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where T1 was assumed to be equal for all 13C metabolites.
The reverse conversion rate kLP was assumed to be 0 in the
model because it is much lower compared to the forward
reaction in physiological conditions, and such assumption
improves the stability of fitting computations.19,20
3 | RESULTS
3.1 | Preclinical studies
The acquisition was delayed by 15 s from the beginning of
injection. Immediately following the arrival of HP-pyruvate
in TRAMP tumor, lactate dehydrogenase (LDH) rapidly catalyzed the reduction reaction to HP-lactate. This reflected the
upregulated LDH gene expression/activity in cancer as found
previously.1 Pyruvate signal displayed its maximum near the
beginning of the acquisition and then decreased because of
the metabolic conversion, the RF excitation pulses, and the
T1 relaxation. The lactate increased at the beginning of the
sequence because of the rapid pyruvate-to-lactate conversion,
reaching maximum at approximately t 5 14 6 4 s. The
decreasing lobe at the latter half of the lactate curve indicated
the timing where the combined loss from progressively
increasing flip angle and T1 relaxation exceeded the contribution from pyruvate conversion. Alanine was converted to a
much lower degree from pyruvate as a key step in gluconeogenesis pathway, which is governed by the alanine transaminase. The alanine time curve approached maximum at
t 5 20 6 6 s. The amount of alanine production was only a
fraction of lactate in the TRAMP tumor, whereas higher alanine level can be seen in the liver of both cancerous and
healthy animals.13
FIGURE 6 Prostate cancer patient 3D dynamic CS-EPSI data with volumetric coverage from base to apex of HP pyruvate and its conversion to lactate (signal summed through time is shown in the overlays). Spatial resolution 5 0.5 cm3
, temporal 5 2 s, 18 time points, starting 5 s after injection of HP
(37%) [1-13C]pyruvate. Region of high lactate conversion correlated with the bilateral biopsy-confirmed cancer.
FIGURE 7 (A) 18 timepoints for HP 13C-pyruvate from a single slice with bilateral biopsy-confirmed prostate cancer. The acquisition began !5 s
after injection. HP-13C pyruvate appears in the prostate at !10 s into the dynamic 3D CS-EPSI acquisition. This data demonstrates the feasibility of acquiring dynamically in three dimensions that covered the entire prostate with 2 s temporal resolution. (B) Temporal dynamics of 13C-lactate from the same data
and slice as in (A). Conversion to lactate in the bilateral cancer regions was observed at !20 s.
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To investigate the feasibility of imaging larger FOVs in
vivo, we applied the 3D CS-EPSI sequence on 3 healthy
rats. Rapid pyruvate perfusion/uptake into the kidney was
observed since the beginning of the sequence, namely at
around !t 5 3 s post-injection. Appreciable amount of lactate exchange was detected as well (Figure 3). Lactate and
alanine production was also found in rat liver (data not
shown). These outcomes were highly consistent with the
previous observations on HP-13C rat studies.21,22
From TRAMP studies (Figure 4), the mean SNR,
summed across time, of total carbon was 69.2 6 28.4 for
tumor, 115.5 6 45.8 for vena cava, and 135.5 6 56.2 for kidneys with !25–35% polarization on dissolution. In the rat
scans (Figure 5), both FOV and voxel size were doubled
from mice. High SNR was found in both rat kidney (172.5 6
100.3) and liver (85.7 6 50.1). Such SNR was adequate for
both direct data visualization and dynamic modeling of metabolic interconversion. The mean size of TRAMP tumors,
2.2 cm3
, was approximately equivalent to 37 voxels (voxel
size 5 0.059 cm3
).
