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Abstract— Kinetic modeling of the in vivo pyruvate-to-lactate
conversion is crucial to investigating aberrant cancer metabolism
that demonstrates Warburg effect modifications. Non-invasive
detection of alterations to metabolic flux might offer prognostic
value and improve the monitoring of response to treatment. In
this clinical research project, hyperpolarized [1-
13C] pyruvate
was intravenously injected in 10 brain tumor patients to measure
its rate of conversion to lactate (kPL) and bicarbonate (kPB) via
echo-planar imaging. Our aim was to investigate new methods to
provide kPL and kPB maps with whole-brain coverage. The
approach was data-driven and addressed two main issues:
selecting the optimal model for fitting our data and determining
an appropriate goodness-of-fit metric. The statistical analysis
suggested that an input-less model had the best agreement with
the data. It was also found that selecting voxels based on postfitting error criteria provided improved precision and wider
spatial coverage compared to using signal-to-noise cutoffs alone.
Index Terms— Brain Cancer, Dissolution Dynamic Nuclear
Polarization, Hyperpolarized MRI, Kinetic Modeling, kPL, kPB,
Metabolic Imaging.
I. INTRODUCTION
issolution Dynamic Nuclear Polarization (dDNP)1,2 is a
powerful technique that enhances nuclear polarization by
up to 5 orders of magnitude.
Its chemical3–5
, biological6–9, physical10–14, pre-clinical15–21
and clinical22–28 potentials have been investigated in the last
two decades. The use of hyperpolarized (HP) substrates has
provided important insights into cancer metabolism6,22,29–31
and cardiac imaging23,32–36. Thus far, HP pyruvate is the most
common substrate for in-vivo applications of dDNP because of
both its relatively long T1(
13C), high biological relevance, and
rapid uptake and conversion.
The first phase I clinical trial of HP [1-
13C]pyruvate was
reported in 201322 on patients with prostate cancer and it
1
Dep. of Radiology and Biomedical Imaging, University of California San
Francisco, San Francisco, CA, USA. 2
Dep. of Neurological Surgery, University of California San Francisco,
San Francisco, CA, USA.
We thank the funding sources: NICO and NIH grants P01CA118816,
R01EB017449 and P41EB013598. As for the first author, the views expressed
are purely those of the writer and may not in any circumstances be regarded
as stating an official position of the European Commission.
Corresponding author: daniele.mammoli@uscf.edu
demonstrated feasibility and safety for this approach to
monitoring cancer progression and treatment response by
quantifying the Warburg effect37 that is expressed through
increased lactate dehydrogenase (LDH) activity. Several
institutions around the globe are now performing clinical
cancer research of HP pyruvate in prostate, breast38, liver39
and brain28, in order to investigate the clinical value of dDNP
HP MRI and its advantages over current molecular imaging
methods including Fludeoxyglucose Positron Emission
Tomography (FDG-PET)40,41.
FDG-PET is an imaging technique used for the diagnosis of
cancer based on the uptake and trapping of the radioactive
substrate, but it does not track its kinetic conversion into
downstream metabolites. Furthermore, it is of limited use in
brain tumors since high glucose uptake is observed not only in
cancerous tissues, but also in healthy cortical gray matter42,43,
confounding the discrimination of brain tumors. There is an
unmet clinical need for a more precise diagnosis of brain
tumors which might be overcome by the capability of dDNP
to track the pyruvate-to-lactate conversion and the Warburg
effect, provided that a robust and reproducible kinetic
modeling of the conversion of pyruvate is achieved44–51.
The goal of this work is to develop, implement and test
methods for calculating precise kPL and kPB maps describing
the enzymatic conversion of [1-
13C]pyruvate to lactate and
bicarbonate in the human brain. We acquired 22 datasets
though a dynamic 2D multislice Echo Planar Imaging (EPI)52
sequence. Dynamic acquisition of MR images has the
advantage to provide robust quantification of kinetic
processes53, regardless of differences in bolus delivery, which
influences, for instance, area-under-curve (AUC) ratios. EPI
acquisition of HP substrates offers improved temporal
resolution and greater spatial coverage than Echo Planar
Spectroscopic Imaging (EPSI) techniques. We present and
compare several models and two approaches to cut off voxels
with improper fitting, in order to provide precise and spatiallyresolved kinetic maps.
Kinetic Modeling of Hyperpolarized Carbon-13
Pyruvate Metabolism in the Human Brain
Daniele Mammoli1, Jeremy Gordon1, Adam Autry1, Peder E. Z. Larson1, Yan Li1, Hsin-Yu Chen1,
Brian Chung1, Peter Shin1, Mark Van Criekinge1, Lucas Carvajal1, James B. Slater1, Robert Bok1,
Jason Crane1, Duan Xu1, Susan Chang2 and Daniel B. Vigneron1.
D
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II. METHODS
Sample preparation
The sample preparation was carried out in collaboration with
UCSF Radiopharmacy. Each sample consisted of a mixture of
Good Manufacturing Practices (GMP) [1-
13C]pyruvic acid
(14.2 M; MilliporeSigma Isotec) and 15 mM Electron
Paramagnetic Agent (EPA), (AH111501, GE Healthcare).
Samples were prepared the same morning of each study and
they were inserted, under aseptic conditions, into pharmacy
kits from GE Healthcare.
Polarization
A SPINlab54 polarizer (GE Healthcare) was used to polarize
[1-
13C]pyruvate at 5 T and about 0.8 K. The sample was
irradiated with microwave at fMW = 140.02 GHz for at least 2
hours, boosting the 13C polarization up to 40% in the liquid
state back-calculated to the time of dissolution.
Pre-injection quality check
After the dissolution, the EPA was filtered mechanically and
quality control was carried out prior to injection: pH, pyruvate
and EPA concentrations, polarization, and temperature were
measured and compared to ranges of acceptability.
Contemporarily, the integrity of the 0.2 μm sterile filter was
tested following manufacturer specifications on its resistance
to a pressure-stress of 50 psi. Following the release by the
pharmacist, ~250 mM pyruvate was injected at a dose of 0.43
mL/kg and a rate of 5 mL/s, followed by a 20 mL saline flush.
A pause of 5 s preceded the acquisition.
Acquisition
All experiments were performed on a 3T clinical MR scanner
(MR750, GE Healthcare) with multi-nuclear excitation and
32-channel reception capability. Carbon-13 RF excitation was
achieved with a volume transmit coil and detected with
custom 13C RF coils with either 8 or 32 channels55.
Images of pyruvate, lactate and bicarbonate were acquired
through a 2D EPI dynamic sequence53, capable of exciting
multiple 13C frequencies sequentially, and having the
following characteristics: 8 slices, 1.5 or 2 cm thick axial slice,
TR = 62.5 ms, TE = 21.9 ms, 20 time-points with a 3 s
temporal resolution, in-plane resolutions of 1.2 x 1.2, 1.5 x 1.5
or 2 x 2 cm2
. In order to improve image quality, we applied
noise pre-whitening56 in k-space prior to sum-of-squares coil
combination.
In Fig. 1 we show the temporal trends of the signals of
pyruvate, lactate and bicarbonate in a single slice.
III. KINETIC MODELING
In the last decade, substantial efforts have been dedicated to
developing kinetic models of MRI signals of HP metabolites
in vivo. Despite several approaches available in literature 14,25–
30, no gold-standard is available because strategies may vary
according to specific experimental conditions.
Since kinetic modelling might suffer from errors induced by
several factors (e.g., variable bolus delivery, acquisition
sequence, magnitude images with non-zero mean noise etc),
discarding voxels with poor fitting is critical to provide precise
kinetic maps.
In this section, we describe kinetic models and fitting criteria
to address these issues and to quantify kinetic rate constants in
a precise way.
First, suitable models for fitting our data are presented.
Then, we describe two criteria to decide how to discard voxels
with suboptimal fitting.
Models
While studying the human brain, in order to obtain insights
into complicated metabolic pathways, it is crucial to
investigate the kinetics of several metabolites in different
cellular environments. Although it is an oversimplification, we
consider a three-site system (i.e., pyruvate, lactate and
bicarbonate) in a single compartment. In such a system, the
magnetization ^n)o is exchanged and relaxes according to the
following equations (single macrons refer to vectors, double
macrons to matrixes):
@
@L ^n)o = _ ? ^n)o ` ]n)o [1]
_ = h
a 0a&98a&95 &89 &59
&98 a 0 a &89 0
&95 0 a 0 a &59
k [2]
^n)o = r
:;<n)o
=>?n)o
>C?n)o
s [3]
]n)o = r
n)o
n)o ? "=>?
n)o ? ">C? s *%#(# n)o = , ? #PL ? )Q [4]
We defined:
&98 and &95 as the rates of labelling exchange from
pyruvate to lactate and to bicarbonate (respectively).
Note they are first order apparent rate constants and
not absolute flux measurements.
R1 = 1/T1 as spin longitudinal relaxation rate.
Figure 1. Temporal dynamics of the signals of pyruvate, lactate and bicarbonate in a single slice (data set 3 in Table 1), acquired with the EPI dynamic
sequence (see text for details). For anatomic reference, a 1H FLAIR image on the left. Note that data have not been coil corrected and reflect the sensitivity
profile of the 8-channel receive-array used in this study.
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6<9
:;< n)o as the experimental temporal trend of the
magnetization of pyruvate.
^6<9n0o as the experimental magnetization vector
^n)o at the first time point.
^6<9no as the experimental magnetization vector
^n)o at the last time point.
Overall, 5 approaches were tested:
A) n)o = 0, &89 = &59 = 0, :;<n)o = 6<9
:;< n)o,
^n0o = ^6<9n0o, ^no = ^6<9no.
This is a model with no input-function57: the fitted
signal of pyruvate is bounded to the experimentally
acquired signal. The first and last time points are
fixed to the experimental values (for all metabolites).
Just forward reactions are considered.
Fitted parameters: 0, &98, &95.
B) Same as A) except for having now &89 d 0 (back
reaction now allowed with a different rate). Fitted
parameters: 0, &98, &95,&89.
C) Same as A) except for having now ^;no =
3
BCL [1 1 1] (last time points are now estimated
from the fitting). Fitted parameters:
0, &98, &95,3
BCL .
D) Same as A) except for having now ^;no =
3
BCL [1 1 1] and ^;n0o =
/
BCL [1 1 1] (both first and last time points are
estimated from the fitting). Fitted parameters:
0, &98, &95,3
BCL , /BCL .
E) &89 = &59 = 0, ^n0o = ^6<9n0o, ^no =
^6<9no. In this model n)o d 0, meaning that there
is an input function n)o (defined in Eq. 4) simulating
the arrival of the bolus in the region of interest. The
magnetization of pyruvate is also fitted. First and last
points are set equal to experimental values. Just
forward reactions are considered. Fitted parameters:
0, &98, &95,,, -, ., "=>?, ">C?.
The differential equations 1-4 were solved through a discrete
model. The transverse magnetization, ^Z;, and the residual
longitudinal component along the z axis, ^<[K, were computed
in Eq 5 and 6 at each time step ) b (where ) = TR and
N = 1, 2, …, 20), taking into account the excitation flip angles
,] for each metabolite.
The magnetization at time step N+1 was calculated in Eq. 7 by
considering the exchange matrix _, ) and ^<[Kn) b o.
^<[Kn) b o = ^n) b o b
n,]o [5]
^Z;n) b o = ^n) b o b
n,]o [6]
^n) b [ ` 1]o = #7_bL^<[Kn) b o [7]
All models were solved via custom MATLAB routines
involving minimization of non-linear least-square problems
(i.e., difference between fitted and experimental
magnetization). skPL is half the 95% confidence interval,
calculated with MATLAB functions lsqnonlin and nlparci. In
all cases, boundary conditions were: 1/80 s-1 < R1 < 1 s-1 and
0 < kPL < 1 s-1
.
Experimental T1s of lactate and pyruvate vary significantly in
solution. However, a recent work53 has shown that the inputless model is insensitive to errors in pyruvate T1 and that T1
values for pyruvate and lactate are similar in vivo (respectively
30 and 25 s, in patient studies at 3 T). We, therefore, chose to
have equal T1 values to improve the stability of the model.
After fitting to experimental data, models were classified
according to 3 indicators, Akaike Information Criterion (AIC),
ghZ
:[<? and &]
98
:[<?, defined as:
= 2& a 2 no [8]
ghZ
:[<? = 100 GZW[
T
GZW[
S [9]
&]
98
:[<? = 100 g1 a l
&]98
C
&]98
e 4 mj [10]
where k is the numbers of parameters estimated in the model,
L is the maximum value of the likelihood function for the
model (i.e., chi squared in our case), ghZ
C is the number of
voxels, in all data sets, that are fitted according to the i
th model
(with i = A-E), ghZ
4 is the ghZC corresponding to model A,
&]98
C is the mean kPL, averaged over all data sets, for the i
th
model and &]98
4 is the &]98C corresponding to model A.
The AIC is a measure of the statistical significance of several
models with a different number of parameters to be estimated
(i.e., the lower the better statistical significance for the model).
ghZ
:[<? is informative of how many voxels are fitted if
compared to model A (chosen arbitrarily as a standard) and
&]
98
:[<? is a measure of how much kPL varies with models.
To assess the reproducibility of the pyruvate bolus delivery,
we define a “mean pyruvate time” )><<
{[>G as the center of mass
of the pyruvate signal over time on a voxel-wise basis53:
)><<
{[>G = :X\\Y V nGoLnGo
:X\\Y V nGo [11]
Table 1. Summary of the experimental conditions of the 22 human brain data
sets. We report coil used, voxel size, flip angles of excitation for each
metabolite, peak SNR (along all voxels and slices in the given data set), mean
kinetic rate kPL and its standard deviation (calculated with “model A” and the
error criterion, described later on in the manuscript) and mean and standard
deviation of the mean arrival time of pyruvate tarr.
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Table 2. Comparison of models A-E according to Akaike Information
Criterion (AIC) and percentage deviation from model A of mean kPL (&]
98
:[<?)
and of number total voxel fitted (ghZ
:[<?).
