Eur J Nucl Med Mol Imaging (2017) 44:704–711
DOI 10.1007/s00259-016-3507-1
ORIGINAL ARTICLE
Assessment of muscle function using hybrid PET/MRI:
comparison of 18F-FDG PET and T2-weighted MRI
for quantifying muscle activation in human subjects
Bryan Haddock 1 & Søren Holm 1 & Jákup M. Poulsen 1 & Lotte H. Enevoldsen 1 &
Henrik B. W. Larsson 1 & Andreas Kjær 1 & Charlotte Suetta 1
Received: 3 May 2016 / Accepted: 29 August 2016 / Published online: 8 September 2016
# The Author(s) 2016
Abstract
Purpose The aim of this study was to determine the relationship between relative glucose uptake and MRI T2 changes in
skeletal muscles following resistance exercise using simultaneous PET/MRI scans.
Methods Ten young healthy recreationally active men (age
21 – 28 years) were injected with 18F-FDG while activating
the quadriceps of one leg with repeated knee extension exercises followed by hand-grip exercises for one arm.
Immediately following the exercises, the subjects were
scanned simultaneously with 18F-FDG PET/MRI and muscle
groups were evaluated for increases in 18F-FDG uptake and
MRI T2 values.
Results A significant linear correlation between 18F-FDG uptake and changes in muscle T2 (R2 = 0.71) was found. for both
small and large muscles and in voxel to voxel comparisons.
Despite large intersubject differences in muscle recruitment,
the linear correlation between 18F-FDG uptake and changes in
muscle T2 did not vary among subjects.
Conclusion This is the first assessment of skeletal muscle
activation using hybrid PET/MRI and the first study to demonstrate a high correlation between 18F-FDG uptake and
changes in muscle T2 with physical exercise. Accordingly, it
seems that changes in muscle T2 may be used as a surrogate
marker for glucose uptake and lead to an improved insight into
the metabolic changes that occur with muscle activation. Such
knowledge may lead to improved treatment strategies in
* Bryan Haddock
bryan.haddock@regionh.dk
1
Department of Clinical Physiology, Nuclear Medicine & PET,
Rigshospitalet Glostrup, Copenhagen University Hospital, Ndr.
Ringvej 57, DK2600 Glostrup, Denmark
patients with neuromuscular pathologies such as stroke, spinal
cord injuries and muscular dystrophies.
Keywords PET/MRI . Hybrid imaging . Muscle . T2 .
Exercise
Introduction
Skeletal muscle tissue accounts for about 40 % of the human
body mass and, given its central role in human mobility and
metabolic function, any deterioration in its contractile and
metabolic properties has a significant effect on human health.
Consequently, more attention has been given to the function of
human skeletal muscle in an attempt to improve the prognosis
and rehabilitation in large patient groups including those with
spinal cord injuries [1], muscular dystrophies [2], metabolic
dysfunction [3–5], hypertension [6], multiple sclerosis [7, 8]
and heart failure [9, 10], and aged individuals [4, 11, 12].
Helping these patient groups requires a detailed understanding of the mechanisms involved in muscle activation
and of the specific conditions that must be present for beneficial effects such as neuron growth, angiogenesis and stem cell
production to take place. Progress in muscle imaging has to a
large degree been due to improvements in PET and MRI that
can provide detailed 3D images of cumulative muscle activity
throughout the body [13–15]. This spatial information is difficult to obtain using surface electromyography (EMG) [16,
17]. With the recent availability of PET/MRI scanners, it is
now possible to obtain PET and MRI measurements simultaneously to fully evaluate the vascular and metabolic processes
involved in normal and dysfunctional muscle activity.
Both PET and MRI can produce 3D parametric images,
which have been shown to correlate with the intensity and
duration of muscle activation [13–15, 18–21]. At present,
Eur J Nucl Med Mol Imaging (2017) 44:704–711
18
F-FDG PET is the gold standard of the two modalities for
imaging cumulative skeletal muscle activation since it measures glucose uptake which has been shown to increase with
increasing muscle metabolism and activity [13, 14, 22–25].
MRI measurements of muscle activation are based on changes
in T2 values, which is affected by several biological factors
including intracellular and intercellular water volumes and
acidification [26]. A common assumption is that measured
increases in T2 values in muscle tissue after activation are a
result of oedema which has been shown to be the predominant
factor causing differences in muscle tissue T2 values [27, 28].