3.2 | Clinical phantom studies
Phantom studies were conducted using clinical setup,
sequence, and coils. The signal pattern on the built-in urea
phantom was found to be of higher homogeneity compared
FIGURE 8 The biopsy-proven Gleason 4 1 3 tumor in the patient’s right lateral midgland (red arrow) exhibited high lactate conversion following
HP pyruvate injection. (A) T2-FSE image showing the tumor voxel selected for the dynamic spectral plot in (B). Also shown is the ADC map where the
tumor region has substantially reduced ADC. (C) Dynamic curves (corrected for variable flip angle) are shown with far higher conversion to lactate in cancer compared to normal appearing regions. (D) Representative spectra for these regions at t 5 36 s. (E) Pyruvate-to-lactate conversion rate kPL parameter
map overlays showed high kPL on the opposite side (yellow arrows) as well, which was also confirmed as Gleason 4 1 3 prostate cancer by post-surgical
histopathology.
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to the ethylene glycol phantoms (Figure 3). Such signal profile probably resulted from the reduced sensitivity in regions
further away from the endorectal coil. The phantom data was
acquired with our 3D CS-EPSI sequence in tandem with the
new spectral–spatial RF pulses with reduced peak power and
duration. On both pyruvate and lactate bands, the phantom
dynamics showed good fitting agreement with the simulated
signal profile (Figure 2B). The apparent SNR of ethylene
glycol phantom was 16.1, and of urea phantom was 354 at
the final time point, where the 13C compounds were individually excited by the lactate excitation band with a mean flip
angle !40 8.
3.3 | Patient data
The subject of the human study testing the feasibility of the
new 3D CS-EPSI methods was a 66-year-old male patient
with biopsy-confirmed prostate cancer of stage T2c, with
PSA level of 6 ng/mL and PI-RADS score of 4/5. At radical
prostatectomy (RP), bilateral Gleason 4 1 3 was found at the
midgland of the prostate. Figure 6 depicts the pyruvate and
lactate area under curve (AUC) overlays in the prostate
region overlaid on T2-weighted reference scan. It demonstrated full gland coverage of this new sequence, from apex
to base, with a spatial resolution of 8 3 8 3 8 mm isotropic
(volumetric 5 0.5 cm3
). Whereas pyruvate intensities encompass the prostate gland as well as some surrounding vasculatures, regions of elevated conversion to lactate correlated
with cancer in the bilateral pathology data of this patient.
Figure 7 illustrates the temporal dynamics in a single
slice containing bilateral prostate cancer confirmed at RP.
HP-13C pyruvate is seen to perfuse into the vasculature surrounding the prostate, and the bolus entered prostate !t 5 10
s into the dynamic 3D acquisition. Rapid conversion to lactate in cancerous regions was observed to occur at !t 5 20 s.
The biopsy-proven Gleason 4 1 3 tumor in the right
peripheral zone exhibited more than 4-fold higher pyruvateto-lactate conversion (kPL) compared to normal-appearing
regions, reflecting high LDH enzymatic activity. The tumor
size was !1.5 cm3
, or 3 voxels (voxel size 5 0.5 cm3).
Figures 8C and 8D showed the HP-13C spectra and dynamics
in a representative voxel of this tumor versus a normalappearing region voxel. The dynamic curves were corrected
for progressive flip angles to show the estimated HP magnetization for kinetic modeling visualization purposes. The raw
signal of both pyruvate and lactate appears to monotonically
increase until the end of acquisition (Figure 8B) because of
the progressively increasing flip angles, whereas the corrected signal (i.e. the HP magnetization) shows the pyruvate
maximizing near !t 5 20 s post-injection and that of lactate
!30 s and decreasing toward the end (Figure 8C). The tumor
region also corresponds to darker region in T2-weighted FSE
image (Figure 8A, top, as encircled by the red box), and high
intensity in high b-value ADC maps (Figure 8A, bottom),
both of which exhibited good consistency with biopsy and
HP 13C findings. The bilateral midgland Gleason 4 1 3 cancer found at radical prostatectomy was also consistent with
the kPL map in Figure 8E, where the tumor in the right was
larger than the one in the left.
