism and increased oxidative stress, it may serve as a therapeutic target to prevent the development

of heart failure in states where PDC activity is impaired [22]. However, since the relative utilization of fatty

acids and carbohydrates shifts depending on the etiology and stage of disease [23], the appropriate therapy

will require individualization for the particular patient, and this is where CMR plus 13C MRI may be

clinically valuable.

Conclusions.

These results represent the first 13C images of the human heart. The appearance of hyperpolarized

signals of both 13CO2 and 13C-bicarbonate from [1-13C]pyruvate suggests that that this technology may one

day allow a direct measure of flux through the pyruvate dehydrogenase complex in the myocardium in vivo

in humans.

ACKNOWLEDGEMENTS

The authors are grateful to Tracey Rideout, Sergio DeFigueiredo and Stephanie Vidotto for pharmacy

technician support for this study, to Julie Green for coordinating the study, and to Ruby Endre and Garry

Detzler for MR technician support.

SOURCES OF FUNDING

Heart and Stroke Foundation of Canada. Dr KA Connelly is supported by a CIHR New Investigator award.

DISCLOSURES

AP Chen is an employee of GE Healthcare.

极T代谢磁共振全球科研集锦

136

DOI: 10.1161/CIRCRESAHA.116.309769 8

REFERENCES

[1] Stanley WC, Chandler MP. Energy metabolism in the normal and failing heart: potential for therapeutic

interventions. Heart Fail Rev. 2002;7:115-130.

[2] Ashrafian H, Frenneaux MP, Opie LH. Metabolic mechanisms in heart failure. Circulation.

2007;116:434-448.

[3] Ardehali H, Sabbah HN, Burke MA, Sarma S, Liu PP, Cleland JG, Maggioni A, Fonarow GC, Abel

ED, Campia U, Gheorghiade M. Targeting myocardial substrate metabolism in heart failure: potential for

new therapies. Eur J Heart Fail. 2012;14:120-129.

[4] Doenst T, Nguyen TD, Abel ED. Cardiac metabolism in heart failure implications beyond ATP

production. Circ Res. 2013;113:709-724.

[5] Garlick PB, Radda GK, Seeley PJ, Chance B. Phosphorus NMR studies on perfused heart. Biochem

Bioph Res Co. 1977;74:1256-1262.

[6] Bailey IA, Gadian DG, Matthews PM, Radda GK, Seeley PJ. Studies of metabolism in the isolated,

perfused rat heart using 13C NMR. FEBS Lett. 1981;123:315-318.

[7] Ardenkjaer-Larsen JH, Fridlund B, Gram A, Hansson G, Hansson L, Lerche MH, Servin R, Thaning

M, Golman K. Increase in signal-to-noise ratio of > 10,000 times in liquid-state NMR. Proc Natl Acad Sci

USA. 2003;100:10158–10163.

[8] Merritt ME, Harrison C, Storey C, Jeffrey FM, Sherry AD, Malloy CR. Hyperpolarized 13C allows a

direct measure of flux through a single enzyme-catalyzed step by NMR. Proc Natl Acad Sci USA.

2007;104:19773–19777.

[9] Golman K, Petersson JS, Magnusson P, Johansson E, Åkeson P, Chai CM, Hansson G, Månsson S.

Cardiac metabolism measured noninvasively by hyperpolarized 13C MRI. Magn Reson Med.

2008;59:1005–1013.

[10] Schroeder MA, Cochlin LE, Heather LC, Clarke K., Radda GK, Tyler DJ. In vivo assessment of

pyruvate dehydrogenase flux in the heart using hyperpolarized carbon-13 magnetic resonance. Proc Natl

Acad Sci USA. 2008;105:12051–12056.

[11] Schroeder MA, Clarke K, Neubauer S, Tyler DJ. Hyperpolarized magnetic resonance A novel

technique for the In vivo assessment of cardiovascular disease. Circulation. 2011;124:1580-1594.