Criteria to mask voxels on kPL maps
In this paragraph, we introduce two approaches to mask
voxels with improper fitting in order to provide “reasonable”
kPL maps with an acceptable compromise between precision of
the fitting in a voxel and the numbers of voxel for which the
fitting itself is retained (i.e. the coverage). Due to low SNR,
kPB maps describing the conversion of pyruvate into
bicarbonate will not be considered in this analysis. In the
"SNR criterion", prior to fitting temporal trends to a model, we
create a mask discarding voxels with peak SNR lower than a
set of two SNR thresholds, one for pyruvate and one for
lactate, labeled as ?SNR:;<
{>Z, SNR=>?
{>Zü. In the "error criterion",
prior to fitting temporal trends to a model, we mask voxels
based on ?SNR:;<
{>Z, SNR=>?
{>Zü, as in the SNR criterion, but,
after fitting, we discard all the voxels where skPL is bigger
than a fraction of kPL itself.
In this way, the global masking procedure depends on both the
set of SNRs and on the statistical outcome of the fitting.
To compare the outcomes of fitting with the two criteria, we
will take into account ghZ
:[<? (as in Eq. 9 but now the index
refers to the criterion) and R2
, defined as:
1 = 1 a n;U2BUoR U
n;U2;]oR U
[11]
where yk and fk refer to the experimental and fitted signals at
the kth time point and +] is the average over time of yk. R2 is
preferred over AIC or chi squared since these last two vary
with different SNR (i.e., if data with lower SNR are filtered,
the residuals n+D a $Do1 are bigger).
Figure 2. [Top] 1H FLAIR and 13C total signal AUC images (data set 3) from
the raw data. [Bottom] Temporal dynamics of signals of pyruvate, lactate and
bicarbonate in the selected voxel.
IV. RESULTS AND DISCUSSION
The subject population comprised 10 patients with recurrent
glioma who had received prior surgery and were undergoing
standard-of-care treatment, including radiation therapy and
temozolomide chemotherapy, along with a variety of adjuvant
therapies. A subset of these patients was imaged serially, and
all received a single injection per day. Table 1 includes the
experimental conditions under which the 22 data sets were
acquired (i.e., coil, voxel size, excitation flip angles) and
estimation of SNRmax (the peak SNR in all voxels and slices in
the given data set), )><<
{[>G (mean arrival time of pyruvate) and
&98
{[>G (mean kPL rate).
In Fig. 2, we can see typical temporal trends of the signal-tonoise ratio of pyruvate, lactate and bicarbonate in a single
voxel, selected from a slice (also shown for all metabolites).
The SNR did not go to zero at later time-points due to the
sum-of-squares coil combination used in this work, which
results in non-zero mean noise caused by noise rectification of
magnitude data.
While there has been no comprehensive study on the impact of
spatial resolution on the measured reaction rate, larger voxels
would have a greater risk of partial-volume effects, reducing
the measured reaction rate because of the strong vascular
pyruvate signal58. Confounding factors such as Rician bias in
magnitude data and variations in bolus delivery between
patients are global factors that impact quantification. On a
voxelwise level, regional variations in perfusion, signal loss
from susceptibility/B0 inhomogeneities and reduced SNR in
voxels at the center of the brain, farthest from the
multichannel receive array, can also impact quantification.
The choice of flip angles will also have an impact on the
precision of rate constant estimates57,59,60. Using a variable
(across frequency) flip angle scheme, with a higher flip angle
for downstream metabolites and a lower flip angle for
pyruvate, is commonly used to compensate for the lower
metabolite concentration and to reduce saturation of pyruvate.
This leads to more precise fitting in the presence of noise by
increasing the metabolite SNR61. While variable (through
time) flip angle schemes can improve SNR, they are also more
sensitive to transmit B1 inhomogeneity59,62.
The SNRmax was excellent for pyruvate and lactate (i.e.,
greater than 100 and 13, respectively, for all datasets) but
greatly variable for bicarbonate.
The mean arrival time of the pyruvate bolus, )><<
{[>G, was
calculated for every data set via Eq. 11. While there is
variability throughout the brain due to differences in arrival
time between arterial and venous voxels, the mean pyruvate
time ranged from 6 s to 11 s post injection, with an average
value of 8 ± 2 s across patients. Compared to initial results
from the prostate63, the mean arrival time in the brain appears
fairly reproducible in our initial cohort.
Mean kPL and its standard deviation were also calculated on
each data set, with the approach described in the next
paragraphs.
Let us define skPL as half the 95% confidence interval from
non-linear least-square fitting to “model A” with the error
criterion with ?SNR:;<
{>Z, SNR=>?
{>Zü = [20, 5] and skPL < 0.6·kPL.
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We have applied the error criterion with ?SNR:;<
{>Z, SNR=>?
{>Zü
= [20, 5] and skPL < 0.6·kPL to compare models A to E. Values
of AIC, &]
98
:[<? and ghZ
:[<? are reported in Table 2: model A
was the best model at describing data (highest ghZ
:[<? and
lowest AIC) and model B the worst (high AIC and negligible
ghZ
:[<?). Model E, which is the only one including an input
function, performed worse than model A (the simplest
assumption-less model), due to a poor agreement between the
input function and our data which may be improved with an
earlier start of acquisition.
Parameters used in both the SNR and the error criterion
should be optimized in order to find a compromise between
precision and number of voxels fulfilling the statistical
requirements based on skPL (i.e., spatial coverage).
The analysis has been carried out on datasets with peak SNR
of lactate lower than 25, by using model A, since it is the one
providing the best fit quality.
In the SNR criterion, we have to choose ?SNR:;<
{>Z, SNR=>?
{>Zü,
namely the set of thresholds for masking voxels with low SNR
(before fitting). We tested 22 sets obtained by all the possible
combinations between SNR:;<
{>Z = [5, 10, 50, 100] and
SNR=>?
{>Z = [2, 3, , 5, , 7].
In the error criterion, the SNR mask was computed based on
6 threshold sets, obtained by combining SNR:;<
{>Z = 5 and
SNR=>?
{>Z = [2, 3, , 5, , 7]. After fitting, we discarded all the
voxels with skPL < c?kPL where c = [0.4, 0.6, 0.8, 1.0].
Table 3a. Values of ghZ
:[<? for the SNR criterion and the error criterion using
model A as standard. Several combinations of ! :;<
{>Z and ! =>?
{>Z were
used for both criteria. In the SNR criterion no conditions are set on skPL
(error of kPL), whereas, in the error criterion, fitting is accepted for skPL <
c·kPL and rejected otherwise.
Table 3b. Values of R2 for the SNR criterion and the error criterion using
model A as standard. Several combinations of SNRpyr and SNRlac were used
for both criteria. In the SNR criterion no conditions are set on skPL (error of
kPL), whereas, in the error criterion, fitting is accepted for skPL < c·kPL and
rejected otherwise.
Values of ghZ
:[<? and R2 are reported in Tables 3a and 3b
(respectively) for both the SNR criterion and the error
criterion. To produce kinetic maps, it is necessary to find a
compromise between statistical precision and available
number of voxels (or alternatively the voxel count within the
FOV) and the final decision may vary from one user to the
other.
Tables 3a and 3b provide insight on how to set limits in our
case. In Tab. 3a, ghZ
:[<? drops with higher SNR:;<
{>Z, higher
SNR=>?
{>Z and lower c, showing that any stricter requirement
diminishes the numbers of accepted voxels. For the SNR
criterion (Tab. 3b), R2 increases with higher SNR=>?
{>Z but not
necessarily with higher SNR:;<
{>Z. This suggests that the
precision of methods based on the SNR alone depends more
critically on lactate rather than on pyruvate. In Tab. 3b, as for
the error criterion, R2 increases with both higher SNR=>?
{>Z and
lower c, meaning that both parameters affect the statistical
precision.
In Tab. 3b, let us consider the R2 values corresponding to
SNR:;<
{>Z = 5 in both criteria (i.e., first column in the SNR
criterion and all columns in the error criterion). Values of R2
in case of SNR=>?
{>Z = 2, 3, in the SNR criterion are smaller
than any R2 in the error criterion (regardless of c). However,
R2 for SNR=>?
{>Z = 5, , 7 in the SNR criterion are comparable
to those in the error criterion. This means that at low SNRlac
the error criterion performs better than the SNR criterion,
while they are comparable at higher SNRlac.
By comparing Tab. 3a and 3b, we can see that, at similar
precisions, the error criterion gives, on average, more voxels
with proper fitting (i.e., higher ghZ
:[<?), yielding to a better
spatial coverage in kPL maps. The trend is particularly evident
at low SNR=>?
{>Z.
Figure 3. Temporal dynamics of the signal of lactate in a voxel (blue crosses)
and fitting (red line) using model A and the error criterion in 6 cases with
different values of the parameter c in the condition skPL < ckPL (see text for
details).
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For example, let us consider two cases with R2 = 0.43 in Tab.
3b: those with ?SNR:;<
{>Z, SNR=>?
{>Zü = [100, 2] for the SNR
criterion and ?SNR:;<
{>Z, SNR=>?
{>Zü = [5, 3] with skPL < 1.0?kPL
for the error criterion. These two cases exhibit the most
dramatic difference since their corresponding values of ghZ
:[<?
in Tab. 3a would be 6 and 33, meaning that the error criterion
provided 5 times more voxels despite having similar statistical
precision.
Furthermore, if one wanted to base the selection on the SNR
alone, removing the check on skPL, in order to choose precise
values, it would be necessary to perform many fittings at
different SNR thresholds in every dataset and to fix a
reasonable threshold.
The error criterion is an easier option since it is sufficient to
set SNR:;<
{>Z and SNR=>?
{>Z to reasonably low thresholds (high
enough to avoid to retain voxels with noisy trends) and leave
the main filtering task to the criterion based on skPL.
We investigated the performances of the error criterion when
applied to random noise.
To do so, we generated several sets of lactate signals
consisting of Gaussian noise and associated them to the
experimental signals of pyruvate from data set 3. Then, we
applied the error criterion with ?SNR:;<
{>Z, SNR=>?
{>Zü = [5, 3]
and skPL < 0.6?kPL and calculated the number of fitted voxels,
namely those where the fitting fulfils the requirements of the
error criterion. We found that, if compared to experimental
signals from data set 3, random noise provides, at best, 40
times less fitted voxels.
This suggests that the improvements introduced by the error
criterion on ghZ
:[<? and R2 in Tables 3a and 3b are barely
influenced by the noise.
All these findings speak in favor of the error criterion, which
seems to be more reasonable than the SNR approach at
masking voxel to be displayed in kPL maps.
The choice of ?SNR:;<
{>Z, SNR=>?
{>Zü and c is arbitrary and
looking at the outcome alone might be misleading, as shown
in Fig. 3. On the quest to opt for specific values, we started by
considering the approach which has been often used in our
group so far, namely ?SNR:;<
{>Z, SNR=>?
{>Zü = [10, 5]. According
to tables 3a and 3b, it has ghZ
:[<? = 17 and R2 = 0.53.
Bearing this in mind, we created kPL and kPB64 maps for every
data set, by using model A combined with the error criterion
with ?SNR:;<
{>Z, SNR=>?
{>Zü = [5, 3] and c = 0.6.
These set of values has ghZ
:[<? = 27 and R2 = 0.53, meaning
that, if compared to the former approach, it provides about
60% more voxels albeit keeping similar statistical precision.
As for data sets in Table 1, the mean value of the fractional
error c = skPL / kPL in a single data set varies between 0.16 and
0.34, giving an idea of how they link to the graphic outcomes
of fitting in Fig. 3.
In Fig. 4 we show examples of 5 kPL maps superimposed to
FLAIR images of a patient affected with gliosarcoma (data set
14) and of 5 kPB maps superimposed to T1-weighted images of
a patient affected with gliosarcoma (data set 16).
Figure 4. [Top] Examples of kPL maps superimposed to FLAIR images of a
patient (data set 14) affected with glioma (active tumor highlighted in red)
[Bottom] Examples of kPB maps superimposed to T1-weighted images of a
patient (data set 16). Sinc interpolation and the error criterion (skPL <
0.6?kPL) were used to generate kinetic maps in both cases (see text for detail).
All the kPL maps have been computed by using the error
criterion with ?SNR:;<
{>Z, SNR=>?
{>Zü = [5, 3] and skPL < 0.6?kPL.
The patient affected with gliosarcoma has previously received
radiation and temozolomide chemotherapy, along with
adjuvant agent pembrolizumab.
The interpretation of the correspondence between higher or
lower kPL values and presence of tumor is deferred to future
studies since it is beyond the scope of this communication,
that is mainly focused on methods to compute kinetic maps for
our brain data sets.
V. CONCLUSIONS
We conducted preliminary studies of hyperpolarized [1-
13C]
pyruvate injected in 10 patients affected with glioma,
demonstrating the feasibility for quantifying kinetic rates kPL
and kPB, which describe the conversion of pyruvate into lactate
and bicarbonate. We compared five kinetic models and two
approaches for discarding voxels with improper fitting and we
provided quantitative and spatially resolved maps of kinetic
rates. Such hyperpolarized MRI experiments enable an
unprecedented window into cerebral metabolism, which is not
provided by other molecular imaging techniques.
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Lactate topography of the human brain using hyperpolarized 13C-MRI
研究背景
研究結(jié)果
研究對象
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13C]lactate ?] 13C]bicarbonate ??????????????
???????????????????????????????ǖ?A??????ǘ?B?????ǘ?C?
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I II III
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研究結(jié)論
應(yīng)用方向
[1-13C]pyruvate代謝??[
13C]lactate?]13C]bicarbonate???????????????????????????
??????????????代謝???????研?????代謝?????研??????????????
??????代謝?????????研??
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IEEE Trans Med Imaging. 2020 Feb;39(2):320-327.
doi: 10.1109/TMI.2019.2926437.