Although MRI has proven reliable in several studies [15, 21,
26], the lack of clarity as to the mechanisms responsible for
the changes in T2 values remains a hindrance. On the other
hand, MRI imaging offers several advantages including its
wide range of applications and the fact that it does not use
ionizing radiation. MRI also has a higher resolution and anatomic contrast than PET, allowing a more precise analysis of
muscle involvement as well as perfusion, fat infiltration and
water movement.
The purpose of the present study was, therefore, to use
PET/MRI to simultaneously measure 18F-FDG uptake and
T2 changes in activated skeletal muscle to determine if MRI
can consistently and accurately give 3D measurements of
muscle activation equivalent to those with 18F-FDG PET. To
the best of our knowledge, this is the first study in which such
a comparison has been made. A strong linear correlation between T2 changes and glucose uptake would indicate that MRI
could provide a helpful surrogate measurement in metabolic
research or a clinical tool for patients with neuromuscular or
metabolic dysfunction. The combination of the two modalities
could also help meet contemporary challenges in metabolic
research and training science such as investigating regulatory
mechanisms and improving the effectiveness of training and
rehabilitation. On the other hand, discrepancies between the
responses measured by the two modalities would open the
possibility of extracting complementary information
pertaining to muscle activation where different mechanisms
are measured with 3D spatial resolution.
705
Exercise intervention
The exercise intervention consisted of exercising the right
quadriceps and lower left arm muscles of a single limb in
order to study both small and large muscle groups, while the
same muscle groups of the contralateral limb were at rest. The
leg exercise consisted of eight sets of ten knee extension (KE)
repetitions with an individually determined repetition maximum load for ten repetitions (10 RM load). Hand-grip (HG)
contractions were performed using an Adidas professional
grip trainer and subjects were instructed to perform as many
contractions as possible in 30 s (Fig. 1). Each exercises was
followed by a rest period to produce a repeating KE–rest–
HG–rest paradigm of exactly 2 min. Prior to these experiments (1 – 2 weeks), the subjects were familiarized with the
KE machine, the seating of the training device (TecnoGym
Inc.) was adjusted, and the individual’s 10 RM load was determined for the KE exercise using the right leg only.
Testing procedure
On the testing day, MRI baseline resting scans were performed prior to exercising. Subjects then performed the eight
KE–rest–HG–rest sets of ten KE repetitions and 30 s of HG
contractions. After two training sets, 249 ± 3 MBq of 18FFDG was injected, and then the subjects completed the remaining six sets. During exercise great care was taken to ensure that muscles in the left leg and right arm were not used in
any way, and thus remained inactive. Accordingly, each individual’s contralateral limb acted as a resting control.
Immediately after the exercise intervention, the subject was
transported as quickly as possible in an MR compatible wheel
chair and positioned in the PET/MRI scanner using the preposition settings from the baseline scan.
PET/MRI data acquisition
Scanning and reconstruction were performed on a Siemens
Biograph mMR PET/MRI scanner (Siemens AG, Erlangen,
Germany; software version syngo MR B20P). MR spin-echo
Materials and methods
Subjects
Ten recreationally active young men (age 24 ± 2 years, BMI
22.9 ± 2 kg/m2) volunteered to participate in the present study.
None of the subjects had previously participated in systematic
resistance training. The Ethics Committee of the Capital
Region of Denmark approved the study (protocol no. H-12013-146) and the participants gave written informed consent
to participate in accordance with the principles of the
Declaration of Helsinki.
Fig 1 Exercise paradigm. For each set, subjects did ten knee extension
(KE) repetitions and 30 s of hand-grip (HG) exercises with a rest period
between such that the set took exactly 2 min. 18F-FDG was injected after
the KE repetitions in the second set while resting. The subject was moved
to the scanner after the HG exercises of the eighth set
706
Eur J Nucl Med Mol Imaging (2017) 44:704–711
and PET dynamic images were acquired simultaneously
centred over the mid-femur. Data acquisition was started as
soon as possible after exercise depending only on the time
required to position the subject in the scanner and acquire a
scout scan. T2 data were acquired using a turbo spin echo
sequence with TR 2,500 ms and two interleaved echo times
TE1 18 ms and TE2 88 ms. The leg data were acquired first
centred over the mid-femur with a 152 × 256 matrix, a pixel
size of 1.75 mm, ten transaxial slices of 8 mm and a scan time
of approximately 4 min. The arm data were then acquired
centred over the maximum cross section diameter of the right
arm with eight slices of 8 mm, a 152 × 256 matrix, a pixel size
of 1.95 mm and a scan time of 4 min.