In the right midgland cancer, the apparent SNR calculated for pyruvate was 104, and for lactate, it was 10.7 at the
last time point. The mean SNR over the acquisition for pyruvate was 45.2 and 6.1 for lactate. The mean SNR over all
time points for total carbon was 51.3. In normal-appearing
regions, the mean SNR of total carbon in this patient was
48.2, comparable to the tumor region.
4 | DISCUSSION
Because HP 13C MR encodes chemical as well as spatial
information, this new molecular imaging technique allows
the simultaneous detection of multiple biologic compounds
and metabolic products with sensitivity enhancements of
>10,000-fold.23 This technique therefore presents the fields
of oncology and medical imaging with an opportunity to
improve our ability to investigate human disease and to ultimately translate these techniques into the clinic for more
individualized patient care.
The translation from animal to clinical HP-13C imaging
faces the challenges of larger imaging volume, reduced peak
RF power, and higher B1 inhomogeneity. To address these
challenges, specialized sequence modifications were developed including a low-power spectral spatial RF excitation,
“FID” acquisition mode and associated reconstruction methods, 3D coverage of the entire prostate with 0.5 cm3 spatial
and 2-s temporal resolution for prostate cancer patient imaging. This study was designed to determine and test the
sequence properties and signal behavior transitioning from
mouse studies to rats and, then to a human subject, with the
intent to optimize the performance and robustness of this
new 3D dynamic acquisition approach and then determine its
feasibility for imaging patients.
One major sequence modification made for the translation to human imaging was in the RF pulse design. The new
spectral-spatial RF pulse provide 67% savings in peak B1 by
means of relaxing the constraints on urea flip angle, which
accounts for the reduced peak RF power from clamshell
transmit coils in clinical setup compared to preclinical settings. The designed peak B1 was chosen to be !60% the
nominal maximum allowable power for the transmit coil to
provide sufficient headroom in transmit power allow for
varying coil loading when scanning different patients. The
1 ppm (!30 Hz) passband for each metabolite in this spectral–spatial pulse was reasonably wide to account for offresonance, which is typically <0.2 ppm (!6 Hz) for TRAMP
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mice and <0.5 ppm (!15 Hz) for human prostate. The timebandwidth of 5 provides good compromise between pulse
duration, peak power, and transition profile sharpness (Figure
2A). The reduction in RF pulse width (8.9 to 6.3 ms) shortened the echo time, which may slightly improve SNR given
limited T2 and T"
2. The phantom signal curve agrees well
with the simulated signal profile, indicating that the excitation pulses can be confidently generated with the clinical coil
configuration. Removing the DSE refocusing pulses also
mitigated the issue of limited peak RF power. The mean
SNR were comparable in TRAMP prostate tumor in the
DSE-enabled sequence from prior studies and the new
sequence in this study (N 5 8, SNRDSE 5 61.6 6 43.6,
SNRFID 5 69.2 6 28.4, P > 0.3).
Imaging a larger subject using the 3D dynamic CS-EPSI
acquisition protocol provided a way to investigate the
sequence parameters and signal behavior because of increasing FOV. The rat scans in this study use a dual-tuned rat coil
that was !5 times the volume of mouse coil. The FOV and
voxel size were doubled both in the phase encode and EPSI
readout direction (in-plane resolution: mouse 5 3.3 mm,
rat 5 6.7 mm; axial resolution: mouse 5 5.4 mm, rat 5
10.8 mm), giving 4 3 voxel volume. The total injection dose
also increased by !6-fold. However, rat received lower
HP-13C dose per unit weight (!60% that of TRAMP). In
addition, the larger coil volumes inherently lead to decreased
sensitivity compared to mouse setup. The other sequence
parameters (e.g., flip angles, temporal resolution, and undersampling ratio) remained the same. Substantial pyruvate and
lactate were observed in rat kidneys just like in TRAMP
mice tumors. The SNR did not deteriorate in rat relative to
mouse (Figures 4 and 5). As such, transitioning to rat scans
revealed the key elements to scaling up the sequence, and
this protocol showed robustness in signal and image quality
with larger imaging subjects and coils.