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Journal Pre-proof
Lactate topography of the human brain using hyperpolarized 13C-MRI
Casey Y. Lee, Hany Soliman, Benjamin J. Geraghty, Albert P. Chen, Kim A. Connelly,
Ruby Endre, William J. Perks, Chris Heyn, Sandra E. Black, Charles H. Cunningham
PII: S1053-8119(19)30793-1
DOI: https://doi.org/10.1016/j.neuroimage.2019.116202
Reference: YNIMG 116202
To appear in: NeuroImage
Received Date: 27 March 2019
Revised Date: 19 August 2019
Accepted Date: 16 September 2019
Please cite this article as: 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., Lactate topography of the human brain using
hyperpolarized 13C-MRI, NeuroImage (2019), doi: https://doi.org/10.1016/j.neuroimage.2019.116202.
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition
of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of
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? 2019 Published by Elsevier Inc.
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Lactate Topography of the Human
Brain using Hyperpolarized 13C-MRI
Casey Y. Lee1,2, Hany Soliman2, Benjamin J. Geraghty8,1, Albert P. Chen3,
Kim A. Connelly4, Ruby Endre8, William J. Perks5, Chris Heyn6, Sandra E.
Black7, and Charles H. Cunningham8,1
1Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
2Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada 3GE Healthcare, Toronto, Ontario, Canada 4 Keenan Research Centre, Li Ka Shing Knowledge Institute, St. Michael’s Hospital,
Toronto, ON, Canada 5Pharmacy, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
6Radiology, Sunnybrook Health Sciences Centre, Toronto, ON Canada 7Department of Medicine (Neurology) and Hurvitz Brain Sciences Research Program,
Sunnybrook Health Sciences Centre, Toronto, ON Canada 8Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
Running Head: Lactate Topography of the Human Brain
Contact Information:
Charles H. Cunningham (Corresponding Author)
Department of Medical Biophysics, University of Toronto.
2075 Bayview Avenue, Toronto, Ontario M4N 3M5, Canada.
Tel: (416) 899-4406
Email: charles.cunningham@utoronto.ca
Wordcount (Main Article): 2138
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Abstract
Lactate is now recognized as an important intermediate in brain metabolism, but its role is
still under investigation. In this work we mapped the distribution of lactate and bicarbonate
produced from intravenously injected 13C-pyruvate over the whole brain using a new imaging
method, hyperpolarized 13C MRI (N=14, ages 23 to 77). Segmenting the 13C-lactate images
into brain atlas regions revealed a pattern of lactate that was preserved across individuals.
Higher lactate signal was observed in cortical grey matter compared to white matter and was
highest in the precuneus, cuneus and lingual gyrus. Bicarbonate signal, indicating flux of
[1-13C]pyruvate into the TCA cycle, also displayed consistent spatial distribution. One-way
ANOVA to test for significant differences in lactate among atlas regions gave F = 87.6 and p
< 10?6. This report of a “l(fā)actate topography” in the human brain and its consistent pattern
is evidence of region-specific lactate biology that is preserved across individuals.
Key Words
Lactate; Bicarbonate; Hyperpolarized 13C MRI; Metabolism; Structural Segmentation; Aerobic Glycolysis
Abbreviations
ANLS astrocyte-neuron lactate shuttle
OGI oxygen to glucose index
DE-EPI dual-echo echo-planar imaging
ANOVA one-way analysis of variance
W coefficient of Kendall’s concordance
LDH lactate dehydrogenase
rCBF regional cerebral blood flow
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1 Introduction
Lactate, once considered a waste product, is now recognized as an important intermediate
in brain metabolism (1, 2, 3, 4). The astrocyte-neuron lactate shuttle (ANLS) model, first
proposed by Pellerin and Magistretti (1), is that the ATP needed to clear glutamate from
synapses is primarily derived from lactate produced by aerobic glycolysis in astrocytes. There
is evidence of multiple roles for lactate: a source of energy in glutamatergic neurons, a
signalling molecule modulating neuronal excitability and synaptic plasticity, and a key player
in maintaining homeostasis (4). Furthermore, experiments in rats have shown that lactate
transport is required for long-term memory formation (5), and that cerebral aerobic glycolysis
and lactate concentration are reduced during sleep (6).
In this work, we mapped the distribution of lactate and bicarbonate produced from
intravenously injected 13C-pyruvate over the whole brain using a new imaging method, hyperpolarized 13C MRI. The 13C-lactate images were segmented into brain atlas regions, which
revealed a consistent spatial distribution of lactate across individuals. This is the first report
of such a “l(fā)actate topography” in the human brain. The consistent pattern is evidence of
region-specific lactate biology that is preserved across individuals.
While there continues to be debate surrounding the degree to which neurons are fueled by
astrocytic lactate (7, 8), mounting evidence points to a critical role of aerobic glycolysis and
lactate in brain energy metabolism. Combined PET measurements of cerebral metabolic rate
of oxygen and glucose using 15O-oxygen and 19F-fluorodeoxyglucose, respectively, can provide
an estimate of aerobic glycolysis through a measure of the oxygen to glucose index (OGI),
which is 6.0 if glucose is fully oxidized. These PET experiments have shown clear evidence of
non-oxidative consumption of glucose (OGI < 6.0), with net brain glucose uptake exceeding
the rate required to match the regional oxygen consumption. OGI is lowest in children and
young adults and increases with aging (9, 10), and recently, it was shown that the OGI
has a distinct topography within the brain, corresponding to regions with persistent gene
expression associated with childhood development (neoteny) (11, 12).
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Hyperpolarized 13C-MRI is a new imaging method that enables time-resolved volumetric
imaging of metabolite production within tissue (13) and has recently been translated to
humans (14, 15, 16). Using this method, we mapped the distribution of 13C-lactate and 13Cbicarbonate produced from intravenously injected 13C-pyruvate in the brains of volunteer
subjects (N=14).
2 Materials and Methods
Written informed consent was obtained from all subjects (N = 14) prior to study participation
under a protocol approved by the Sunnybrook Research Ethics Board and by Health Canada
as a Clinical Trial Application. A 20-gauge intravenous catheter was inserted into the forearm
of each subject before they were positioned supine and head-first in a GE MR750 3.0T
MRI scanner (GE Healthcare, Waukesha, WI). A custom 13C head coil was used to acquire
3D dual-echo echo-planar imaging (DE-EPI) data of [1-13C]lactate, [1-13C]bicarbonate, and
[1-13C]pyruvate (axial, FOV 24 x 24 x 36 cm3, 1.5 cm-isotropic resolution, 5 s temporal
resolution, total of 60 s acquisition). At each timepoint, lactate, bicarbonate, and pyruvate
images were acquired with net tip angles of 80?, 80?, 11?, respectively, which was spread
across 24 excitations. After the metabolic images, the 13C head coil was carefully replaced
with a standard 8-channel 1H neurovascular array (Invivo Inc.) and standard anatomical
images were acquired.
Each subject was injected with a 0.1 mmol/kg dose of [1-13C]pyruvate prepared within
a sterile fluid path. Each dose contained 1.47 grams of [1-13C]pyruvic acid (Sigma Aldrich,
St. Louis, MO) and AH111501 [Tris(8-carboxy-2,2,6,6 (tetra(methoxyethyl) benzo-[1,2-
4,5]bis-(1,3)dithiole-4-yl)methyl sodium salt] (Syncom, Groningen, The Netherlands) in a
49:1 weight by weight ratio, respectively. This mixture was then hyperpolarized in a polarizer (General Electric SPINLab system, equipped with a quality control module) for 3
hours to achieve maximum polarization. Just prior to 13C image acquisition, the sample
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was dissolved within the sterile fluid pathway by 38 mL of heated and pressurized sterile
water, transferred to the receiver vessel and mixed with 17.5 mL of a neutralizing solution
(600 mmol/L NaOH, 333 mmol/L Tris base, and 333 mg/L disodium EDTA) plus 19 mL of
sterile water. An aliquot of the final product was used for quality control assessment and
the remainder (45 mL) was transferred into a Medrad (Medrad, Indianola PA) syringe for
injection. The sample was injected at 4 mL/s followed by a 25 mL normal saline flush at 5
mL/s using a Spectris Solaris power injector. The 13C image acquisition was initiated at the
end of the saline flush.
Volumetric images of [1-13C]lactate, 13C-bicarbonate, and [1-13C]pyruvate were acquired
using a 3 Tesla MRI scanner and a 13C head coil. Metabolite images covering a 3D volume
(128 x 16 x 24 voxels / 1.5 cm isotropic resolution) were acquired at 5 s intervals over
the 60 s acquisition window. The center frequency of the spectrally-selective echo-planar
pulse sequence (17, 18) was toggled between the resonance frequencies of [1-13C]lactate, 13Cbicarbonate and [1-13C]pyruvate, resulting in separate volumetric images for each of these
metabolites, every 5 s. Following the metabolite images, a standard suite of anatomical brain
images was acquired with a conventional head coil.
To understand how the observed spatial distribution of metabolite signals related to brain
structure, the LONI pipeline processing environment (19) was used to segment the metabolite
images from each subject into the 56 regions contained in the LPBA40 atlas (20). The 13C image analysis workflow was modified from the LONI Brain Parser workflow. First, the 13C images were resampled to match the coordinates and matrix size of the anatomical T1-weighted
images (axial fast spoiled gradient echo images, FOV 25.6 x 25.6 cm2, 1 mm-isotropic resolution, TR/TE 7.6/2.9 ms, flip angle 11?). The T1-weighted images from each subject were
registered to the averaged human brain images used to produce the LPBA40 atlas by generating and applying a B-spline deformation field. The Brain Parser package was then used
on these forward deformation field-warped T1-weighted images to label the 56 regions. The
labelled 3D images were then warped by the inverse of the deformation field to transform the
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labels back to the coordinates of the original T1-weighted images. The mri segstats program
included in the Freesurfer package (http://surfer.nmr.mgh.harvard.edu) was then used to
compute mean signals from each atlas region from the [1-13C]lactate and 13C-bicarbonate
images from each subject. These values were normalized by converting to a z -score (the
number of standard deviations from the mean of all regions within a single subject). This
normalization accounts for any global scaling of the data for an individual subject, such
the signal-enhancement level of the injected 13C-pyruvate and any difference in substrate
delivery to the brain.
3 Results
Representative metabolite images from two subjects are shown in Fig. 1. Lactate signal,
which results from the conversion of [1-13C]pyruvate to [1-13C]lactate, was observed in all
subjects with a consistent topography. Higher lactate signal was observed in cortical grey
matter compared to white matter and was highest in the precuneus, cuneus and lingual
gyrus. Bicarbonate signal, which indicates the flux of [1-13C]pyruvate through the pyruvatedehydrogenase complex on the mitochondrial membrane, resulting in 13CO2 which is rapidly
converted to 13C-bicarbonate, had a similarly consistent spatial distribution, and was also
highest in many of the same regions as lactate, such as the occipital lobe.
The segmented regional z -score analysis revealed a highly consistent but spatially variable
pattern of lactate signal across subjects, as seen in Fig. 2(a). One-way analysis of variance
(ANOVA) was run to test for significant differences in lactate z-score among atlas regions,
giving F = 87.6 and p < 10?6. The same analysis reported the significant differences in
bicarbonate z -score among atlas regions, giving F = 79.6 and p < 10?6. Consistent regional
distribution patterns were observed across subjects for all metabolite z-scores, with Kendall’s
concordance coefficients (W ) of 0.83 for lactate, 0.82 for bicarbonate and 0.85 for pyruvate.
The lactate-to-pyruvate ratio for each region displayed large variance between subjects
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(see supplemental Fig. 6(a)). However, the concordance of the regional lactate-to-pyruvate
ratio (W = 0.75) was only slightly lower that for regional lactate (W = 0.83), showing a
fairly consistent pattern in the lactate-to-pyruvate ratio across subjects. Plotting the lactate
z-scores vs. the pyruvate z-scores for all subjects showed that the lactate and pyruvate
signals were correlated until a lactate z-score around 1.0, and were not correlated for the
higher lactate regions (see supplemental Fig. 6(d)).
To further investigate whether cerebral venous drainage is dominating the regional variation in 13C-lactate signal, a skilled neuroradiologist drew ROIs on the right jugular vein
in a subset of the subjects (N=5), and the timecourse of the 13C-lactate and 13C-pyruvate
in this ROI was plotted (see Supplementary Fig. 7). The observed lactate-to-pyruvate ratio
(computed as an area-under-the-curve ratio) was significantly lower in the jugular ROIs as
compared with all of the brain atlas regions, which is evidence against the 13C-lactate signal
observed in the brain being dominated by venous signal. A limitation of this analysis is
that the jugular veins are immediately adjacent to the carotid arteries, so contamination of
the jugular signal with 13C-pyruvate signal from the artery could have biased the lactateto-pyruvate ratio downwards. However, the lactate signal in the jugular ROIs was still far
lower than the lactate signal in the surrounding brain parenchyma, which has much lower
blood volume, with all of the jugular lactate z -scores below 0 (-2.65 to -0.37). As for 13Cbicarbonate, the literature on hyperpolarized 13C-pyruvate to date contains no reports of
13C-bicarbonate signal in the blood. This widely observed finding is consistent with 13Cbicarbonate being trapped in the intracellular space on the timescale of the hyperpolarized
13C experiment. Thus it is unlikely that the 13C-bicarbonate signal observed in the brain is
in the vascular pool.
The potential for bias in the regional metabolite signals due to the 1.5 cm (isotropic)
voxel size in the 13C images was investigated. Region volumes for the 14 subjects are plotted
in Fig. 5 (supplementary). Kendall’s concordance (W ) coefficient for the region volumes confirmed strong agreement between subjects (W = 0.98). The concordance (W ) between the
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mean lactate z -scores and mean regional volumes was 0.59, confirming a weak concordance.
Regional volumes ranged between 1.40 - 104.03 cm3 (median 11.96 cm3). Of 56 segmented
regions, 48 regional volumes were larger than the volume of a single isotropic 1.5 cm voxel.