18
F-FDG PET emission data were acquired in list mode
simultaneously with the T2 data using a single bed position
for 3 min with the same centring as the T2 acquisition. PET
images were reconstructed using 3D OSEM with three iterations, 21 subsets and 4 mm gaussian postreconstruction filter,
using attenuation maps created from MRI Dixon scans. The
reconstructed data had an output matrix of 512 × 512 and a
resulting voxel size of 1.4 × 1.4 × 2.0 mm which was resized
with nearest neighbour interpolation to match the T2 data for
analysis. After 18F-FDG PET and MRI T2 data acquisition, a
multibed PET acquisition was performed to visually review
the 18F-FDG distribution within the body.
Data analysis
18
F-FDG PET data and MRI T2 data are coregistered to ensure
identical tissue volumes for region of interest (ROI) and voxel
to voxel comparison. T2 maps were created from two images
with echo times of 18 ms (TE1) and 88 ms (TE2) using the
E2−T E1
formula T 2 ¼ lnðT E1 Timage
=T E2 imageÞ. To reduce the inclusion of
blood vessels and fat tissue, a maximum T2 value of 80 ms
and a minimum of 35 ms were used as thresholds for a voxel
to be included as muscle tissue. An analysis of muscle groups
in the leg and arm was performed using median values from
ROIs drawn to include, as much as possible, its entire volume.
ROIs drawn in the arm are referred to as either belonging to
‘extensor’ or ‘flexor’ muscle groups. Relative values for both
18
F-FDG uptake (relGU) and changes in T2 values (relΔT2)
were calculated using the equations::
relGU ¼
¼
SU V muscle −SU V re f
; relΔT 2
SU V re f
T 2muscle −T 2re f
T 2re f
ð1Þ
Here ‘muscle’ refers to the voxel or ROI of muscle and
‘ref’ is the reference muscle tissue ROI. In the arm, the reference muscle tissue was a large ROI including the extensor and
flexor muscles of the resting arm. In the leg, the reference
tissue ROI was drawn over the vastus muscle groups from
the quadriceps of the resting leg. Since the ratio relΔT2 normally has small values less than 0.5 as opposed to relGU
values, which range from 1 to 7, relΔT2 is reported in percent.
Statistical analysis
The correlation between muscle ROI relΔT2 and relGU
values was evaluated using linear regression with relGU as
the independent variable. In order to use linear regression it
is necessary to determine whether the covariance is the same
for all muscle tissue independent of the subject or the muscle
type. For this reason three ANCOVA analyses were performed
to determine if linear regression coefficients were independent
of the subject and muscle group, and whether the muscle was
from the arm or leg. The first ANCOVA tested for significant
differences in regression coefficients when all data was
subgrouped by muscle group, the second tested for significant
differences between subjects, and the last tested for significant
differences between arm and leg data. A similar regression
analysis was performed at the voxel level using the mean
relΔT2 for voxels of muscle tissue grouped by intervals of
relGU. Since most MRI kinetic studies link concentration
changes with the relaxation rate, R2 (R2 = 1/T2), as opposed
to the relaxation time constant, T2, relΔR2 was calculated in a
similar manner to the calculation shown in Eq. 1, and the
relationship between relΔR2 and relGU evaluated by linear
regression analysis. Since the exercise paradigm did not include the hamstring leg muscles, these muscle groups were
not included in the regression analysis.
Testing for significance was performed using a Student’s t
test with a threshold p value of <0.05. Data are reported as
means ± standard deviation (SD). The adjusted coefficient of
determination (R2) was used to evaluate the goodness of fit of
the data to a linear model. All image manipulations, calculations and statistical analyses were performed using scripts
created in MATLAB 2013b (MathWorks, Natick, MA).
Results
All ten subjects completed the training paradigm and the subsequent scanning. One subject’s data were excluded due to
technical difficulties during scanning and one subject’s arm
data were excluded due to artefacts. The average time from
stopping the exercises to starting the 18F-FDG PET/MRI scan
was 9.1 ± 3.8 min. Both 18F-FDG PET and T2-weighted MRI
scans measured higher intensities in the muscle groups of the
exercised quadriceps and exercised arm muscles than in the
non-activated muscle groups (Figs. 2, 3 and 4). The spatial
distribution of 18F-FDG activity and MRI T2 changes were
very similar, even in subjects in whom activation was inhomogeneous throughout the exercised arm or quadriceps
Eur J Nucl Med Mol Imaging (2017) 44:704–711
707
and leg data on the basis of both whole muscle ROIs and mean
voxel values (Fig. 5a, c). The linear regression of muscle ROI
relΔT2 (%) with relGU as a covariant (R2 = 0.71) gave:
relΔT 2 ¼ 8:58 relGU− 0:4
ð2Þ
The ANCOVA analysis showed that the regression parameters (slope and intercept) did not vary significantly between
subjects nor between arm and leg muscles. Similarly, there
were no significant differences between the muscle subgroups
with the exception of the rectus femoris muscle, which had a
significantly higher slope of 12. Linear regression of mean
voxel relΔT2 versus relGU (Fig. 5c) gave a similar slope to
Eq. 2, although with an intercept that was significantly lower.
