Jabrane Karkouri1,2,3, Magalie Viallon1, Arthur Coste1, Josef Pfeuffer4, Thomas Troalen2, Sylvain Grange1, Remy Prost1, Fabien Millioz1, Pierre Croisille1, and Helene Ratiney1
1Université de Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, Lyon, France, Lyon, France, 2SIemens Healthcare SAS, Saint-Denis, France, 3Wolfson Brain Imaging Center, University of Cambridge, Cambridge, United Kingdom, 4SIemens Healthcare SAS, Erlangen, Germany
Synopsis
In this abstract, a novel approach based
on spiral MRSI and under-sampling of the temporal dimension is applied in the
case of dynamic phosphorus at 3T. The goal was to assess mitochondrial capacity
of different individual muscles within the calf at once.
Introduction
Quantifying
energetic muscular metabolism and mitochondrial capacity is of crucial interest
to reveal muscular disorders, metabolic diseases or cardiovascular diseases.
31P spectroscopy is a well-known non-invasive way to monitor energetic metabolism
and dynamic concentrations of phosphorylated metabolites during exercise or
after, during recovery, and provides pieces of information on mitochondrial and
oxidative capacity. In clinical practice, mostly non-localized 31P spectroscopy
is done, preventing metabolic information from different individual muscles to
be measured, and averaged information results from the whole muscle as
collected at once by the surface coil used for the experiment. In this work, the
recently proposed MRSI approach detailed in [5] coupling spiral k-space
sampling and temporal under-sampling was applied on dynamic phosphorus
experiment, to evaluate its performance to spatially resolve 31P signal within
individual muscles of the calf at rest but also dynamically during controlled
exercise at 3T.Methods
The
sequence has been developed on a Siemens MR machine (figure 1). The prototype sequence has been tested on a
Prisma 3T scanner (max slew rate SR= 200T/m/sec and Max Gradient amplitude
Gmax=80mT/m). The total acquisition time Tacq depends on the number of spatial
interleave Nspat, the number of temporal interleaves Ntemp (repetition of the
spiral train with a delay equal to the spectroscopic temporal resolution) and
the repetition time TR:
Tacq = TR × Nspat × Ntemp
The method in [6] has been implemented (figure 2) as well. The SBS algorithm
enabling to define the irregular sampling has been implemented on the scanner
and outputs the temporal samples to acquire given the frequency support, the
vector size and the reduction factor for the under-sampling that the user defines
in a Special Card (on siemens MR machine windows interface).
31P experiments at rest were made using a 31P-CSI double phase encoding
sequence with a TR of 4s, a bandwidth of 2000Hz and a 8x8 matrices size. A
31P-CSI spiral sequence with the same acquisition parameters has been done at
rest. A reduction factor of 3 has been used for the under-sampled spiral CSI,
with a frequency support chosen such that it contains PCr, Pi and gamma-ATP
resonances. The resulting acquisition times were 2min56s for the fully temporal
spiral CSI sequence and 1min36s in the under-sampled case, which results in a
gain in the terms of acquisition time of 1.8. The two method fully and
under-sampled are compared in terms of signal to reconstruction error ratio
(SRER) which is computed according to the the fully and regularly sampled
signal.
Dynamic 31P experiments were done with an isometric flexion movement. The TR
used for dynamic experiments was of 2s for the fully sampled spiral CSI and
under-sampled for the under-sampled spiral CSI. The bandwidth for proper 31P
spectra reconstruction has been set to 2000Hz. The number of temporal
interleaves was of 4 for the fully sampled spiral CSI and 2 for the
under-sampled spiral CSI, which lead to a resolution between full reconstructed
spectra of 8s in the fully sampled case and 4s in the under-sampled case. The
data processing pipeline is given on figure 3.Results
Figure 4
shows PCr and Pi maps acquired at rest with the two methods fully spiral CSI
and under-sampled spiral CSI. Reconstructed spectra are shown as well for a
voxel of the image. The SRER in the highlighted voxel is of 19, 01 dB, which
corresponds to an energy of the reconstruction error 9 times smaller than the
energy of the signal. The SRER has also been averaged within all the voxels
containing soleus and gastrocnemius muscle and its value is 17, 31 dB.
Concerning dynamic 31P experiments, PCr
depletion was of 47 % in the soleus muscle and 34 % in the gastrocnemius muscle
for the fully temporal spiral CSI case and 38 % in the soleus muscle and 31 %
in the gastrocnemius muscle for the under-sampled spiral CSI case. Normalized
PCr by PCr + Pi evolution over time is shown in figure 5 for both methods in
the soleus and the gastrocnemius muscles. The sum of PCr and Pi is also given.
PCr recovery constant times were of 45,4s in the soleus and 34,7s in the
gastrocnemius for the fully sampled case and 44,2s in the soleus and 39,3s in
the gastrocnemius for the undersampled case.Discussion
The novel
fast spiral MRSI acquisition strategy has been presented and tested in-vivo on
phosphorus experiments. The very first in vivo experiments at rest to compare
the two methods resulted in satisfactory
SRER. For dynamic phosphorus applications, we are particularly interested in
PCr and Pi evolutions over time. The spectrum support has beenconsidered as sparse
allowing to apply the acceleration
method. Results showed that we were able to extract the recovery constant of PCr
even in the under-sampled case. In the case of an undersampling, the temporal
resolution of the dynamic experiment (time between each full 31P spectra) is
lower than in the fully sampled case, here with a contained amplification of
the noise.Acknowledgements
This work has been supported by
Siemens Healthineers and the LABEX PRIMES (ANR-11-LABX-0063), program ”Investissements
d’Avenir” (ANR-11-IDEX-0007) and carried out within the framework of France Life Imaging (ANR-11-INBS-0006). Some support was also received from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 801075.
References
1.
Posse, S., et al., MR spectroscopic imaging: principles and recent advances.
Journal of magnetic resonance imaging : JMRI, 2013. 37(6): p.1301-25.
2.
Adalsteinsson, E., et al., Volumetric spectroscopic imaging with spiral-based
k-space trajectories. Magnetic Resonance in Medicine, 1998.39(6): p.889-898
3.
Reeves, S.J. and L.P. Heck. Selection of observations in signal reconstruction.
IEEE Trans. Signal Proc. 1995; 43: 788-791
4.
Karkouri, J. et al., Irregular spiral acquisition for compressive sensing in
MRSI, ISMRM 2017
5. Karkouri, J et al., Time samples selection in spiral acquisition for sparse
magnetic resonance spectroscopic imaging, IEEE ICIP 2017
6. VALKOVIČ, Ladislav, CHMELÍK, Marek,
MEYERSPEER, Martin, et al. Dynamic
31P–MRSI using spiral spectroscopic imaging can map mitochondrial capacity in
muscles of the human calf during plantar flexion exercise at 7 T. NMR in Biomedicine, 2016, vol. 29, no
12, p. 1825-1834