4 Discussion
The high 13C-lactate signal level observed here is consistent with the 13C-lactate observed
in recent data from other groups (16, 21, 22). While the steady-state lactate concentration
in the brain measured by proton spectroscopy is relatively low (0.3 mM to 1 mM) (23,
24) and increases only slightly with activation (25, 26), the hyperpolarization makes 13Clactate readily observable at sub-millimolar concentration. The consistency of the lactate
topography across subjects suggests that region-specific lactate biology is similar between
individuals.
A number of previous studies have reported the regional distribution of factors that
are potentially related to the observed 13C-lactate pattern in the human brain including
the regional distribution of aerobic glycolysis (11, 9, 27), the enzyme lactate deydrogenase
(LDH) (28, 29), cerebral blood flow (30), oxidative damage to mitochondrial DNA (31) and
FDG uptake (32). There is some agreement between these distributions and the high lactate
regions seen here. For example, the topography of elevated aerobic glycolysis (reduced OGI)
(11, 9) showed elevated aerobic glycolysis in the precuneus, and regional cerebral blood
flow (rCBF) maps show the high z-scores in both the lingual gyrus and precuneus (30).
However, two of the regions reported to have elevated aerobic glycolysis corresponded to
the regions with the lowest lactate z -scores (caudate and gyrus rectus), and the superior
frontal gyrus shows high FDG uptake (32) but had a mean lactate z-score near zero, so
more investigation is required to understand how these factors may be related. It is worth
noting that reduced OGI indicates regions where there is a net non-oxidative use of glucose,
whereas hyperpolarized MRI shows all lactate created from the injected substrate, including
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lactate that ultimately ends up being used in oxidative phosphorylation.
The appearance of [1-13C]lactate signal is dependent on a different set of glycolysis-related
factors in comparison to the uptake of radiolabeled glucose and oxygen. These major factors are the expression levels of monocarboxylate transporters to transport pyruvate and
lactate across lipid bilayers, the availability of the enzyme LDH, and the local concentration
of NADH, which is oxidized to NAD+ when pyruvate is reduced to lactate in the cytosol.
Depletion of NADH has been shown to be the dominant factor in the 13C-lactate signal reduction observed after treating cancer cells (33), so regional variation in NADH concentration
is a possible explanation for the observed lactate pattern.
5 Conclusion
Hyperpolarized 13C MRI revealed a consistent lactate topography across subjects of varying
age. This consistent pattern is evidence of region-specific lactate biology that is preserved
across individuals.
6 Figures and Tables
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4924
2462 1266
2529
1937
3873
968
1935
875
1750
1750
3500
2750
5500
1500
3000
Lactate Bicarbonate
Subject #10 (29 y.o. ) Subject #9 (69 y.o.)
A
B
C
Figure 1: Representative images of 13C-lactate (left) and 13C-bicarbonate (right) from
healthy female volunteers aged 29 (upper) and 69 (lower). The metabolite signals are displayed as colour overlays on the corresponding T1-weighted anatomical images in grayscale,
and were computed by summing the 12 timepoints over the 60 s acquisition window. The
arrows indicate (A) the left precuneus, (B) the left cuneus and (C) the reference sample used
for pre-scan calibration.
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A
B
Figure 2: The topography of normalized lactate and bicarbonate signals across subjects
(N=14). (A) Lactate and (B) bicarbonate z -scores plotted vs. the LPBA40 atlas region
labels, with each colour showing a different subject.
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A
B
Figure 3: Consensus maps of the (A) lactate and (B) bicarbonate signals calculated from
the mean z -score for each LPBA40 atlas region.
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A
B
Figure 4: Heatmaps representing the lactate and bicarbonate z-scores with the regions in
ascending order for lactate from one of the subjects (23,M). LPBA40 numbers and names
of the regions with the 8 highest lactate z -scores are: (49) left precuneus, (67) left cuneus,
(50) right precuneus, (68) right cuneus, (90) right lingual gyrus, (122) right cingulate gyrus,
(121) left cingulate gyrus, (89) left lingual gyrus.
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Quantifying normal human brain metabolism
using hyperpolarized [1-13C]pyruvate and magnetic resonance imaging
研究背景
研究結(jié)果
研究對象
???????代謝??????????????????????????????????????????
???????????????????????? A?????????????????????????
???????????代謝???????????????????1H-MRS??????????????
????????????
?極?] 1-13C]pyruvate ?????????????????????研???研?????極?] 1-13C]pyruvate ?
???????????????????????????????代謝??????????????????
? (I):????代謝?]?1-13C]pyruvate?171ppm??代謝?]?
13C]???183ppm??[13C]?????161ppm????
???????????集????????4s????????????????????????????
? (II): ??????????????????????????????????????????????
? (III): ???????????????????????代謝?kPL?kPB???????????????????
??????????????????????
??[1-13C]pyruvate????????????13C?????????????????????????????
??????????????????代謝???????代謝????????????????????
?????????????????????????????????????????????????
?????????????????????????????????????代謝???kPL???0.012±
0.006s-1?kPB???0.002± 0.002s-1????13C?????????? ????????????
4??????ǖ???????代謝
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研究結(jié)論
應(yīng)用方向
??????極?] 1-13C]pyruvate MRI ???????????????代謝????????????????
代謝???????????????????????????????????????????????
?????代謝??? ?
? ?????????????????研?
? ??????????????研?
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Quantifying normal human brain metabolism using hyperpolarized [1–13C]
pyruvate and magnetic resonance imaging
James T. Grist a, Mary A. McLean b, Frank Riemer a, Rolf F. Schulte c, Surrin S. Deen a,b,
Fulvio Zaccagna a, Ramona Woitek a,b, Charlie J. Daniels a, Joshua D. Kaggie a, Tomasz Matyz a,
Ilse Patterson d
, Rhys Slough d, Andrew B. Gill a, Anita Chhabra g, Rose Eichenberger h,
Marie-Christine Laurent a, Arnaud Comment b,i, Jonathan H. Gillard a, Alasdair J. Colesj,
Damian J. Tyler k
, Ian Wilkinson e, Bristi Basu f, David J. Lomas a, Martin J. Graves d,
Kevin M. Brindle b, Ferdia A. Gallagher a,*
a Department of Radiology, University of Cambridge, Cambridge, UK
b Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
c General Electric Healthcare, Munich, Germany
d Radiology, Cambridge University Hospitals, Cambridge, UK
e Department of Medicine, University of Cambridge and Cambridge Clinical Trials Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK f Department of Oncology, University of Cambridge, Cambridge, UK
g Pharmacy, Cambridge University Hospitals, Cambridge, UK
h University of Cambridge, MRC Epidemiology Unit, Cambridge, UK
i GE Healthcare, Chalfont St Giles, UK
j Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
k Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK
ARTICLE INFO
Keywords:
Metabolism
Hyperpolarized
MRI
Carbon-13
Brain
Pyruvate
ABSTRACT
Hyperpolarized 13C Magnetic Resonance Imaging (13C-MRI) provides a highly sensitive tool to probe tissue
metabolism in vivo and has recently been translated into clinical studies. We report the cerebral metabolism of
intravenously injected hyperpolarized [1–13C]pyruvate in the brain of healthy human volunteers for the first time.
Dynamic acquisition of 13C images demonstrated 13C-labeling of both lactate and bicarbonate, catalyzed by
cytosolic lactate dehydrogenase and mitochondrial pyruvate dehydrogenase respectively. This demonstrates that
both enzymes can be probed in vivo in the presence of an intact blood-brain barrier: the measured apparent exchange rate constant (kPL) for exchange of the hyperpolarized 13C label between [1–13C]pyruvate and the
endogenous lactate pool was 0.012 ! 0.006 s"1 and the apparent rate constant (kPB) for the irreversible flux of
[1–13C]pyruvate to [13C]bicarbonate was 0.002 ! 0.002 s"1
. Imaging also revealed that [1–13C]pyruvate, [1–13C]
lactate and [13C]bicarbonate were significantly higher in gray matter compared to white matter. Imaging normal
brain metabolism with hyperpolarized [1–13C]pyruvate and subsequent quantification, have important implications for interpreting pathological cerebral metabolism in future studies.
1. Introduction
Cerebral metabolism is important for normal brain function and becomes deranged in a number of pathological processes, such as inflammation, infection, ischemia, traumatic brain injury and in tumors (Jalloh
et al., 2015; Mathur et al., 2014; Matz et al., 2006). 18F-fluorodeoxyglucose (FDG) uptake, detected using positron emission tomography
(PET), is one approach to imaging this cerebral metabolism in patients.
Despite the sensitivity of PET, the signal acquired from 18F-FDG represents flux in only part of the glycolytic pathway measuring a combination
of delivery to the tissue, uptake by glucose transporters and subsequent
phosphorylation in the reaction catalyzed by the glycolytic enzyme,
hexokinase. As the technique cannot detect downstream products of
glucose metabolism, such as lactate and CO2, it provides no direct
* Corresponding author. Department of Radiology, School of Clinical Medicine, Box 218, University of Cambridge, Cambridge, CB2 0QQ, UK.
E-mail address: fag1000@cam.ac.uk (F.A. Gallagher).
Contents lists available at ScienceDirect
NeuroImage
journal homepage: www.elsevier.com/locate/neuroimage
https://doi.org/10.1016/j.neuroimage.2019.01.027
Received 12 September 2018; Received in revised form 8 January 2019; Accepted 10 January 2019
Available online 11 January 2019
1053-8119/? 2019 Published by Elsevier Inc.
NeuroImage 189 (2019) 171–179
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information on glycolytic fluxes and mitochondrial oxidative metabolism. The work presented here uses a new imaging method to investigate cerebral metabolism of pyruvate, a breakdown product of glucose.
Pyruvate is transported across both the intact blood-brain barrier and
the plasma membrane by the monocarboxylate transporters (MCTs).
Pyruvate can either be metabolized to lactate, catalyzed by cytosolic
lactate dehydrogenase (LDH), or is metabolized by oxidative decarboxylation to acetyl-CoA, catalyzed by mitochondrial pyruvate dehydrogenase (PDH). Proton (1
H) magnetic resonance spectroscopy (MRS) of the
healthy brain has demonstrated steady state cerebral lactate concentrations in the region of 0.6–1 mM, although it may be up to 2.2 mM in
neonates, where glucose metabolism is altered compared to the adult
brain (Prichard et al., 1991; Tomiyasu et al., 2016). The metabolic shift
from mitochondrial oxidative metabolism to glycolysis and lactate formation occurs in a number of pathological processes, such as ischemia,
inflammation, and in tumors (Jalloh et al., 2015; Mathur et al., 2014;
Matz et al., 2006). However, imaging of the spatial and temporal distribution of lactate by 1
H-MRS is inhibited by a low signal-to-noise ratio
(SNR) at clinical field strengths. Therefore, alternative non-invasive
methods to image lactate would be valuable to monitor glycolysis in vivo.
Hyperpolarized carbon-13 Magnetic Resonance Imaging (13C-MRI)
has emerged as a promising technique for studying tissue metabolism in
humans (Zaccagna et al., 2018). The method increases the SNR of liquid
state carbon-13 MRS (13C-MRS) by more than four orders of magnitude
(Ardenkjaer-Larsen et al., 2011; Hurd et al., 2012). This substantial increase in SNR has been used to non-invasively image the spatial distribution of intravenously injected 13C-labelled molecules in vivo such as
[1–13C]pyruvate. Importantly, dynamic 13C-MRI acquisition allows the
injected hyperpolarized [1–13C]pyruvate to be differentiated from its
metabolic products such as [1–13C]lactate, [1–13C]alanine and 13C-labelled carbon dioxide/bicarbonate as they form in real-time. There
are a number of challenges for imaging hyperpolarized carbon-13
metabolism, particularly the short half-life of the hyperpolarized
signal, which is typically 25–30 s for [1–13C]lactate and [1–13C]pyruvate
in vivo but as low as 10 s for [13C]bicarbonate (Gallagher et al., 2008).
This limits the range of metabolites that can be probed using this technique. Furthermore, the hyperpolarized signal is irreversibly depleted as
images are acquired and therefore efficient imaging strategies are
required to maximize the data that can be obtained. There are a number
of sequences that have been used in human studies and here we have
utilized IDEAL (Iterative Decomposition with Echo Asymmetry and Least
squares estimation) spiral chemical shift imaging (CSI), as it allows for
spatial averaging to increase the SNR for short lived metabolites (Gordon
et al., 2016; Wiesinger et al., 2012).
In order to quantify the dynamics of pyruvate metabolism, a number
of quantitative approaches for describing the exchange of hyperpolarized
carbon-13 label between pyruvate and lactate have been proposed,
including both model-based and model-free methods (Gomez Dami ! !an
et al., 2014; Khegai et al., 2014; Schulte et al., 2013). Modelling the
process as a two-site exchange system, which gives the apparent exchange rate constant (kPL) for label flux between pyruvate and lactate, is
the most accurate approach. However, time-to-peak (TTP) for the lactate
signal intensity and the ratio of the integrals of the lactate and pyruvate
signals (area under the curve, AUC) are simple model-free approaches
that can also be used to estimate label flux (Daniels et al., 2016).
Quantifying these metrics in normal tissue is important so that changes in
diseased tissue can be understood and monitored over time. Frequency
domain kinetic modelling has been shown to be robust in low SNR environments, such as hyperpolarized 13C-MRI data. Furthermore, the
frequency domain benefits from incorporating the arterial input function
(AIF) into the data, which would otherwise be challenging to accurately
estimate from the low spatial resolution hyperpolarized images (Khegai
et al., 2014).
Previous studies in rodent and non-human primate brains have
demonstrated cerebral lactate labelling following injection of hyperpolarized [1–13C]pyruvate, with a kPL of 0.0026 s!1 reported in the
macaque brain, albeit using a higher dose than currently used for humans
(~0.38 mmol/kg compared to ~0.11 mmol/kg) (Park et al., 2014).