[12] Lau AZ, Chen AP, Barry J, Graham JJ, Dominguez-Viqueira W, Ghugre NR, Wright GA, Cunningham

CH. Reproducibility Study for Free-Breathing Measurements of Metabolism Using Hyperpolarized 13C in

the Heart. Magn Reson Med. 2013;69:1063-1071.

[13] Schroeder MA, Lau AZ, Chen AP, Gu Y, Nagendran J, Barry J, Hu X, Dyck JR, Tyler DJ, Clarke K,

Connelly KA, Wright GA, CH Cunningham. Hyperpolarized 13C magnetic resonance reveals early‐ and

late‐onset changes to in vivo pyruvate metabolism in the failing heart. Eur J Heart Fail. 2013;15:130-140.

[14] Le Page LM, Rider OJ, Lewis AJ, Ball V, Clarke K, Johansson E, Carr CA, Heather LC, Tyler DJ.

Increasing pyruvate dehydrogenase flux as a treatment for diabetic cardiomyopathy: a combined 13C

hyperpolarized magnetic resonance and echocardiography study. Diabetes. 2015;64:2735-2743.

极T代谢磁共振全球科研集锦

137

DOI: 10.1161/CIRCRESAHA.116.309769 9

[15] Lau AZ, Chen AP, Ghugre NR, Ramanan V, Lam WW, Connelly KA, Wright GA, Cunningham CH.

Rapid multislice imaging of hyperpolarized 13C pyruvate and bicarbonate in the heart. Magn Reson Med.

2010;64:1323-1331.

[16] Tropp J, Calderon P, Carvajal L, Robb F, Larson PEZ, Shin P, Vigneron DB, Nelson SJ. A carbon

receive array of 8 elements, interoperable with proton scanning, for human temporal lobe. Proceedings of

the 20th Annual Meeting of ISMRM; Melbourne, Australia. 2012. Abstract 2658.

[17] Ardenkjaer‐Larsen JH, Leach AM, Clarke N, Urbahn J, Anderson D, Skloss TW. Dynamic nuclear

polarization polarizer for sterile use intent. NMR Biomed. 2011;24:927-932.

[18] Witney TH, Kettunen MI, Brindle KM. Kinetic modeling of hyperpolarized 13C label exchange

between pyruvate and lactate in tumor cells. J Biol Chem. 2011;286:24572-24580.

[19] Rider OJ, Tyler DJ. Clinical implications of cardiac hyperpolarized magnetic resonance imaging. J

Cardiov Magn Reson. 2013;15:93.

[20] Taegtmeyer H, McNulty P, Young ME. Adaptation and maladaptation of the heart in diabetes: Part I

General concepts. Circulation. 2002;105:1727-33.

[21] Mazumder PK, O’Neill BT, Roberts MW, Buchanan J, Yun UJ, Cooksey RC, Boudina S, Abel ED.

Impaired cardiac efficiency and increased fatty acid oxidation in insulin-resistant ob/ob mouse hearts.

Diabetes. 2004;53:2366-74.

[22] Lewis AJ, Neubauer S, Tyler DJ, Rider OJ. Pyruvate dehydrogenase as a therapeutic target for obesity

cardiomyopathy. Expert Opin Ther Tar. 2016;20:755-66.

[23] Ardehali H, Sabbah HN, Burke MA, Sarma S, Liu PP, Cleland JG, Maggioni A, Fonarow GC, Abel

ED, Campia U, Gheorghiade M. Targeting myocardial substrate metabolism in heart failure: potential for

new therapies. Eur J Heart Fail. 2012;14:120-129.

