Yan Li1, Artan Kaso2, Ralph Noeske3, Angela Jakary1, Rolf F Schulte3, Christopher P Hess1, Janine M Lupo1, and Peder E.Z. Larson1
1University of California, San Francisco, CA, United States, 2University of Maryland, Baltimore, MD, United States, 3GE Healthcare, Munich, Germany
Synopsis
The purpose of this study was
to implement and optimize multi-voxel semi-LASER MRSI in brain regions that are
frequently used in clinical studies, such as deep gray structures and motor
cortex, within a clinically feasible time.
Introduction
MRS data obtained with the
use of ultra high-field strength (>=7T) MR scanners have considerable
advantages over 3T data, including significantly higher SNR, better spectral
resolution and more accurate quantification for both short TE single voxel
methods and multi-voxel MRSI. These improved capabilities allow for the
detection of separate glutamate (Glu), glutamine (Gln) and glutathione (GSH) resonances
and/or obtaining higher spatial resolution spectral data. Semi-LASER
localization, which can provide more uniform excitation, has been applied in
the multi-voxel MRSI mode to evaluate brain metabolism in the center of the frontal
brain at 7T (1, 2). The purpose of this study was to implement and optimize multi-voxel semi-LASER
MRSI in brain regions that are frequently used for clinical studies, such as deep
gray structures and motor cortex, within a clinically feasible scan time (5-10
minutes). Methods
Five healthy controls had MR
scans using a 32-channel receive-only array with a volume transmit head coil
(NOVA Medical, Wilmington, MA, United States) on a whole-body 7T GE MR950
scanner (GE Healthcare, Waukesha, WI, United States). Before spectral
acquisition, the manufacturer’s higher-order shimming procedure was performed. Multi-voxel
MRSI data were obtained using VAPOR water suppression and semi-LASER
localization with TR/TE=2500-3000/30 ms, matrix=48x48, FOV=220x220 mm, slice
thickness 15 mm, voxel size=5x5x15 mm, and an interleaved flyback applied in
the anterior/posterior (A/P) direction. To eliminate lipid signal, we applied very
selection suppression outer volume suppression (OVS) (3) and/or a spectrally selective adiabatic inversion
recovery pulse (4). The spectral data were combined and processed as described previously
(4), and then quantified by LCModel (5) using a simulated basis-set. Metabolite levels were
expressed relative to the creatine (Cr) peak. Results
Figure 1
illustrates an example of multi-voxel MRSI data from a healthy volunteer. Table 1 summarizes the ratios of
choline-containing compounds/Cr (Cho/Cr), N-acetyl-aspartate/Cr (NAA/Cr),
Glu/Cr, Gln/Cr, myo-inositol/Cr and GSH/Cr in the deep brain regions from all
the healthy controls. Because of the limited number of voxels with relative CRLB
< 20% for GABA within these structures, the ratio of GABA/Cr was not
included in the table. The localization approach used in this example excited a
rectangular volume of interest, which could have suppressed metabolic signals
that were close to the skull. In this case, the application of inversion
recovery pulses can be beneficial for measuring brain metabolism in the cortex (see
Figure 2). Discussion
Improving the sensitivity of
MRSI and increasing the number of metabolites that can be assessed is an
important advance for evaluating patients with neurological and psychiatric
diseases. This study has demonstrated the application of high-resolution
multi-voxel MRSI at 7T to obtain Glu, Gln, mI and GSH at the spatial resolution
of 0.5x0.5x1.5cm (0.375 cm3) within a clinical feasible
acquisition time. Among the deep brain structures, the caudate was the most
difficult to shim and had a larger linewidth, which may require more advanced
methods to achieve B0 homogeneity (6, 7). In future, we will evaluate differences in deep brain metabolism related
to age and gender, as well as optimize our methods to better assess brain
metabolism within the motor cortex. Conclusion
This study demonstrates the
feasibility of using a fast and high spatial resolution multi-voxel MRSI acquisition
at ultra high-field strength, which is an important advance in the application
of MRSI for evaluating patients with psychiatric and neurodegenerative diseases,
such as Huntington’s Disease.Acknowledgements
This
work was supported by NIH R01 CA127612, NIH R21 HD092660, NIH R01 R01NS099564
and a technology development research grant from GE
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