Although formation of hyperpolarized [13C]bicarbonate has also been
observed in some rodent studies using a high pyruvate dose, it has not
been reported in non-human primates. This suggests that cerebral pyruvate metabolism may be dose dependent, that there may be interspecies variation in pyruvate metabolism, or that metabolism may be
affected by anaesthesia (Josan et al., 2013).
Hyperpolarized [1–13C]pyruvate has recently been applied to patient
studies with the first report in prostate cancer (Nelson et al., 2013). More
recent studies have demonstrated the technique in normal human heart
and as a treatment response marker in prostate cancer (Aggarwal et al.,
2017; Cunningham et al., 2016). Lactate labelling has also been
demonstrated in patients with brain tumors following therapy, where
there is also preliminary evidence for the formation of cerebral bicarbonate (Park et al., 2018; Miloushev et al., 2018). However, as these
tumors are highly invasive and have undergone therapy, the metabolism
of normal brain has not yet been established. Here we have used 13C-MRI
to image the conversion of hyperpolarized [1–13C]pyruvate into both
[1–13C]lactate and [13C]bicarbonate in the normal human brain and
have quantified pyruvate metabolism in gray and white matter.
2. Method and materials
2.1. Subject recruitment and screening
Local ethical approval was obtained for this prospective study (NRES
Committee East of England, Cambridge South, REC number 15/EE/
0255). Between September 2017 and March 2018, four volunteers (mean
age 27 " 2 years, one male, three female) were consented and screened
prior to imaging; this included assessment of blood pressure, oxygen
saturation, heart rate, electrocardiogram (ECG), and blood analysis (urea
& electrolytes, full blood count, serum lactate, serum glucose, and lactate
dehydrogenase (LDH)). Only volunteers with normal screening tests
were included in the study. Blood sampling was undertaken prior to
imaging and 30 min after. Oxygen saturation and heart rate were monitored during pyruvate injection and throughout the examination. The
volunteers were observed for up to 30 min after the end of the
examination.
2.2. 13C pyruvate preparation
Pharmacy kits/fluid paths for insertion into the clinical hyperpolarizer (SPINlab, 5T, Research Circle Technology, Niskayuna, NY)
were filled under sterile conditions. 1.47g [1–13C]pyruvic acid (Sigma
Aldrich, St Louis, Missouri, USA) containing 15 mM of an electron
paramagnetic agent (EPA, Syncom, Groningen, Netherlands) was sealed
in a vial; 38 mL sterile water was used for dissolution; 19 mL sterile water
with 17.5 mL NaOH/Tris/EDTA (2.4%, 4.03%, and 0.033% w/v
respectively, Royal Free Hospital, London) was used as a buffer for
neutralisation. Pharmacy kits were stored in a freezer at !20 #C for at
least two weeks prior to use (Zaccagna et al., 2018). The vial containing
the frozen pyruvate/EPA mix was defrosted in the helium pressurised
airlock in the hyperpolarizer for one hour. The sample was irradiated at
139 GHz at ~0.8 K for approximately three hours. Following rapid
dissolution, the pyruvic acid was neutralised with the buffer and Quality
Control (QC) checks were performed by an integrated QC module which
measured: pyruvate and EPA concentration, pH, temperature, sample
polarisation and volume of dissolute. The release criteria for injection
were: pyruvate concentration 220–280 mM; radical concentration
<3 μM; pH 6.7–8.1; and temperature 25–37 #C. After release, the sample
was passed through a hatch into the adjacent MRI scanner room and
0.4 mL/kg of the final ~250 mM hyperpolarized pyruvate solution was
injected at 5 mL/s using a syringe driver (Medrad, Warrendale, Pennsylvania, USA) followed by a saline flush of 25mL at 5 mL/s.
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2.3. Phantom imaging protocol
Imaging was undertaken using a 3T MR system (MR750, GE
Healthcare, Waukesha WI), using a dual-tuned 1
H/13C quadrature head
coil (Rapid Biomedical, Rimpar Germany).
Transmit Gain (TG) and center frequency (f0) were determined using
a Bloch-Siegert method (Schulte et al., 2011). To assess the 13C transmit
and receive B1 sensitivity of the coil, a uniform 16 cm diameter sphere
filled with pure polydimethylsiloxane was placed within a coil-loading
ring filled with saline (GE, GE Healthcare, Waukesha WI) inside of the
coil; this was used to obtain signal from natural abundance carbon
(Supplementary Fig. 1A). Transmit B1 was determined by acquiring 13C
IDEAL spiral CSI images with 9 nominal flip angles between 60! and 180!
and fitting a sine function to each voxel in the resulting images to
determine the ratio of nominal to actual flip angle (8-step cycle interleaving one slice-selective spectrum and seven spirals, Field of View
(FOV) 400 mm, slice thickness 40 mm, acquired resolution 26 " 26 mm2
,
reconstructed resolution 8.3 mm2, flip angles 70–180! in 10! increments,
repetition time (TR) 1 s, echo shift 1.1 ms) (Wiesinger et al., 2012).
2.4. Clinical imaging protocol
The clinical imaging was performed with the same MR system and
coil set up as for the phantom imaging.
The clinical 1
H imaging protocol comprised: T1-weighted volumetric
imaging (3D; inversion prepared gradient echo; inversion time ? 450 ms;
FOV ? 240 mm; TR ? 8.6 ms; echo time (TE) ? 3.3 ms; flip angle
(FA) ? 12!; spatial resolution ? 0.9 " 0.9 " 1 mm3
); B0 field map
(FOV ? 240 mm, TE ? 7, 14 ms, TR ? 100 ms, FA ? 20!, spatial resolution ? 1 " 1x5 mm3
). 13C transmit gain (TG) and center frequency (f0) were set using a13C
enriched urea phantom (8 M, Sigma-Aldrich, UK) attached to the ear
defenders worn by the subject. 13C imaging was undertaken using a dynamic IDEAL spiral acquisition (pulse bandwidth ? 2500 Hz, TR ? 0.5 s;
time resolution ? 4 s; FA ? 15!; FOV ? 240 mm; acquired spatial resolution ? 12 " 12 mm2
; reconstructed resolution ? 5 " 5 mm2; slice thickness ? 30 mm, acquired voxel volume ? 4.32 cm3
, total imaging time
60 s). Images and spectra were reconstructed with 15 Hz line broadening.
Data acquisition began 10 s after the end of injection. Summed images at
the acquired resolution without zero-filling are shown in Supplementary
Fig. 2C to demonstrate the true resolution. 13C image reconstruction, post-processing, and analysis were performed in Matlab (The Mathworks, Natick, MA). Data and postprocessing code are available upon request to the authors.
2.5. Quantitative post processing
Imaging data were reconstructed by explicitly calculating the IDEAL
Fourier matrix, prior to inversion. B0 maps were summed over each 13C
imaging slab. An example B0 map is shown in Supplementary Fig. 2B. B0
correction was applied during inversion, using an additional frequency
demodulation component (Moriguchi et al., 2003).
The slice spatial offset between metabolites was defined by equation
(1):
Δz ? Δf
γGss
(1)
Where Δz is the spatial shift (m), Δf the frequency difference between
metabolites (Hz), γ the gyromagnetic ratio of 13C (MHzT$1
), and Gss the
strength of the slice-select gradient (mT). This offset was used to determine the separate range of thin axial imaging slices contributing signal to
each metabolite individually.
Imaging and spectroscopic data were summed in the complex and
magnitude domains respectively, and ratio maps of lactate-to-pyruvate,
bicarbonate-to-pyruvate, and bicarbonate-to-lactate were calculated.
Total pyruvate, lactate, and bicarbonate maps were generated by
normalizing all the voxels to the peak pyruvate signal in the brain. The
rate constant, kPL, was calculated using a two-site exchange model using a
frequency-domain approach and linear least-squares fitting, with any
back conversion (kLP) and spin lattice relaxation effects combined as an
effective relaxation term, T1 eff (Khegai et al., 2014). The rate constant,
kPB, was also evaluated using a two-site model in the frequency domain,
representing the metabolism of [1–13C]pyruvate to [13C]carbon dioxide
catalyzed by pyruvate dehydrogenase (PDH), followed by exchange with
[
13C]bicarbonate, catalyzed by the enzyme carbonic anhydrase (Gomez
!
Dami!an et al., 2014).
dMBetT
dt ? $ρBMBetT t kPBMBetT (2)
Where MBetT is the time dependent bicarbonate signal, ρB is the
effective relaxivity of the bicarbonate signal (the inverse of T1 eff) and kPB
is the metabolic conversion rate of pyruvate to bicarbonate.
2.6. Image analysis
Segmented white, gray and whole brain matter masks were produced
from the 3D T1 weighted acquisition using statistical parametric mapping
(SPM12, Wellcome Trust Centre for Neuroimaging, UCL, London). A twostage approach was used to account for the chemical shift displacement
between different metabolites. Firstly, gray and white matter probability
maps were calculated by summing over different ranges of thin axial
imaging slices to match the thickness of the 13C imaging slices, offset for
each metabolite by its chemical shift displacement (Fig. 1A–B). Secondly,
binary maps were produced from these images which contained >60%
gray matter, white matter or brain for all three metabolites (Chard et al.,
2002).
2.7. Region of interest analysis
A number of regions of interest (ROIs) within the brain were evaluated to determine if there was spatial heterogeneity in tissue metabolism:
basal ganglia, deep white matter, corpus callosum, cortical gray matter,
and the brain stem (Fig. 2) to assess for spatial metabolic heterogeneity.
Several of these regions contained a combination of both gray and white
matter. Analysis was performed on the averaged voxels from all the
volunteers.
Tissue probability maps for each 13C slice were first calculated by
summing over a range of thin axial slices for each metabolite determined
by its chemical shift displacement from the transmitted frequency, as
illustrated for white matter in the superior slice of one volunteer. A:
lactate; B: pyruvate; C: bicarbonate. D: The final binary masks were
calculated where the average probability for gray matter or white matter
was >60% for all three metabolites; see text for details. E: 13C-pyruvate, 13C-lactate and 13C-bicarbonate distribution derived from these segmentation maps showing signal in white (unfilled) and gray (filled)
matter; signals are normalized to the peak pyruvate signal in the whole
brain, *p < 0.05.
A, B, and C (left to right): Example ROIs containing deep white
matter, basal ganglia, cortical gray matter, corpus callosum, and the
brain stem.
Whole brain, gray matter and white matter analyses were performed
with segmented tissue masks for total pyruvate, total lactate, total bicarbonate, kPL, kPB, lactate-to-pyruvate, bicarbonate-to-lactate, and
bicarbonate-to-pyruvate ratios by averaging all voxels acquired from all
volunteers using the segmented masks. Inter-slice gray and white matter
analyses were performed by averaging voxels from all volunteers on a
slice-by-slice basis. Further comparisons between metabolic parameters
were made between tissue regions of interest, as well as analysis with and
without B0 correction.
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2.8. Statistical analysis
Statistical analysis was undertaken by comparing paired values between gray and white matter on an inter-slice basis using the Wilcoxon
sign rank test in the Matlab Statistics and Machine Learning Toolbox. All
statistical results were corrected for multiple comparisons using a Bonferroni correction. Statistical significance was defined as p < 0.05.
3. Results
3.1. Coil radiofrequency homogeneity
The assessment of radiofrequency (RF) excitation (B1) uniformity
using the polydimethylsiloxane phantom demonstrated a highly homogeneous B1 field. The mean ratio of the nominal to the actual flip angle
within the central slice was 84 ! 3% (mean ! S.D.). The imaging and
slice profile results are shown in Supplementary Figs. 2B and 2C.
3.2. Hyperpolarized imaging
The time taken for dissolution and QC was 35 s. The time between the
release of the [1–13C]pyruvate filled syringe and the start of the intravenous injection was 11 ! 2 s (mean ! S.D.). The levels of polarisation
achieved in all four subjects, as measured in the liquid state by the QC
module, was 25 ! 3% (mean ! S.D.).
Summed spectra from the entire time course demonstrated [1–13C]
pyruvate signal (171 ppm) in the three axial slices acquired, which
extended from the brain vertex to the cerebellum (Fig. 3). These spectra
also demonstrated both [1–13C]llactate (183 ppm) and [13C]bicarbonate
(161 ppm) in all four volunteers and a small quantity of pyruvate hydrate
(177 ppm) was observed at early time points. Fig. 4A is an example
spectral time course with a time resolution of 4 s, demonstrating the
arrival/formation of the three metabolites over time in a single volunteer. Fig. 4B demonstrates the mean signal from all four volunteers,
normalized to the peak [1–13C]pyruvate signal in each case. On average,
signal from [1–13C]pyruvate, [1–13C]lactate and [13C]bicarbonate were
observed 4, 8, and 16 s after the start of imaging respectively, which
commenced 10 s after the start of the hyperpolarized [1–13C]pyruvate
injection.
A-C: The spatial location of the 3 cm 13C slices used in this study are
shown in green on a sagittal T1 weighted image through the brain: three
slices were imaged containing the cerebellum (inferior slice, A), basal
Fig. 1. The distribution of hyperpolarized signal from the three metabolites within gray and white matter.
Fig. 2. Region of interest analysis and a field map of the healthy brain.
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ganglia (central slice, B), and corona radiata (superior slice, C). D-F: show
the summed 13C magnitude spectra from the total time course acquisition, demonstrating signal from [1–13C]pyruvate (171 ppm), [1–13C]
lactate (183 ppm) and [13C]bicarbonate (161 ppm) in all slices.
A: Dynamic spectra acquired every 4 s from the central slice of a
volunteer, following injection of hyperpolarized [1–13C]pyruvate.
[1–13C]Pyruvate (171 ppm) inflow is seen with subsequent exchange into
[1–13C]lactate (183 ppm) at approximately 8 s after pyruvate arrival and
formation of [13C]bicarbonate (161 ppm) beginning at approximately
12 s. B: Average signal intensities (! S.D.) for all three metabolites from
all four volunteers demonstrating the temporal dynamics; signal has been
normalized to the peak [1–13C]pyruvate signal in each case. Lactate and
Fig. 3. 13C spectra acquired through the healthy brain following injection of hyperpolarized pyruvate.