The resulting regression equation (R2 = 0.99) was:
relΔT 2 ¼ 8:3 relGU− 3:6
ð3Þ
The correlation between mean relΔR2 values and glucose
uptake (Fig. 5d) was also linear. The regression equation
(R2 = 0.97) was:
relΔR2 ¼ −6:7 relGU þ 4:4
Fig. 2 Whole-body 18F-FDG PET image of subject after the exercise
intervention. The right quadriceps and lower left arm muscles used in
the exercise intervention show significantly higher glucose uptake than
the resting contralateral limbs
muscle (Fig. 4). Activation measurements (relΔT2 and relGU)
and control T2 measurements for each muscle group are presented in Table 1. There was a highly significant (p < 0.01)
linear correlation between the two measurements of activation, relΔT2 and relGU, when comparing all exercised arm
Fig. 3 MRI (relΔT2) and 18FFDG PET (relGU) images of legs
and arms after exercise. Top:
Cross sections of the mid-femurs
in the same subject after unilateral
exercise of the quadriceps of the
right leg. Bottom: Cross sections
of the forearms at maximum diameter in the same subject after
exercise of the left arm. For both
the arms and the legs, the images
show the relative increases in T2
values (relΔT2 as precentages)
and relative increases in SUV
values (relGU as ratios)
ð4Þ
In both muscle ROI and mean voxel comparisons, relΔT2
values did not increase significantly further for relGU values
exceeding a value of 4 (Fig. 5) though only three muscle ROIs
were in this range.
Mean muscle T2 values from control MRI scans obtained
prior to exercise were 49.2 ± 3.2 ms in the leg muscle and
significantly lower (41 ± 0.8 ms) in the arm muscle
(Table 1). There were no significant left–right differences.
All muscle groups in the resting leg showed a significant decrease in T2 values after training with a mean decrease of 5 %
(Table 1). The decrease in T2 values was less pronounced in
the hamstring muscle groups than the quadriceps of the resting
leg muscles, although not significantly so. The same muscles
had positive relGU values (mean 0.4 ± 0.1), which represents
a small but significantly elevated glucose uptake. Contrary to
resting leg muscles, muscles in the resting arm had higher
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Eur J Nucl Med Mol Imaging (2017) 44:704–711
Fig. 4 Response of muscle
groups to training as measured in
terms of relΔT2 and relΔGU. The
results from nine subjects for each
muscle group in exercised arm
and leg are shown. Leg
quadriceps muscles: RF rectus
femoris, VL vastus lateralis, VI
vastus intermedius, and VM
vastus medialus. Leg hamstring
muscles: AM adductor magnus,
ST semitendinosus, SM
semimembranosus, BFS biceps
femoris short-head, and BFL
biceps femoris long-head. Lower
arm muscle groups: Extensor
extensor group, and Flexor flexor
group
average T2 values after training compared to the control scan,
although the difference was not significant.
knowledge. The correlation was found to be consistent when
comparing groups of both small and large muscles. Only the
rectus femoris muscle had a relΔT2/relGU ratio moderately
higher than that of other muscle groups. The linear relationship was also consistent for a wide range of measured activities. Despite the controlled exercise paradigm, there was a
large variation in the magnitude of measured muscle activities
among subjects, muscle groups and even within muscles
(Fig. 5a, Table 1). Still, the relGU/relΔT2 correlation was
found to be consistent regardless of the magnitude of the measured activity even for voxel by voxel comparisons (Fig. 5c).
Lastly, this study also showed a decrease in resting leg muscle
T2 values after the subject had exercised the contralateral leg,
which has not been reported previously.