极T代谢磁共振全球科研集锦

138

DOI: 10.1161/CIRCRESAHA.116.309769 10

FIGURE LEGENDS

Figure 1: Representative 13C images displayed as color overlays on top of grayscale anatomical images in

a mid-left ventricle (LV) slice from subject 01 (a-c) and subject 03 (d-f). The [1-13C]pyruvate substrate was

seen mainly in the blood pool within the cardiac chambers (a,d). Flux of pyruvate through the pyruvate

dehydrogenase complex is reflected in the 13C-bicarbonate images (b,e), with signal predominantly in the

wall of the LV. The [1-13C]lactate signal (c,f) appeared with a diffuse distribution covering the muscle and

chambers.

Figure 2: Grayscale anatomical (a) and 13C images (b-d) from subject 01 are shown separately with the

calculated maximum signal-to-noise ratio (SNR). A summary of maximum image SNR across the different

subjects is shown on the right (e)..

Figure 3. Representative dynamic 13C spectra acquired using a non-selective excitation pulse from one of

the subjects are shown in a) and b). The spectrum in b) is the sum of the 5 consecutive time points shown

in a). Lactate and bicarbonate to pyruvate ratios from the spectroscopic data are shown in c) and d),

respectively. For the three subjects with cardiac-gated spectroscopic acquisitions, the lactate-to-pyruvate

ratios increased over time whereas the bicarbonate-to-pyruvate ratios remained relatively stable during the

time before the signal-to-noise ratio became too low for quantification.

极T代谢磁共振全球科研集锦

139

DOI: 10.1161/CIRCRESAHA.116.309769 11

Novelty and Significance

What Is Known?

● Altered metabolism plays a role in heart failure progression.

● Therapy that targets metabolism will require individualization for the particular patient.

● Hyperpolarized 13C MRI holds the potential to provide a non-invasive metabolic assessment

What New Information Does This Article Contribute?

● The first hyperpolarized 13C images of the human heart.

● Demonstrated the feasibility of an integrated structural/functional/metabolic MRI assessment.

● A hyperpolarized 13C image-quality benchmark in the human heart using current technology

Altered cardiac metabolism has long been known to play a role in progression of heart failure. Although

there are many methods for making in vivo measurements related to metabolism, a non-invasive test that

can be applied longitudinally in patients has remained an unmet need. In this study, an augmented form of

MRI which enables imaging of the metabolic conversion of pyruvate, an important metabolic intermediate,

into its downstream metabolic products is demonstrated for the first time in humans. These results represent

the first 13C metabolic images of the human heart, and open the door to future studies in patients.

极T代谢磁共振全球科研集锦

140

DOI: 10.1161/CIRCRESAHA.116.309769 12

TABLES

Table 1: Subject characteristics and scan details.

Subject ID 01 02 03 04 Mean

Age (yrs) 28 47 48 41 41

Ejection Fraction (%) 64 66 58 55 61

Body surface area (BSA) 1.79 1.95 2.12 1.95 1.95

Left ventricular (LV) mass (g/m2) 62 93 89 92 84

LV end-diastolic volume index (mL/m2) 82 107 110 115 104

LV end-systolic volume index (mL/m2) 30 36 46 51 41

LV stroke volume index (mL/m2) 52 70 64 64 63

Heart rate (beats per minute) 69 57 55 47 57

Dose given (mL) 29.3 33.0 37.0 33.0 33.1

Start of injection from dissolution (s) 66 71 66 66 67

Start of scan from dissolution (s) 77 84 95 82 85

Duration of imaging scan (s) 14 17 19 21 18

Start of spectroscopy from dissolution (s) 100 123 139 132 124

极T代谢磁共振全球科研集锦

141

a b c

10

30

20

20

60

40

5

25

15

80

40

10

50

30

20

60

40

d e f

5 cm 5 cm 5 cm

5 cm 5 cm 5 cm

Pyruvate Bicarbonate Lactate

Pyruvate Bicarbonate Lactate

FIGURE 1

极T代谢磁共振全球科研集锦

142

Bicarbonate Lactate Pyruvate

0

80

160

Maximum Image SNR

5

15

25

10

20

30

20

40

60

Pyruvate

Bicarbonate

Lactate

CINE

40

80

120

5 cm

5 cm

5 cm

5 cm

a

d

b

c

Max SNR = 80

Max SNR = 38 Max SNR = 104

e)