Fig. 4. Dynamic 13C spectra from the healthy brain showing the time course of [1–13C]pyruvate, [1–13C]lactate and [13C]bicarbonate.
Fig. 5. IDEAL spiral 13C imaging demonstrating metabolite distribution in the healthy human brain.
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bicarbonate have been shown with a five-fold increase in signal intensity
for ease of viewing.
IDEAL spiral 13C-MRI demonstrated the spatial distribution of the
hyperpolarized metabolites throughout the brain (Fig. 5). Pyruvate signal
was observed in the cerebrum and cerebellum and in both gray and white
matter. The pyruvate and lactate signals were particularly high in the
cerebral venous sinuses (e.g. the superior sagittal sinus and at the
confluence of the sinuses), demonstrating that LDH activity and lactate
transport were sufficiently rapid to allow tissue washout during the
timescale of the experiment (see Supplementary Fig. 2A).
A. Example summed images from the brain of a healthy volunteer
(number 1) demonstrating [1–13C]pyruvate, [1–13C]lactate and [13C]
bicarbonate signal from three axial slices: superior, central and inferior.
The T1-weighted images have also been shown, as have the quantitative
maps of the exchange of pyruvate to lactate (kPL in s!1
). B: Similar imaging shown as in (A) from the central slice of the three other volunteers.
3.3. Tissue segmentation
Segmentation analysis of the whole brain is shown in Fig. 1E.
Significantly higher signal from all three metabolites was observed in
gray matter compared to white matter: [1–13C]pyruvate, 0.47 " 0.24 vs.
0.25 " 0.12; [1–13C]lactate, 0.09 " 0.04 vs. 0.06 " 0.03; [13C]bicarbonate, 0.03 " 0.01 vs. 0.02 " 0.01 respectively; p < 0.05). Since the difference in metabolites between gray and white matter was largest for
pyruvate, the ratios of lactate-to-pyruvate and bicarbonate-to-pyruvate
tended to be higher in white matter than gray, but this only reached
significance for lactate. There was no significant difference in kPL, kPB, or
bicarbonate-to-lactate ratio between the two tissue types (Fig. 6,
Table 1).
The mean kPL derived from the whole brain for all four subjects was
0.012 " 0.006 s!1
. Similar results were obtained for both segmented gray
and white matter (Table 1). The mean value for kPB derived from the
whole brain for all four subjects was 0.02 " 0.002 s!1
. Results from each
volunteer are shown in Supplementary Table 1. The effective mean pyruvate and lactate relaxation times (Tl eff) for the whole brain was
26 " 10 s.
A: Mean lactate-to-pyruvate ratios derived from the segmented imaging data for the three brain slices showing the signal from all voxels in
both white (unfilled) and gray (filled) matter averaged across all volunteers (mean " S.D.). B: Mean bicarbonate-to-pyruvate ratios. C:
Apparent exchange rate constants modelled from the time course data. D:
Mean bicarbonate-to-lactate ratios.
3.4. Region of interest analysis
In comparison, region of interest analysis revealed significant differences in kPL between deep white matter and regions containing basal
ganglia or the brainstem (0.008 " 0.002 vs. 0.024 " 0.05; 0.008 " 0.002
vs. 0.020 " 0.004 s!1
, respectively; p < 0.05 in both cases). This result
suggests that there are regional variations in kPL across the brain.
The 13C imaging data was used to derive quantitative parameters
from the whole brain, as well as segmented white and gray matter. Mean
(" S.D.) values for kPL, kPB, lactate-to-pyruvate ratio, bicarbonate-topyruvate ratio and bicarbonate-to-lactate ratio are shown.
3.5. Serum blood results
Serum analysis revealed an increase in lactate concentration between
baseline and 30 min after pyruvate injection: t24 " 8% (mean " S.D.;
n ? 3; range 0.1–0.7 mM). However, there was no change in serum
glucose or LDH. Volunteers experienced no change in baseline vital signs
and no significant side effects were experienced.
4. Discussion
Glucose, lactate and pyruvate all play a role as cerebral energy
sources. Astrocytic end-feet have high concentrations of glucose transporters and cover a large proportion of the capillary walls to facilitate
rapid glucose transport into the brain (Magistretti et al., 1999). Following
release by neurons, the neurotransmitter glutamate may undergo
sodium-dependent transport from the synaptic cleft into astrocytes,
where it stimulates glycolysis and lactate formation (Magistretti et al.,
1999). When this lactate is transported into the extracellular space by
MCTs, it may be taken up by neurons and converted into pyruvate, which
can then be used as an energy source. This hypothesis is supported by the
differential distribution of LDH isoforms between the two cell types:
LDH5 (comprising four LDHA subunits) has been shown to be present in
astrocytes but not neurons and is found in tissues that are more glycolytic, favoring the formation of lactate; in contrast, neurons express LDH1
exclusively (comprising four LDHB subunits) which is present in tissues
that have a predominately oxidative metabolism and favor the production of pyruvate (Bittar et al., 1996; Laughton et al., 2000). In this way,
there is a close metabolic coupling between astrocytes and neurons
involving an interplay between glucose, glutamate, pyruvate and lactate,
with astrocytes being more glycolytic and neurons being predominately
oxidative and consuming lactate (Bittar et al., 1996).
Although the roles of glucose and lactate in the brain are well
described, cerebral pyruvate transport and metabolism in the healthy
human brain is less well understood, as endogenous pyruvate concentrations are much lower and pyruvate is largely intracellular. The MCT
family transports pyruvate in addition to lactate; for example, astrocytes
express MCT1 and MCT4, and neurons express MCT2 (Pellerin et al.,
2007). MCT2 has a particularly high affinity for pyruvate with a Km of
0.1 mM, followed by MCT1 with a Km of 1.0 mM. Therefore, both cell
types, but particularly neurons, will rapidly transport pyruvate at the
peak tissue pyruvate concentrations achieved in the experiments
described here i.e. 0.1–1 mM (P!erez-Escuredo et al., 2016). The kinetics
of pyruvate metabolism observed in this study are a function of pyruvate
delivery to the brain, MCT expression, LDH activity and tissue lactate
concentration, all of which may vary between regions of the brain.
This study has quantified the metabolism of hyperpolarized pyruvate
in the healthy human brain for the first time. We have shown that [1–13C]
pyruvate is rapidly transported across the blood-brain barrier to form
[1–13C]lactate within the lifetime of the hyperpolarized signal. The peak
[1–13C]pyruvate and [1–13C]lactate signals were measured at 12 and 16 s
respectively, following the start of imaging. The presence of [1–13C]
lactate in the cerebral venous sinuses shows that there is also rapid
washout of the labelled lactate. The presence of [13C]bicarbonate
throughout the brain demonstrates that PDH activity is sufficient in the
normal human brain to enable mitochondrial function to be probed in
addition to cytosolic LDH activity; the peak signal from [13C]bicarbonate
was measured at 26 s following the end of injection. These results
demonstrate the possibility of applying this technology not only to diseases where lactate is elevated, but also as a biomarker of early mitochondrial damage, which is a feature of inflammation.
The signals acquired from [1–13C]pyruvate, [1–13C]lactate and [13C]
bicarbonate were higher in gray matter compared to white matter.
Perfusion differences between gray and white matter may partially account for the higher gray matter signal: gray matter perfusion has been
shown to be 1.4–4.0 times higher than in white matter (Li et al., 2014).
Given the relatively low temporal resolution of the metabolic imaging
used here, temporal differences in the pyruvate and lactate timecourses
could not be detected between gray and white matter. The lactate to
pyruvate ratio showed the only significant difference between tissue
types, which may be driven by the higher perfusion of gray matter.
Furthermore, although not significant in this N ? 4 population, there was
an increase in the bicarbonate-to-lactate ratio between the cortical gray
matter and deep white matter regions of interest, potentially showing
differences in metabolism. However, partial volume effects may also play
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a role as the thickness of the gray matter may be as small as 3 mm in
places and the results here represent a small sample size (Fischl and Dale,
2000). In comparison, region of interest analysis demonstrated a significant difference in kPL between areas of deep white matter and the basal
ganglia and brain stem, regions known to have high glycolytic activity
(Berti et al., 2014). This may be explained by regional variations in cerebral LDH expression or cellular density or cell type (Laughton et al.,
2000). Histochemical and in situ hybridisation methods have shown high
levels of LDH expression in the hippocampus, pons, thalamus and
neocortex of several species. Further larger studies, assessing repeatability and reproducibility of these findings and spatial heterogeneity
across the brain, will be important in understanding these preliminary
findings, as well as the development of methods to automatically
segment areas of high exchange by exploiting the multidimensional nature of dynamic hyperpolarized data and fully integrating spatial, temporal and spectral information (Daniels and Gallagher, 2017).
The mean whole brain kPL measured here was 0.012 ! 0.006 s"1
,
which is greater than a value of 0.0026 s"1 measured in the anaesthetised
macaque brain utilising a pyruvate dose which was approximately four
times greater per unit body weight (Park et al., 2014). A previous study
assessing the anesthetised porcine brain showed lactate formation only
after the transient opening of the blood-brain barrier, when a kPL of
0.012 ! 0.007 s"1 was measured which is similar to the values reported
here (Miller et al., 2018).. Higher kPL values have been measured in tumors, reflecting elevated LDH and lactate concentrations in these tissues.
For example, a value of 0.025 s"1 was reported in human prostate cancer
before treatment and 0.007 s"1 following androgen ablation therapy,
which is similar to the value reported here for normal tissue (Aggarwal
et al., 2017). However, the value of kPL measured here in normal brain is
lower than that found in a previous human brain tumour study, where a
mean whole brain kPL of 0.12 s"1 was detected; this included tumor as
well as normal-appearing brain and therefore kPL may be dominated by
the elevated tumor metabolism (Miloushev et al., 2018).
The mean kPB derived from the whole brain was 0.002 ! 0.002 s"1
,
which represents flux through the reaction catalyzed by PDH and subsequent exchange of the 13C label between carbon dioxide and bicarbonate. The latter reaction, which is catalyzed by carbonic anhydrase, is
rapid and assumed to be at equilibrium (Gallagher et al., 2015). Although
we were able to demonstrate higher bicarbonate signal in gray matter
compared to white matter, there were no significant tissue differences
found in kPB or in the ratios of bicarbonate-to-pyruvate or
lactate-to-pyruvate. The SNR of bicarbonate was a limiting factor in this
analysis acquisition and methods to increase this are important for future
studies: this could be achieved by incorporating spectral-spatial pulses to
selectively increase the excitation flip angle for bicarbonate (Larson et al.,
2008; Schulte et al., 2013). An increase in SNR will also enable acquisition of higher spatial resolution images of each metabolite. Future
larger studies are required to assess variation between individuals which
may be affected by factors such as age, gender and weight.
An important element of this study was the uniform RF excitation
profile, which has allowed a comparative quantitative analysis to be
undertaken across the brain. A uniform RF excitation profile is a
Fig. 6. Quantitative analysis of metabolism in white and gray matter.
Table 1
Quantitative metabolic parameters derived from regions within the brain without B0 correction. Segmented regions were automatically derived and regions of interest
(ROIs) were manually drawn.
kPL (s"1) kPB (s"1) Lactate: pyruvate ratio Bicarbonate: pyruvate ratio Bicarbonate: lactate ratio
Segmented whole brain mask 0.012 ! 0.006 0.002 ! 0.002 0.23 ! 0.07 0.07 ! 0.04 0.32 ! 0.15
Segmented white matter 0.012 ! 0.007 0.002 ! 0.002 0.25 ! 0.08 0.08 ! 0.05 0.32 ! 0.21
Segmented gray matter 0.011 ! 0.005 0.002 ! 0.002 0.22 ! 0.06 0.07 ! 0.03 0.32 ! 0.18
Cortical gray matter ROI 0.012 ! 0.001 0.003 ! 0.002 0.23 ! 0.02 0.08 ! 0.02 0.33 ! 0.1
Basal ganglia ROI 0.024 ! 0.005 0.002 ! 0.001 0.18 ! 0.03 0.04 ! 0.02 0.20 ! 0.1
Corpus callosum ROI 0.013 ! 0.004 0.002 ! 0.001 0.21 ! 0.03 0.07 ! 0.03 0.30 ! 0.2
Deep white matter ROI 0.008 ! 0.002 0.002 ! 0.001 0.22 ! 0.07 0.05 ! 0.02 0.20 ! 0.2
Brainstem ROI 0.020 ! 0.004 0.003 ! 0.002 0.22 ! 0.04 0.04 ! 0.02 0.21 ! 0.08
J.T. Grist et al. NeuroImage 189 (2019) 171–179
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consideration for selective excitation sequences, as the uncertainty in the
delivered flip angles may significantly affect the results derived from
kinetic modelling (Sun et al., 2018). The relative lack of contrast reported
here between kinetic parameters in normal-appearing gray and white
matter was unexpected, but could assist the identification of significantly
altered metabolism above or below background in pathological
conditions.
In conclusion, this study has demonstrated that 13C-MRI can be used
to acquire quantitative non-invasive measurements of hyperpolarized
[1–13C]pyruvate metabolism in the normal human brain and could be
used to measure regional variations in metabolism across the brain. This
work provides evidence that the methodology may have a role in
assessing disease processes where lactate is elevated and where mitochondrial function may be altered.
Acknowledgements
This study was funded by the Wellcome Trust, Cancer Research UK
(CRUK, C19212/A16628, C19212/A911376, C197/A16465), the CRUK
Cambridge Centre, National Institute of Health Research-Cambridge
Biomedical Research Centre, Medical Research Council, CRUK/Engineering and Physical Sciences Research Council Imaging Centre in
Cambridge and Manchester, Addenbrooke's Charitable Trust, the Cambridge Experimental Cancer Medicine Centre, the Evelyn Trust, the
Multiple Sclerosis Society, the National Institute for Health Research
[Cambridge Biomedical Research Centre at the Cambridge University
Hospitals NHS Foundation Trust and the Mark Foundation Institute for
Integrative Cancer Medicine at the University of Cambridge]. The views
expressed are those of the authors and not necessarily those of the NHS,
the NIHR or the Department of Health and Social Care.