Discussion
To the best of our knowledge, this is the first simultaneous
PET/MRI study of muscle activation. Notably, our 18F-FDG
PET/MRI data confirms the results of previous MRI studies
that have shown increases in muscle T2 values [15, 20, 21] and
PET studies that have shown increases in glucose uptake after
muscle activation [13, 14, 18, 19]. More importantly, this
study showed a strong linear correlation between relGU and
relΔT2, which has not been investigated previously to our
Table 1
ROI muscle relΔT2, relGU and measured T2 values from nine subjects (means ± standard deviation)
Muscle group
Active limb
relΔT2
(%)
relGU
Resting limb
T2 (ms)
relΔT2
(%)
relGU
Control After
exercise
Leg quadriceps
muscles
Leg quadriceps
muscles
Lower arm muscle
groups
a
T2 (ms)
Control After
exercise
Vastus lateralis
26 ± 10
3.2 ± 1.4 46 ± 2
57 ± 5
Vastus intermedius
Vastus medialus
Rectus femoris
Semimembranosus/semitendinosus
Biceps femoris long-head/biceps
femoris short-head
Adductor magnus
Extensor
Flexor
24 ± 11
22 ± 11
42 ± 8
−1 ± 2
−3 ± 3
3.1 ± 1.3
2.9 ± 1.3
3.7 ± 0.6
0.2 ± 0.1
0.4 ± 0.2
45 ± 1
47 ± 3
48 ± 4
47 ± 2
45 ± 2
56 ± 5
55 ± 6
64 ± 4
45 ± 2
43 ± 2
0.4 ± 4a −0.1 ± 0.1a
−1.8 ± 2a 0.1 ± 0.0a
3.1 ± 8a 0.1 ± 0.1a
23 ± 10 0.0 ± 0.1
−2.3 ± 3
0.3 ± 0.1
−1.8 ± 6
0.4 ± 0.1
48 ± 2
46 ± 1
50 ± 3
58 ± 5
47 ± 2
46 ± 3
45 ± 3
44 ± 1
47 ± 3
55 ± 4
44 ± 2
45 ± 4
0.1 ± 8
15 ± 11
19 ± 10
0.4 ± 0.1 47 ± 3
1.6 ± 1.0 40 ± 2
2.3 ± 1.4 41 ± 2
45 ± 4
46 ± 4
48 ± 3
−2.0 ± 8
0.4 ± 0.2 47 ± 4
0 ± 4a −0.1 ± 0.1a 42 ± 2
0.5 ± 4a 0.0 ± 0.1a 41 ± 3
45 ± 5
43 ± 3
44 ± 5
Values calculated using the median of the reference ROI. Therefore, the values for these muscles individually are not zero
Eur J Nucl Med Mol Imaging (2017) 44:704–711
709
Fig. 5 Relationships between relΔT2 and relGU in the exercised limbs of
all subjects. a Values from whole muscle ROIs for all individual muscle
groups (as shown in Fig. 3) of exercised arms and legs. b The same ROI
data as in a grouped into the relGU intervals shown and plotted as the
mean ROI relΔT2 for each interval. c, d Comparisons including all voxels
of muscle tissue showing mean relΔT2 (c) and mean relΔR2 (d) for the
relGU intervals shown. All error bars represent ±standard deviations
The finding of a correlation between an oedema marker
(relΔT2) and a metabolism marker (relGU) corresponds well
with the findings of prior research in muscle physiology and
glucose transport [3, 23, 29, 30]. An explanation for the correlation could be that both were blood flow-dependant under the
conditions of this study. In a maximal dynamic exercise bout
with a small muscle mass, such as the exercise regime in the
present study, similar levels of muscle hyperaemia are reached
regardless of the arterial blood oxygen content and partial pressure, venous blood pH, femoral vein blood temperature or
haemoglobin desaturation [30]. Instead, the immediate vascular
response depends on the total workload or, more specifically,
the total number of active motor units and the amount of tension
they create [23, 29, 31–33]. Thus, the linear correlation between
glucose uptake and oedema the linear correlation of each with
muscle activation could stem from the linear correlation between blood flow and the degree of muscle activation [29, 30,
33]. MRI and PET measure muscle activity, not by the degree of
neural activation or by metabolism itself, but by the associated
influx of water and glucose, respectively, and therefore perfusion plays a role in the measurement mechanism of both modalities. Muscular activity cannot, however, be assumed to be
homogeneous throughout a given muscle [8, 34]. As also found
in previous studies, our results show that even though quadriceps were activated using a strictly controlled paradigm, there
was a large variation in the measured activation among subjects,
among individual muscle groups and even within a muscle
[19–21, 35]. However, the correlation between relΔT2 and
relGU was found to be consistent in voxel by voxel comparisons
and displayed the same inhomogeneous activation patterns.