FIGURE 2

极T代谢磁共振全球科研集锦

143

13C bicarbonate

[1-13C] pyruvate

13CO2

[1-13C] lactate

[1-13C] alanine

0

1.0

2.0

3.0

4.0

5.0

20 30 40 50 60 70 80

subject 2

subject 3

subject 4

0

0.2

20 30 40 50 60 70 80

subject 2

subject 3

subject 4

bicarbonate / pyruvate ratio

a) lactate / pyruvate ratio

b)

c)

d)

139 s

Time postinjection

142 s

146 s

149 s

152 s

0.1

0.3

Time from end of imaging (s)

Time from end of imaging (s)

FIGURE 3

极T代谢磁共振全球科研集锦

144

Efect of Doxorubicin on Myocardial

Bicarbonate Production from Pyruvate

Dehydrogenase in Women with Breast Cancer

研究背景

研究结论

应用方向

研究结果

研究对象

ሞথ๴ᕾ࣍ૌᄱ࿿ዎଐڦළ၇ҵ࣒ኁዐLjࡗג 5% ࣒ڦኁ؜၄႐ሤ۾Ⴀă႐ሤ໦ฅऐ዆ชփྜ全ൣؤLjڍ၍૭༹໦

ฅ੗ీ๟ᇱᅺኮᅃăራ೺݀၄ᕾ࣍ૌᄱ࿿ᆅഐڦ႐क໦ฅܔᇀଣ׉ݥضዘᄲLjڍ๟ణമժ࿄ڦࡻ࢔ᆖၟ๮܎ă

Ԩ研৯ॽג极ࣅ] 1-13C]pyruvate ׯၟᆌᆩᇀҮி໎ႎޤዺࣅଐമࢫළ၇ҵ࣒ኁڦ႐ሤ代谢ׯၟLj༑৯ܔዎଐമࢫ代

谢Վࣅೠڦࠚ੗ႜႠă

໯ᆶ࣒ኁۼీںࡻ࢔ఱ๴ג极ࣅ]1-13C]pyruvateׯၟLj࿮փଆᆖၚLj൐ዎଐമए၍13C MRI೷၎ຼă4߲ଐײኮڦࢫ]

13C]

༐໗ൠჸ႑ࡽԲኵᆶ၂ዸইگLj[

13C] ළ໗႑ࡽᆶူই൵๞ă7 ߲๴๬ڦଐࢫዘް၂ၟ՗௽କג极ࣅ] 1-13C]pyruvate

ׯၟଆڦࡻ੗ዘްႠă

ጺ঳ઠ঄Ljג极ࣅ] 1-13C]pyruvate ׯၟܔළ၇ҵ࣒ኁࣅଐࢫ႐ሤ۾Ⴀၳᆌڦ代谢ॠ֪๟௺ڦߌLj੗ዘްࢅڦఱ๴ଆ

ăڦࡻ

ࣅଐ႐ሤ۾Ⴀᆅഐ႐क໦ฅራ೺ೠࠚǗࣅଐᆅഐڦ႐ሤ۾Ⴀऐ዆研৯ă

ג极ࣅ]1-13C]pyruvateׯۨၟ࿋DŽADžĂ13C MRS

೷DŽBDžतҮி໎ዎଐമࢫ代谢ׂ࿿ۨଉă[

13C]

༐໗ൠჸ၎Բᇀጺ13C႑ࡽԲኵሞዎଐമࢫᆶ၂

ዸইگă

10ఁႴҮி໎ᄱ࿿ዎଐڦළ၇ҵ࣒ኁǖࣅଐമ

ၟׯ႐ሤ代谢ࢫ