Thanks to Sarah Hilborne, Jackie Mason, Vicky Fernandez, Hannah
Loveday, Ashley Grimmer, Emma Ward, Brian White, Amy Fray, Ronnie
Hernandez, Matthew Locke, Claire Trumper, Dario Prudencio, and Bruno
do Carmo.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.neuroimage.2019.01.027.
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Imaging acute metabolic changes in mild traumatic brain injury patients using hyperpolarized [1-13C]pyruvate
研究背景
研究結(jié)果
研究對象
???????Traumatic brain injury, TBI?????????????代謝??????????????????
???????????????代謝????????????????????????代謝???????
????????????????????????????????????
????? TBI ????????????????????????????????????????????
?????????????????代謝???????????????????????????????
???????
CT ?磁共振?????????????????????????????????????????????
???????????研??????????????????????????極?] 1-13C]pyruvate 代謝?
??研??極???????????代謝???????????????????????代謝??????
2??????????TBI???
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研究結(jié)論
應(yīng)用方向
?極?] 1-13C]pyruvate ????????????????代謝??????????? CT?磁共振???????
????????????????????????????? [
13C] ???????????????? [13C] ??
????????代謝?????????????????????????? [
13C] ????????????
??????????????????????????????????????????????????
??????
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??????????????????????????????????AD, PD ????極?] 1-13C]pyruvate
????????代謝??研??????????
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? ???????????????????????????????????
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????
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??????(A)(B)ǖ磁共振???????ǘ(C)(D)ǖ?????? ASL ??ǘ(E)(F)ǖ?極?] 1-13C]pyruvate ??ǘ(G)ǖ
?極?] 1-13C]pyruvate ??代謝????
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?????????28 ???????T1W ? T2W FLAIR ?????????????????????? ASL
?????????????????????????????????????????極?] 1-13C]pyruvate 代
謝?? [
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?????2cm ????5cm ?????????6 ??????T1W ? T2W FLAIR ?????????????
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13C] ??????????? [13C] ???
??????????50%
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iScience
Article
Imaging Acute Metabolic Changes in Patients with
Mild Traumatic Brain Injury Using Hyperpolarized
[1-13C]Pyruvate
Edward P.
Hackett, Marco C.
Pinho, Crystal E.
Harrison, ...,
Surendra
Barshikar,
Christopher J.
Madden, Jae Mo
Park
jaemo.park@utsouthwestern.
edu
HIGHLIGHTS
Clinical translation of
hyperpolarized pyruvate
to TBI was demonstrated
Patients with mild TBI
were imaged with
hyperpolarized [1-13C]
pyruvate
Altered lactate and HCO3
–
production in the brain
nearest the site of trauma
Hackett et al., iScience 23,
101885
December 18, 2020 a 2020
The Author(s).
https://doi.org/10.1016/
j.isci.2020.101885
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iScience
Article
Imaging Acute Metabolic Changes in Patients
with Mild Traumatic Brain Injury
Using Hyperpolarized [1-13C]Pyruvate
Edward P. Hackett,1 Marco C. Pinho,1,2 Crystal E. Harrison,1 Galen D. Reed,1,3 Jeff Liticker,1 Jaffar Raza,4
Ronald G. Hall,4 Craig R. Malloy,1,5 Surendra Barshikar,6 Christopher J. Madden,7 and Jae Mo Park1,2,8,9,
*
SUMMARY
Traumatic brain injury (TBI) involves complex secondary injury processes
following the primary injury. The secondary injury is often associated with rapid
metabolic shifts and impaired brain function immediately after the initial tissue
damage. Magnetic resonance spectroscopic imaging (MRSI) coupled with hyperpolarization of 13C-labeled substrates provides a unique opportunity to map the
metabolic changes in the brain after traumatic injury in real-time without invasive
procedures. In this report, we investigated two patients with acute mild TBI (Glasgow coma scale 15) but no anatomical brain injury or hemorrhage. Patients were
imaged with hyperpolarized [1-13C]pyruvate MRSI 1 or 6 days after head trauma.
Both patients showed significantly reduced bicarbonate (HCO3
–
) production, and
one showed hyperintense lactate production at the injured sites. This study reports the feasibility of imaging altered metabolism using hyperpolarized pyruvate in patients with TBI, demonstrating the translatability and sensitivity of
the technology to cerebral metabolic changes after mild TBI.
INTRODUCTION
Traumatic brain injury (TBI) causes mechanical damage and disruption of normal metabolism in the brain
(Corps et al., 2015). A major challenge for clinicians is managing complex secondary processes following
the primary injury. After the primary trauma, the surviving tissue undergoes metabolic shifts, resulting in
the development of potentially hazardous secondary metabolites and further damage. The secondary
events develop over a timescale after the primary injury, providing a potential window of opportunity
for detection and therapeutic intervention. Moreover, TBI is suspected to contribute to a variety of
chronic degenerative processes such as chronic traumatic encephalopathy, Alzheimer disease, and Parkinson disease (Smith et al., 2013). Numerous therapies have been proposed to reduce or prevent secondary brain damage, directly impacting long-term patient outcome (Xiong et al., 2015). Therefore,
the noninvasive detection and characterization of TBI pathophysiology during the acute and sub-acute
stages will have critical clinical implications and will be vital for identifying and developing effective
therapies.
Altered glucose metabolism and mitochondrial dysfunction are features of the pathophysiologic events
subsequent to TBI (Brooks and Martin, 2015; Kim et al., 2017). Invasive microdialysis methods have been
reported to study brain metabolism in these patients, but in spite of the critical importance of directly detecting mitochondrial function, no specific noninvasive methods exist. Positron emission tomography (PET)
detects uptake of radioactively labeled compounds such as glucose or acetate (e.g., [18F]FDG, [11C]acetate) in the human brain. Previous studies demonstrating cerebral hyperglycolysis in patients with TBI using
[
18F]fluorodeoxyglucose ([18F]FDG) PET measured cellular glucose uptake but could not measure the metabolic fate of glucose (Bergsneider et al., 1997). Another [18F]FDG-PET study that combined with microdialysis of patients with TBI reported that the rate of glucose metabolism correlates with microdialysate lactate
and pyruvate concentrations but not with the lactate-to-pyruvate ratio (Hutchinson et al., 2009), suggesting
an increase in glucose metabolism to both lactate and pyruvate, as opposed to a shift toward anaerobic
metabolism. Other studies indicated that the increased glucose uptake is directly related to the upregulated pentose phosphate pathway rather than to the rest of the glycolysis (Dusick et al., 2007).
1Advanced Imaging Research
Center, The University of
Texas Southwestern Medical
Center, Dallas, TX 75390, USA
2Department of Radiology,
The University of Texas
Southwestern Medical
Center, Dallas, TX 75390, USA
3GE Healthcare, Dallas, TX
75390, USA
4Department of Pharmacy
Practice, The Texas Tech
University Health Sciences
Center, Dallas, TX 75216, USA
5Department of Internal
Medicine, The University of
Texas Southwestern Medical
Center, Dallas, TX 75390, USA
6Department of Physical
Medicine & Rehabilitation,
The University of Texas
Southwestern Medical
Center, Dallas, TX 75390, USA
7Department of Neurological
Surgery, The University of
Texas Southwestern Medical
Center, Dallas, TX 75390, USA
8Department of Electrical and
Computer Engineering, The
University of Texas at Dallas,
Richardson TX 75080, USA
9Lead Contact
*Correspondence:
jaemo.park@utsouthwestern.
edu
https://doi.org/10.1016/j.isci.
2020.101885
iScience 23, 101885, December 18, 2020 a 2020 The Author(s).
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). 1
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Alternative way of imaging in vivo metabolism is using dynamic nuclear polarization (DNP) and rapid dissolution technique (Ardenkjaer-Larsen et al., 2003). Commercially available DNP polarizer can achieve more
than 100,000-fold signal amplification of magnetic resonance-detectable (e.g., 13C-labeled) substrates in
liquid state. Administration of hyperpolarized 13C-labeled substrates in coordination with 13C magnetic
resonance spectroscopy imaging (MRSI) opened new opportunities to assess in vivo metabolic processes
of individual enzyme-catalyzed reactions in various organs including the brain. In particular, previous animal studies using hyperpolarized [1-13C]pyruvate demonstrated that increased [1-13C]lactate production in
the injured brain tissue was associated with microglial activation (DeVience et al., 2017; Guglielmetti et al.,
2017). Metabolites that are typically detectable in the cerebral 13C spectrum using hyperpolarized [1-13C]
pyruvate are [1-13C]lactate and [13C]bicarbonate (HCO3
–
) as shown in Figure 1. The yellow arrows indicate
metabolic shifts in acute TBI (DeVience et al., 2017). In this study, we translated these preclinical discoveries
to demonstrate metabolic abnormalities in the brain of patients after acute mild TBI.
RESULTS
Two patients with acute TBI were recruited from the Parkland Health and Hospital System Emergency
Room. Computed tomographic (CT) scans at admission confirmed that neither patient had underlying fractures, hemorrhage, or other anatomical injury. Patient #1 was a daily smoker, and Patient #2 was diabetic.
Otherwise, both subjects were generally healthy and had no history of mental illness or alcoholism. Both
patients tolerated the examination well.
The first patient was a 35-year-old African American male (88 kg, 168 cm) with a 2-cm laceration to the left
frontal scalp due to blunt force trauma (whipped by a metal gun) with a Glasgow Coma Scale (GCS) score
of 15 and no loss of consciousness (LOC). Figure 2 shows axial images of this patient 28 h after the head
trauma. Besides left frontal scalp cutaneous edema/ecchymosis, no cerebral contusion or anatomical
lactate
glucose
glycolysis
cytoplasm
mitochondrion
TCA cycle
1 2 3
[1-13C]pyruvate pyruvate
acetyl-CoA
PDH
PC
LDH
CO2
CO2
HCO3
-
13C
12C
citrate
fumarate
malate
PEP OAA
α-ketoglutarate
CO2
CO2
Figure 1. Metabolic Fate of Hyperpolarized [1-13C]Pyruvate in the Brain
[1-13C]Pyruvate (black circle: 13C, white circle: 12C) is converted either to [1-13C]lactate via LDH or acetyl-CoA and 13CO2
(and [13C]HCO3
-
) through PDH. Possibly, [1-13C]pyruvate can be also converted into [1-13C]oxaloacetate (OAA) through
PC, eventually releasing the labeled carbon (13C) as CO2. The gray circle indicates 13C after backward scrambling of OAA
to fumarate and malate in the TCA cycle. PEP: phosphoenolpyruvate; LDH: lactate dehydrogenase; PDH: pyruvate
dehydrogenase; PC: pyruvate carboxylase.
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brain damage was identified in the T1-weighted and T2-weighted fluid-attenuated inversion recovery
(FLAIR) images. Arterial spin labeling (ASL) detected a hyperintense region at the site of the left frontal
scalp injury but unremarkable appearance of cerebral perfusion. Likewise, total hyperpolarized 13C signal
was hyperintense in the injured scalp (Figure 2D) but comparable between the left and right brain hemispheres. In contrast, [13C]HCO3
– and [1-13C]lactate production from hyperpolarized [1-13C]pyruvate was
altered in the injured side of the brain (left frontal lobe, underlying the scalp injury) when compared
with the contralateral side (Figures 2E and 2F). [13C]HCO3
– signal, averaged over the injured region of
interest and normalized with total 13C (TC) signal (ROITBI, HCO3
–
/TC = 0.027), was 54.0% lower than
that in the contralateral ROI (ROICTL, HCO3
–
/TC = 0.059). Conversely, lactate production in the injured
region (lactate/TC = 0.236) was larger by 23.7% than the unimpacted contralateral brain region
(lactate/TC = 0.191).
The second patient (Patient #2; 48 years old, 77 kg, 160 cm, Hispanic, male) sustained a head injury as a
consequence of an !6-m fall from a construction site scaffolding (GCS 15, 2 min LOC). When recruited,
he had a 2-cm laceration to the left medial eyebrow and 5-cm subcutaneous hematoma extending laterally
from the laceration. He was studied using the same 1
H/13C magnetic resonance imaging (MRI) protocol
6 days after the initial injury. Similar to Patient #1, no structural brain damage was found in the 1H MRI,
but smaller [13C]HCO3
– was observed in the injured region (HCO3–
/TC = 0.025) than the contralateral
side of the brain (0.047) (Figure 3). [1-13C]Lactate level was comparable (lactate/TC = 0.164 for the injured
region and 0.162 for the contralateral region).
A B
G
C D
E F
Figure 2. Metabolism of Acute TBI in Patient #1 Imaged by Hyperpolarized 13C Pyruvate
The patient (35 years old, male), injured on the left frontal scalp (GCS 15, no LOC), was imaged 28 h after the injury.
(A) An axial slice that includes the injured region was prescribed for 13C imaging.
(B and C) (B) T1-weighted and T2-weighted 1
H FLAIR and (C) ASL images showed swelling and increased perfusion in the
injured site outside the skull (yellow circle), whereas no cerebral contusion, hemorrhage, or hypoperfusion was detected.
(D) Region with increased total hyperpolarized 13C (TC) signal image matched to the scalp edema.
(E and F) (E) Increased [1-13C]lactate conversion and (F) decreased [13C]HCO3
– production were detected in the brain
tissues of the injured hemisphere.
(G) Averaged spectra over the injured brain region (ROITBI, dotted spectra) and the contralateral normal-appearing brain
region (ROICTL, solid spectra).
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DISCUSSION
A common feature reported in patients with TBI is the increase of glucose consumption rate with no parallel
increase in mitochondrial oxidative phosphorylation, known as hyperglycolysis (Bergsneider et al., 1997).