For an endurance exercise or exercise involving several of
the body’s main muscle groups, blood flow would be regulated
by many mechanisms [23, 29, 30] and the relΔT2/relGU ratios
would possibly differ from those found in this study. Most
importantly, elevated glucose uptake in active muscle cells
could continue even after oedema reaches a maximum. This,
we believe, is a plausible explanation as to why increases in the
relΔT2 values reach a plateau after relGU values exceed a value
of around four (Fig. 5). This effect may indicate that muscle
activation of longer duration would be more difficult to quantify and the resulting relationship between glucose uptake and
oedema would be more complex. The point at which relΔT2
reaches a maximum may differ between muscles. Enocson
et al. found that during KE exercises MRI changes in the rectus
femoris are are greater than those in the vastus muscles at maximum work load [21]. This may explain the slightly elevated
relΔT2/relGU slope of the rectus femoris muscle shon in the
ANCOVA analysis. On the other hand, the relationship
remained consistent across a large range of intensities of measured muscle activity and a large range in muscle group size.
This indicates that the relΔT2/relGU correlation would apply to
a variety of training paradigms of shorter endurance.
18
F-FDG PET and T2-weighted MRI are increasingly being
used to measure muscle activation, but there are still factors that
710
need to be considered. First, it is important to remember that
both are a cumulative measurements of muscle activity preceding image acquisition and give little information about how the
activity varied over time. The summative time frame includes,
though to a lesser degree, the period after exercise as well. Our
analysis of 18F-FDG PET data assumed, as in previous studies
[11, 36], that the levels of radioactivity in plasma are relatively
low after the exercise and that the period between the end of
exercise and the start of the scan has a minor impact on relative
skeletal muscle tissue tracer concentrations. Exercise was
stopped 12 min after18F-FDG bolus injection and scanning
was started on average 20 min injection. During the period from
12 to 20 min after injection, arterial blood activity in an average
subject will fall from about 30 % of its maximum to about 20 %
[37] which is a relatively small amount, but not negligible.
Second, the MR data do not represent muscle tissue during
exercise but rather 9 min after exercise. The elevated T2 values
from muscle activation are known to change after stopping
exercise and have been found to decay back to preactivation
levels in a somewhat exponential fashion with an approximate
half-life of 12 min [15]. To date, no accurate model describing
the evolution of T2 values from the end of training to full recovery has been proposed, which means measurements cannot
be properly corrected for the time delay.
A third consideration is the choice of reference tissue. The
finding that even resting muscle does not have static T2 values
after activation of other muscle groups is a new consideration
affecting the accuracy of the calculation of relΔT2. Using a
control scan prior to exercise so each voxel serves as its own
reference to calculate relΔT2 would be more representative than
defining an ROI in resting muscle as the reference tissue. In this
study, using the same reference tissue for calculations of relΔT2
and relGU was important in accurately determining the correlation between the two measures and, for this reason, relΔT2 is
calculated using the same reference ROI as the relGU, instead of
using the control scan. Confirming the state of ‘resting’ muscle
tissue in calculations of relGU faces the same challenge, and a
control scan immediately before exercise begins is not a plausible solution. Several groups have used ROIs drawn over bone
marrow as the reference tissue or preferably blood samples to
alleviate this problem when using 18F-FDG PET [36]. In the
present study, the inactivity of resting muscles used as the reference tissue was controlled using T2-weighted MRI data from
before to after exercise. However, standardization of reference
measurements for the calculation of relGU may give more accurate quantification of the correlation between oedema and
relGU for comparison between subjects.
The strengths of 18F-FDG PET and MRI are not just the possibility for subjects to move freely during exercise, but that the
resulting activity can be measured throughout the body, including
deeper muscles, and mapped in 3D with high resolution.
Traditionally, muscle activation has been measured with EMG
which provides good temporal resolution during exercise but
Eur J Nucl Med Mol Imaging (2017) 44:704–711
has a limited ability to differentiate between muscle groups and
cannot easily measure deep muscles [17]. Given the inhomogeneity of activity between muscles and within a muscle group,
surface measurements alone provide information of less value
than 3D mapping. In studies in which it is important to reveal
changes in magnitude or strategy over time, and which therefore
require EMG, images acquired with PETor MRI would be able to
supply complementary spatial information revealing intramuscular and intermuscular heterogeneity. The wide range of applications of MRI also allows mapping of muscle to be combined with
other MRI measurements of interest such as spectroscopy,
diffusion-weighted imaging and blood oxygen level-dependent
imaging.
Given that skeletal muscle tissue plays a major role in metabolic regulation [3–5], it is important to be able to evaluate
in vivo skeletal muscle activation and metabolism and thereby
increase our understanding of musculoskeletal function. The
results of the present study contribute to this growing field of
metabolic research by characterizing skeletal muscle tissue
using the correlation between oedema (relΔT2) and metabolism
(relGU) during its activation. The high correlation between the
two parameters provides a new tool for analysing muscle activation allowing MRI to be used as a surrogate measure in a
broad range of research areas such as sports performance, and
the effects of lifestyle, ageing and rehabilitation. This approach
also allows PET/MRI to be used as a diagnostic tool to differentiate muscle groups in patients having a relΔT2/relGU correlation that differs from that of healthy subjects.