A 2-s temporal resolution was chosen for human protocol
identical to that in TRAMP studies. In TRAMP scans, this
consistently provided >30 (apparent) SNR for both pyruvate
and lactate (!25–35% polarization), which translated into
<3% error in quantitative metabolism models for kPL evaluation based on simulations. For the clinical study, the pyruvate
SNR was similar to TRAMP (!30), while lactate SNR was
relatively lower (!6). Two primary sources of uncertainty
contribute to the apparent SNR of this 3D CS-EPSI acquisition—the data noise and CS reconstruction errors. Referring
to Figure 3 of the paper by Larson et al.,15 the reconstruction
error was <0.001 for SNR of 45 and <0.007 for SNR of 6.
Therefore, it can be concluded that the data noise was dominant source of error under both clinical and mouse scheme.
SNR improvement is theoretically possible through a longer
temporal resolution, because it results in effective signal
averaging and decrease of undersampling ratio. Nevertheless,
a longer temporal resolution can create temporal blurring and
ambiguity on timing of dynamic curve, which can negatively
impact quantitative modeling.
In the patient research, the acquisition began at t 5 5 s
post-injection, compared to the t 5 0 s in mouse studies,
whereas both shared the same 2-s temporal resolution. The
acquisition time window covered both bolus dynamics and
pyruvate-to-lactate conversion in human prostate cancer,
whereas the TRAMP scan focused more on the latter half of
the pharmacokinetics that mainly reflected pyruvate metabolism. A main reason for the 5-s delay in human studies was
to prevent hyperpolarized magnetization in the intravenous
tubing and arm being excited by the clamshell transmit coils.
Because at the end of the injection, the bolus could still be
traversing through the antecubital vein, which is typically
located inside the “hot” zones of the transmitter, the delay
allows hyperpolarized bolus to perfuse into tumor region
before RF excitation.
In clinical hyperpolarized 13C imaging, because of the
absence of arterial coverage or perfusion markers, it is more
challenging to account for pharmacokinetic parameters such
as circulation and AIF. In contrast, because the bolus delivery was more rapid in small animals (e.g., TRAMP mice),
and a reference AIF was relatively easy to estimate using the
HP 13C urea through arterial voxels according to a preclinical
co-polarized imaging study of TRAMP tumor,1 the sequence
can be configured to put more focus on net metabolism by
acquiring at a longer (15 s) delayed window.1,24 Additionally, the circulation and bolus delivery is generally slower in
human versus small animals. Therefore, carefully selecting
an acquisition window that both accounts for pyruvate infusion and pyruvate–lactate conversion benefits the clinical
quantitation of prostate tumor metabolism.
Prostate cancer is a major health concern in the United
States with >160,000 new cases per year and >26,000
deaths.25 Because of increased screening using serum prostate specific antigen (PSA) and extended-template transrectal
ultrasound (TRUS) guided biopsies, patients with prostate
cancer are being identified at an earlier and potentially more
treatable stage. Unfortunately, the aggressiveness of individual tumors cannot be predicted with great confidence in individual patients using currently available clinical and imaging
prognostic data.26–30 Preliminary data strongly indicate that
hyperpolarized 13C-pyruvate MRI using DNP has the potential to dramatically improve prostate cancer clinical management. In transgenic prostate cancer mouse models, this
method demonstrated the unprecedented ability to separate
early stage (low-grade) tumors from late stage (high-grade)
cancer based on this metabolic parameter (conversion
through the LDH-catalyzed pyruvate metabolism).2 Higher