Hyperglycolysis coupled with mitochondrial dysfunction should favor metabolism of pyruvate, the end
product of glycolysis, to lactate via lactate dehydrogenase, rather than oxidation to acetyl-CoA via pyruvate dehydrogenase (PDH). The hyperintense [1-13C]lactate signal in the impacted region from Patient
#1 is consistent with previous in vivo preclinical imaging studies. DeVience et al. showed significant increase of [1-13C]lactate production from hyperpolarized [1-13C]pyruvate 4 h post-injury using a controlled
cortical impact rat model (DeVience et al., 2017), and Guglielmetti et al. reported longitudinal changes of
lactate production using this technique (Guglielmetti et al., 2017). The absence of hyperglycolysis in Patient
#2 could be due to the severity of the injury (Bergsneider et al., 1997) or metabolic transition from hyperglycolysis to hypometabolic stage (Greco et al., 2020; Jalloh et al., 2015).
Mitochondrial dysfunction plays a key role in the pathophysiology of TBI (Kim et al., 2017). [13C]HCO3
– production from hyperpolarized [1-13C]pyruvate reflects PDH flux. As PDH is an enzyme complex that is integrated into the inner membrane of the mitochondria, the decreased [13C]HCO3
– production in the injured
brain region implies mitochondrial injury/dysfunction in spite of normal anatomy, which is consistent with
previous animal study (DeVience et al., 2017). It should be noted that [13C]HCO3
– can be also produced via
an anaplerotic pathway into the tricarboxylic acid cycle, which is exclusively achieved by pyruvate carboxylase (PC), an astrocyte-specific enzyme (Shank et al., 1985), as shown in Figure 1. Pyruvate carboxylation in
A B
G
C D
E F
Figure 3. Hyperpolarized 13C Pyruvate Imaging of Patient #2
The patient had a 2-cm laceration to the left medial eyebrow and 5-cm hematoma extending laterally from the laceration
from a head injury (GCS 15, 2 min LOC). Images were acquired 6 days from the injury.
(A–C) (A) 13C slice was prescribed to include the laceration. Axial 1
H images of (B) T2-weighted FLAIR and (C) ASL showed
the injured region outside the skull (yellow circle).
(D) Besides the scalp hematoma, no abnormal distribution of total hyperpolarized 13C signals (TC).
(E and F) (E) Lactate was comparable between the impacted region and the contralateral side of the brain, whereas (F)
decreased HCO3
– signal was observed in the impacted brain region.
(G) Averaged spectra over the injured brain region (ROITBI, dotted spectra) and the contralateral normal-appearing brain
region (ROICTL, solid spectra).
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astrocyte increases during the secondary injury process, whereas pyruvate utilization via PDH is downregulated to protect neurons (Bartnik-Olson et al., 2013).
As shown in the Figures 2D and 3D, hyperpolarized 13C signals were predominant in the gray matter rather
than the white matter or subcutaneous tissues. These are consistent with previous human brain studies
using hyperpolarized pyruvate (Gordon et al., 2019; Lee et al., 2019). Besides the directly impacted brain
regions, the lactate and HCO3
– maps showed larger variation in the patients when compared with those
acquired from healthy subjects in previous studies. For instance, reduced HCO3
– production was observed
in the right posterior brain region of Patient #1, indicating possible contrecoup injury in the region.
Both patients were overweight. Patient #1 was very muscular with body mass index (BMI) of 31.2 but did not
have a diagnosis of type 2 diabetes and was not on any hypoglycemic medications. Patient #2 (BMI = 30.1)
did have a diagnosis of type 2 diabetes on metformin (500 mg orally twice daily). Plasma glucose levels of
the patients were well within 160 mg/dL (108 for patient #1, 133 for patient #2), which is representative of
normal brain biochemistry for clinical purposes as measured by PET (Varrone et al., 2009; Viglianti et al.,
2017). Although diffuse changes in pyruvate metabolism could be postulated due to hyperglycemia, it
seems unlikely that regional abnormalities as observed here would occur.
The specific timing of this glucose dysmetabolism is currently not known. Longitudinal monitoring with a
larger patient population will be needed to further evaluate the utility of hyperpolarized pyruvate for
noninvasive assessment of the TBI metabolism. If this result can be verified, it would have a major impact
on the evaluation of patients involved in sports, military activities, or those who suffer from assault or accidents. Considering that this technique is safe and well-tolerated by patients, this technology may prove
valuable for managing patients experiencing secondary injury processes, identifying early treatment
response, and accelerating drug development efforts. In fact, pyruvate has been suggested as a
neuro-protective substrate with therapeutic effect (Moro et al., 2016). Patients with moderate and severe
TBI are expected to have larger metabolic alteration with potential challenges of imaging data interpretation due to morphological distortion and hemorrhage. Beyond acute and sub-acute TBI, the imaging
methods and associated biomarkers to be developed under this application will be likely also applicable
to concussion.
In this study, we investigated cerebral metabolism in patients with acute TBI using hyperpolarized [1-13C]
pyruvate. We found altered cerebral metabolism in the brain nearest the site of trauma despite no visible
anatomical damage in the brain on MRI, demonstrating the sensitivity of hyperpolarized pyruvate to
altered metabolism in TBI. The acute metabolic changes in [1-13C]lactate and [13C]HCO3
– images in the
injured region indicate the potential clinical utility of hyperpolarized [1-13C]pyruvate in managing patients
with TBI and provides objective evidence of injury even when conventional studies with CT and MRI are
unrevealing.
Limitations of the Study
This study demonstrated the feasibility of hyperpolarized [1-13C]pyruvate for imaging metabolic changes
following a mild brain injury in humans. Although we could detect clear alteration of pyruvate metabolism
in the injured brain, biological interpretation of the imaging biomarker needs to be careful and requires
further clarification. First, production of [1-13C]lactate signal reflects both metabolic flux from pyruvate
to lactate and isotopic chemical exchange between pyruvate and lactate (Kennedy et al., 2012). The latter
can be significantly affected by the intrinsic lactate pool size (Hurd et al., 2013). Second, unlike [13C]HCO3
–
,
[1-13C]lactate signal detected in the brain is not exclusively produced by brain tissues as [1-13C]lactate produced in the vasculature or other organs (Wespi et al., 2018; Xu et al., 2011) can be delivered to the brain.
Moreover, although lactate is a preferred energy substrate for neurons (Be′ langer et al., 2011; Magistretti
and Allaman, 2018), it is likely that the excessive cerebral presence of hyperpolarized pyruvate results in
cellular transport of pyruvate (and lactate) into both neurons and glia via monocarboxylate transporters.
The large spatial resolution is another limitation of the study. Although the large voxel size was helpful
to achieve reliable detection of HCO3
-
, it was unavoidable to experience partial volume effects in the 13C images. Imaging with improved spatial resolution will be required for accurate regional assessment
of TBI metabolism and to reduce the partial volume effects. The statistical analysis was not performed in
this pilot study primarily due to the small number of subjects and the difficulty of patient recruitments in
such an acute stage. Considering the diversity of brain injury types and the complexity of secondary
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injuries, studies with a larger number of patients with longitudinal follow-ups will be necessary for further
evaluation of the imaging technique and for systematic characterization of the metabolic alterations.
Resource Availability
Lead Contact
Further information and requests for resources should be directed to and will be fulfilled by the Lead Contact, Jae Mo Park (jaemo.park@utsouthwestern.edu).
Materials Availability
This study did not generate new unique reagents or materials.
Data and Code Availability
Original/source data or images in the paper is available upon request.
METHODS
All methods can be found in the accompanying Transparent Methods supplemental file.
SUPPLEMENTAL INFORMATION
Supplemental Information can be found online at https://doi.org/10.1016/j.isci.2020.101885.
ACKNOWLEDGMENTS
Funding: The Texas Institute for Brain Injury and Repair of Peter O’Donnell Jr. Brain Institute; The Mobility
Foundation; National Institutes of Health of the United States (1R01NS107409-01A1, 5P41EB015908-32,
1S10OD018468-01); The Welch Foundation (I-2009-20190330). Personnel Support: We appreciate the
research nurses and the MR technicians of the Advanced Imaging Research Center at UT Southwestern—Lucy Christie, Jeannie Baxter, Kelley Derner, Maida Tai, and Salvador Pena.
AUTHOR CONTRIBUTIONS
M.C.P., C.J.M., and J.M.P. designed research; E.P.H., M.C.P., S.B., C.J.M., and J.M.P. recruited patients;
E.P.H., C.E.H., J.L., J.R., G.D.R., C.R.M., and J.M.P. performed research; E.P.H., M.C.P., C.J.M., and
J.M.P. analyzed data; E.P.H., M.C.P., C.R.M., C.J.M., and J.M.P. wrote the paper.
DECLARATION OF INTERESTS
G.D.R. is an employee of GE Healthcare.
Received: July 20, 2020
Revised: October 25, 2020
Accepted: November 25, 2020
Published: December 18, 2020
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iScience, Volume 23
Supplemental Information
Imaging Acute Metabolic Changes in Patients
with Mild Traumatic Brain Injury
Using Hyperpolarized [1-13C]Pyruvate
Edward P. Hackett, Marco C. Pinho, Crystal E. Harrison, Galen D. Reed, Jeff
Liticker, Jaffar Raza, Ronald G. Hall, Craig R. Malloy, Surendra Barshikar, Christopher
J. Madden, and Jae Mo Park
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Supplementary Information
Transparent Methods
Human subjects and patient recruitment criteria
This study was approved by the local Institutional Review Board (IRB) of the (niversity of Texas
Southwestern Medical Center (IRB: ST( 072017-009; ClinicalTrials.gov NCT03502967). A written and
informed consent was obtained from all the study participants. The imaging protocol was fully compliant
with HIPPA regulation. Patients between the age of 18-60 years old in good general health were screened
for enrollment if they had sustained a mild injury to the head with no structural brain damage detected in
any CT scans within 10 days of trauma. Patients were further screened to exclude any confounding
comorbidities including a history of mental illness, history of substance abuse, and other major medical
conditions. Patients with known prior cardiovascular or neurological disease were excluded.
1
H/13C MRI protocol
All the studies were performed using a clinical SPINlab
polarizer (GE Healthcare, *aukesha,
*I (SA), a 3T wide-bore MR scanner (GE Healthcare, 750w Discovery), and a 13C/1
H dual-frequency (
1
H:
quadrature transmit/receive, 13C: quadrature transmit/8-channel phased array receive) nested-design
radiofrequency (RF) head coil (Clinical MR Solutions, Brookfield, *I (SA) (Ma et al., 2019). Both study
participants in this report were recruited at the Parkland Health and Hospital System Emergency
Department, a (T Southwestern-affiliated hospital in Dallas. *hen admitted, a full workup that includes
primary and secondary trauma survey, blood tests, and CT imaging of head and C-spine was initiated and
the CT scans showed no evidence of intracranial injury for either patient. The patients were released with
minor laceration care and brought in for research imaging. The subjects were imaged with a brain MR
protocol, which includes an injection of hyperpolarized [1-
13C]pyruvate (IND: 133229). The
hyperpolarized pyruvate solution was injected after a two-dimensional T2-weighted fluid attenuated
inversion recovery (FLAIR) scan. For 13C acquisition, a volume of 250-mM hyperpolarized pyruvate
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2
corresponding to a 0.1 mmol/kg dose was injected, followed by a 25-mL saline flush. The injection rate
was 5 mL/sec. A single-slice two-dimensional (2D) dynamic spiral chemical shift imaging (spiral CSI) was
used for dynamic imaging of hyperpolarized 13C signals (Park et al., 2012; 2019).
Each subject was scanned by a 3-plane 1
H gradient echo sequence (fast GRE; field of view [FOV]
= 32 cm, echo time [TE] = 1.1 msec, repetition time [TR] = 3.6 msec, slice thickness = 10 mm), followed
by a 2D axial T2-weighted fluid-attenuated and inversion recovery (T2w FLAIR; FOV = 24 cm ′ 24 cm,
TE = 144.7 ms, TR = 8 sec, inversion time [TI] = 2163 msec, slice thickness = 5 mm, number of slices =
16, flip-angle [FA] = 160o) as a basic assessment of axonal/parenchymal injuries that are typical
manifestations of trauma. Prior to 13C imaging, B0 inhomogeneity of the target 13C slice was minimized
using a single-voxel 1
H point-resolved spectroscopy (PRESS) sequence by adjusting shim gradients up to
1st order. For 13C metabolic imaging, 13C spiral chemical shift imaging (FOV = 24 cm ′ 24 cm, matrix size
= 16 ′ 16, slice thickness = 3 cm, variable FA up to 30o per timepoint, TR = 5 sec, 7 spatial interleaves of
spiral readout, spectral width = 814 Hz, 48 echoes) was used with a bolus injection of hyperpolarized [1-
13C]pyruvate. The 13C scan was initiated 5-sec after the start of injection. The 13C transmit power was precalibrated with a gadolinium-doped 0.4-M spherical [13C]HCO3
– phantom (diameter = 18 cm).After the 13C
acquisition, 1
H images were further acquired from each subject for mapping brain anatomy: T1-weighted
FLAIR (FOV = 24 cm ′ 24 cm, TE = 9.4 msec, TR = 3109 msec, inversion time [TI] = 1215 msec, slice
thickness = 5 mm, number of slices = 16, FA = 142o
), a 3D pseudo-continuous arterial spin labeling (ASL)
sequence with stack of spiral readouts (FOV = 24 cm ′ 24 cm ′ 16.2 cm, TE = 9.9 msec, TR = 6733 msec,
post-labeling delay = 2025 msec, 3 averages, matrix size = 512/interleave, 8 interleaves, flip angle = 90o
).
ASL was performed to identify vascular imbalance of the injured region (e.g., hypoperfusion).
Reconstruction of 13C data
All 13C data were reconstructed using MATLAB (Mathworks, Natick, MA). After apodization by
a 10-Hz Gaussian filter, the raw data were zero-filled in spectral and spatial domains by a factor of 4,