Conclusion
This is the first PET/MRI study of muscle activation. Analysis
of the data showed a significant linear correlation between
18
F-FDG uptake and changes in muscle T2 in groups of both
small and large muscles. Accordingly, it seems that changes in
muscle T2 may be used as a surrogate marker for glucose uptake.
Compliance with ethical standards
Conflicts of interest None.
Ethical approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the
institutional and/or national research committee and with the principles
of the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.
Informed consent Informed consent was obtained from all individual
participants included in the study.
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
Eur J Nucl Med Mol Imaging (2017) 44:704–711
References
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
Bjerkefors A, Carpenter MG, Cresswell AG, Thorstensson A.
Trunk muscle activation in a person with clinically complete thoracic spinal cord injury. J Rehabil Med. 2009;41:390–2.
Altamirano F, Perez CF, Liu M, Widrick J, Barton ER, Allen PD,
et al. Whole body periodic acceleration is an effective therapy to
ameliorate muscular dystrophy in mdx mice. PLoS One. 2014;9:
e106590.
Fujimoto T, Kemppainen J, Kalliokoski KK, Nuutila P, Ito M,
Knuuti J. Skeletal muscle glucose uptake response to exercise in
trained and untrained men. Med Sci Sports Exerc. 2003;35:777–83.
Myers J, Prakash M, Froelicher V, Do D, Partington S, Atwood JE.
Exercise capacity and mortality among men referred for exercise
testing. N Engl J Med. 2002;346:793–801.
Reichkendler MH, Auerbach P, Rosenkilde M, Christensen AN,
Holm S, Petersen MB. Exercise training favors increased insulinstimulated glucose uptake in skeletal muscle in contrast to adipose
tissue: a randomized study using FDG PET imaging. Am J Physiol
Endocrinol Metab. 2013;305:E496–506.
Pinto RR, Polito MD. Haemodynamic responses during resistance
exercise with blood flow restriction in hypertensive subjects. Clin
Physiol Funct Imaging. 2016;36:407–13.
Campbell E, Coulter EH, Mattison PG, Miller L, McFadyen A,
Paul L. Physiotherapy rehabilitation for people with progressive
multiple sclerosis: a systematic review. Arch Phys Med Rehabil.
2016;97:141.e3–151.e3.
Rudroff T, Kindred JH, Koo PJ, Karki R, Hebert JR. Asymmetric
glucose uptake in leg muscles of patients with multiple sclerosis
d u r i n g w a l k i n g d e t e c t e d b y [ 1 8 F ] - F D G P E T / C T.
NeuroRehabilitation. 2014;35:813–23.
Nayor M, Vasan RS. Preventing heart failure. Curr Opin Cardiol.
2015;30:543–50.
Dhakal BP, Malhotra R, Murphy RM, Pappagianopoulos PP,
Baggish AL, Weiner RB, et al. Mechanisms of exercise intolerance
in heart failure with preserved ejection fraction: the role of abnormal peripheral oxygen extraction. Circ Heart Fail. 2015;8:286–94.
Rudroff T, Kalliokoski KK, Block DE, Gould JR, Klingensmith
WC, Enoka RM. PET/CT imaging of age- and task-associated differences in muscle activity during fatiguing contractions. J Appl
Physiol. 2013;114:1211–9.
Hvid L, Aagaard P, Justesen L, Bayer ML, Andersen JL, Ørtenblad
N, et al. Effects of aging on muscle mechanical function and muscle
fiber morphology during short-term immobilization and subsequent
retraining. J Appl Physiol. 2010;109:1628–34.
Pappas GP, Olcott EW, Drace JE. Imaging of skeletal muscle function using (18)FDG PET: force production, activation, and metabolism. J Appl Physiol. 2001;90:329–37.
Yokoyama I, Inoue Y, Moritan T, Ohtomo K, Nagai R. Simple
quantification of skeletal muscle glucose. J Nucl Med. 2003;44:
1592–8.
Fisher MJ, Meyer RA, Adams GR, Foley JM, Potchen EJ. Direct
relationship between proton T2 and exercise intensity in skeletal
muscle MR images. Invest Radiol. 1990;25:480–5.
Farina D, Holobar A, Merletti R, Enoka RM. Decoding the neural
drive to muscles from the surface electromyogram. Clin
Neurophysiol. 2010;121:1616–23.
