1H magnetic resonance spectroscopic imaging (MRSI) is a powerful technique that reveals biochemical or metabolic information at different locations. However, for in-vivo human brain MRSI, obtaining high quality metabolic data is a difficult task with many technical challenges. The first is the low signal to noise ratio (SNR) and the resultant long scan time due to low concentrations of metabolites. Other than this, in-vivo brain MRSI also suffers from water and lipid signal contamination. As a result, brain MRSI is normally conducted with restricted spatial coverage.
A 3D volumetric MRSI sequence with automated quantification that addresses the aforementioned challenges was designed and implemented at 1.5T. To achieve whole-brain coverage, water and lipid suppression was performed with spectral-spatial excitation pulses with lipids further suppressed using adiabatic inversion recovery. Fast imaging was achieved with spiral encoding. To assess the repeatability of this method, both inter-subject and intra-subject reproducibility studies were performed. The inter-subject reproducibility study was conducted on 8 subjects with the largest regional coefficient of variance (CV) of 12.1%. The intra-subject reproducibility study was conduct on 2 subjects with each subject scanned 6 times. The largest intra-subject regional CVs for each subject were 12.7% and 6.6% respectively.
At 3T, the large chemical shift dispersion enabled a frequency-selective lipid suppression without affecting metabolite of interest. For robust water and lipid suppression, a scheme with multiple dual-band presaturation RF pulses was proposed and implemented. The flip-angles of the dual-band frequency-selective RF pulses were optimized to suppress water and lipids with a large range of T1s in the presence of 20% B1 inhomogeneity. The robust suppression of both water and lipids was demonstrated in both phantom and in-vivo studies.
With multi-coil data acquisition, MRSI can be further accelerated with parallel imaging technology. To further reduce scan time, the KSPA parallel reconstruction algorithm was applied to MRSI with spiral k-space trajectories. Exploiting the sparsity of the reconstruction matrix, the KSPA MRSI performed parallel reconstruction with significantly reduced memory requirement. In addition, by applying a single reconstruction matrix, it achieved much faster reconstruction than the iterative SENSE method.
|School Location:||United States -- California|
|Source:||DAI-B 69/10, Dissertation Abstracts International|
|Subjects:||Biomedical engineering, Electrical engineering|
|Keywords:||Brain, Magnetic resonance spectroscopic imaging, Volumetric|
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