grade prostate cancers, both in transgenic models and human
biopsies, have demonstrated several fold increases in LDH
expression.2,31 No other imaging method has demonstrated
this ability to differentiate low grade, clinically insignificant
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prostate cancers that are the majority of cases from highgrade disease that kills >26,000 Americans per year. The
Phase 1 clinical trial of HP 13C-pyruvate in prostate cancer
patients demonstrated feasibility and safety through this first
human study of hyperpolarized MR metabolic imaging12
using single-slice 1D dynamic, 2D dynamic, and 3D single
time point acquisitions. This clinical trial indicated the potential to characterize the extent and aggressiveness of prostate
cancer in individual subjects to ultimately benefit clinical
treatment decisions and to monitor treatment response. However, the acquisition methods used in that trial did not provide simultaneously the required spatial and dynamic
temporal resolution necessary for optimal HP 13C MR clinical research studies. Development and translation of 3D
dynamic HP 13C MRSI offers a new method to quantitatively
analyze metabolism in human prostate cancer throughout the
gland. In this study, the human 3D MRSI acquisition demonstrated the ability to obtain dynamic information on the conversion of [1-13C]pyruvate to [1-13C]lactate that is catalyzed
by lactate dehydrogenase (LDH) that is upregulated in prostate cancer. Adequate SNR and temporal resolution enabled
the calculation of kPL maps with a spatial resolution of
0.5 cm3
. This supports the use of this 3D acquisition
approach in future studies to investigate in sufficiently large
patient populations prostate cancer aggressiveness and
response to therapy. Importantly, the capability to image biochemical processes and visualize diseases with high spatiotemporal resolution opens the door to many potential HP 13C
translational and research applications. For instance, high
lactate/pyruvate ratios were detected in various cancer types
such as xenografts of human brain tumor,32 renal carcinoma
cells,33 and breast tumor xenografts.7 Modulation of lactate
production was found in tumors subjected to chemo34 and
targeted therapy.35 Besides cancer, HP 13C imaging has been
used to study kidney urea transporters,36 diabetes and gluconeogenesis,37 cardiac diseases,38 and neurodevelopment.39
5 | CONCLUSIONS
This new 3D dynamic MRSI acquisition method incorporating new spectral–spatial RF pulses, “FID” readout, and
modified CS reconstruction addressed the challenges of
larger imaging volumes and reduced available peak RF
power required for human studies. Scalability in acquisition,
reconstruction, and quantitation methods was demonstrated
by the satisfactory image quality, SNR, and apparent kinetic
rate constants between cancer–normal during the transition
from mice to human patient studies. The results demonstrate
the feasibility to characterize prostate cancer metabolism in
the clinical setting using this new 3D dynamic HP MR technique to quantify and image the kinetic rate constant, kPL, of
the conversion of [1-13C]pyruvate to [1-13C]lactate that has
been shown to be increased in prostate cancer.1,2,40,41
ACKNOWLEDGMENTS
We would like to thank Dr. Renuka Sriram for the helpful
discussions.
ORCID
Peder E.Z. Larson http://orcid.org/0000-0003-4183-3634
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SUPPORTING INFORMATION
Additional Supporting Information may be found in the
online version of this article.
How to cite this article: Chen H-Y, Larson PEZ, Gordon JW, et al. Technique development of 3D dynamic
CS-EPSI for hyperpolarized 13C pyruvate MR molecular imaging of human prostate cancer. Magn Reson
Med. 2018;00:1–11. https://doi.org/10.1002/mrm.