Enoka RM, Duchateau J. Inappropriate interpretation of surface
EMG signals and muscle fiber characteristics impedes progress
on understanding the control of neuromuscular function. J Appl
Physiol. 2015;119:1516–8.
711
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
Bojsen-Møller J, Losnegard T, Kemppainen J, Viljanen T,
Kalliokoski KK, Hallén J. Muscle use during double poling evaluated by positron emission tomography. J Appl Physiol. 2010;109:
1895–903.
Kalliokoski KK, Boushel R, Langberg H, Scheede-Bergdahl C,
Ryberg AK, Døssing S, et al. Differential glucose uptake in quadriceps and other leg muscles during one-legged dynamic submaximal knee-extension exercise. Front Physiol. 2011;2:75.
Kinugasa R, Kawakami Y, Sinha S, Fukunaga T. Unique spatial
distribution of in vivo human muscle activation. Exp Physiol.
2011;96:938–48.
Enocson AG, Berg HE, Vargas R, Jenner G, Tesch PA. Signal
intensity of MR-images of thigh muscles following acute openand closed chain kinetic knee extensor exercise – Index of muscle
use. Eur J Appl Physiol. 2005;94:357–63.
Richter EA, Derave W, Wojtaszewski JF. Glucose, exercise and
insulin: emerging concepts. J Physiol. 2001;535:313–22.
Jensen TE, Richter EA. Regulation of glucose and glycogen metabolism during and after exercise. J Physiol. 2012;590:1069–76.
Romijn J, Gastaldelli A, Horowitz J, Endert E, Wolfe R. Regulation
of endogenous fat and carbohydrate metabolism in relation to exercise intensity and duration. Am J Physiol. 1993;265:380–91.
Van Loon LJ, Greenhaff PL, Constantin-Teodosiu D, Saris WH,
Wagenmakers AJ. The effects of increasing exercise intensity on
muscle fuel utilisation in humans. J Physiol. 2001;536:295–304.
Patten C, Meyer RA, Fleckenstein JL. T2 mapping of muscle.
Semin Musculoskelet Radiol. 2003;7:297–305.
Li K, Dortch RD, Welch EB, Bryant ND, Buck AKW, Towse TF,
et al. Multi-parametric MRI characterization of healthy human
thigh muscles at 3.0 T – relaxation, magnetization transfer, fat/water, and diffusion tensor imaging. NMR Biomed. 2014;27:1070–84.
Giri S, Chung Y-C, Merchant A, Mihai G, Rajagopalan S, Raman
SV, et al. T2 quantification for improved detection of myocardial
edema. J Cardiovasc Magn Reson. 2009;11:56.
Joyner MJ, Casey DP. Regulation of increased blood flow
(hyperemia) to muscles during exercise: a hierarchy of competing
physiological needs. Physiol Rev. 2015;95:549–601.
Calbet JAL, Lundby C. Skeletal muscle vasodilatation during maximal exercise in health and disease. J Physiol. 2012;590:6285–96.
Tschakovsky ME, Rogers AM, Pyke KE, Saunders NR, Glenn N,
Lee SJ. Immediate exercise hyperemia in humans is contraction
intensity dependent: evidence for rapid vasodilation. J Appl
Physiol. 2004;96:639–44.
VanTeeffelen JW, Segal SS. Effect of motor unit recruitment on
functional vasodilatation in hamster retractor muscle. J Physiol.
2000;524(Pt 1):267–78.
Hoelting BD, Scheuermann BW, Barstow TJ. Effect of contraction
frequency on leg blood flow during knee extension exercise in
humans. J Appl Physiol. 2001;91:671–9.
Kinugasa R, Watanabe T, Ijima H, Kobayashi Y, Park HG, Kuchiki
K, et al. Effects of vascular occlusion on maximal force, exerciseinduced T2 changes, and EMG activities of quadriceps femoris
muscles. Int J Sports Med. 2006;27:511–6.
Kindred JH, Ketelhut NB, Benson JM, Rudroff T. FDG-PET detects non-uniform muscle activity in the lower body during human
gait. Muscle Nerve. 2016. 10.1002/mus.25116.
Rudroff T, Kindred JH, Kalliokoski KK. [18F]-FDG positron emission tomography – an established clinical tool opening a new window into exercise physiology. J Appl Physiol. 2015;118:1181–90.
O’Sullivan F, Kirrane J, Muzi M, O’Sullivan JN, Spence AM,
Mankoff DA, et al. Kinetic quantitation of cerebral PET-FDG studies without concurrent blood sampling: statistical recovery of the
arterial input function. IEEE Trans Med Imaging. 2010;29:610–24.