27179
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14
8.1 Supplementary Figures and Tables
Label # Region Label # Region
21 L superior frontal gyrus 65 L inferior occipital gyrus
22 R superior frontal gyrus 66 R inferior occipital gyrus
23 L middle frontal gyrus 67 L cuneus
24 R middle frontal gyrus 68 R cuneus
25 L inferior frontal gyrus 81 L superior temporal gyrus
26 R inferior frontal gyrus 82 R superior temporal gyrus
27 L precentral gyrus 83 L middle temporal gyrus
28 R precentral gyrus 84 R middle temporal gyrus
29 L middle orbitofrontal gyrus 85 L inferior temporal gyrus
30 R middle orbitofrontal gyrus 86 R inferior temporal gyrus
31 L lateral orbitofrontal gyrus 87 L parahippocampal gyrus
32 R lateral orbitofrontal gyrus 88 R parahippocampal gyrus
33 L gyrus rectus 89 L lingual gyrus
34 R gyrus rectus 90 R lingual gyrus
41 L postcentral gyrus 91 L fusiform gyrus
42 R postcentral gyrus 92 R fusiform gyrus
43 L superior parietal gyrus 101 L insular cortex
44 R superior parietal gyrus 102 R insular cortex
45 L supramarginal gyrus 121 L cingulate gyrus
46 R supramarginal gyrus 122 R cingulate gyrus
47 L angular gyrus 161 L caudate
48 R angular gyrus 162 R caudate
49 L precuneus 163 L putamen
50 R precuneus 164 R putamen
61 L superior occipital gyrus 165 L hippocampus
62 R superior occipital gyrus 166 R hippocampus
63 L middle occipital gyrus 181 Cerebellum
64 R middle occipital gyrus 182 Brainstem
Table 1: (Supplementary) The numerical labels used in the LPBA40 atlas and the corresponding brain regions.
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15
A
B
Figure 5: (Supplementary) (A) Average (± SD) of segmented LPBA40 regional volumes from
14 subjects. Red line indicates isotropic 1.5 cm voxel volume. Regions that were smaller
than the size of a single voxel were: L/R caudate, L/R putamen, L/R gyrus rectus, L/R
hippocampus. (B) Volume z -scores plotted vs. the LPBA40 atlas region labels, each colour
showing a different subject.
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A
B
C
-4 -3 -2 -1 0123
-2
-1
0
1
2
3
4
5
6
Pyruvate z-score
Lactate z-score
-4 -3 -2 -1 0123
-3
-2
-1
0
1
2
3
4
Bicarbonate z-score
Lactate z-score
D
Figure 6: (Supplementary) Metabolite signal ratios vs. LPBA40 atlas region number
(N=14). (a) lactate-to-pyruvate and (b) lactate-to-bicarbonate ratios, calculated by computing the area-under-the-curve from the timecourse for each metabolite and taking the
ratio between these areas. Each colour represents a different subject. (c) For reference, the
pyruvate z -scores are plotted vs. the LPBA 40 regions, showing a consistent pattern across
subjects which is different from the lactate and bicarbonate patterns. (d) Scatter plots for
regional pyruvate z-scores vs. lactate z-scores (left) and bicarbonate z-scores vs. lactate
z-scores for all subjects.
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0 50 0
10
20
30
40
50
60
time [s]
signa [a.u.]
Subject 4
AUCR =0.37
0 50 0
10
20
30
40
50
60
time [s]
Subject 6
AUCR =0.22
0 50 0
10
20
30
40
50
60
70
80
90
time [s]
Subject 7
AUCR =0.26
0 50 0
5
10
15
20
25
time [s]
Subject 8
AUCR =0.30
0 50 100 0
20
40
60
80
100
120
140
160
time [s]
Subject 9
AUCR =0.18
lactate
pyruvate
a
b
Figure 7: (Supplementary) Venous drainage signal measured in the right jugular. (a) Representative region-of-interest (ROI) drawn in the right jugular of subject #7, with 13C-lactate
signal shown in the colour overlay. The 13C-pyruvate and 13C-lactate signal vs. time in the
right jugular ROI for a subset of the subjects (N=5), with lactate-to-pyruvate area-underthe-curve ratios (AUCR) for each subject.
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7 Author Contributions
CYL, BJG, APC, CHC developed the data acquisition methods, CYL and RE operated the
MRI scanner, CYL and BJG did the image reconstruction and image analysis, HS and KAC
assessed the subjects and performed the 13C-pyruvate injections, RE loaded the injector,
WJP performed compounding and pharmacy release of the 13C-pyruvate doses, APC and
CHC built the 13C head coil, CH and SEB interpreted the images. All authors critically
reviewed the manuscript.
8 Acknowledgements
Funding support from the Canadian Cancer Society grant 705246 and Canadian Institutes
of Health Research grant PJT-152928.
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