Sequence-phase optimal (SPO) [d̳e̳l̳t̳a̳]B₀ field control for lipid suppression and homogeneity for brain magnetic resonance spectroscopic imaging
Author(s)Arango, Nicolas(Nicolas S.)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
MetadataShow full item record
This work develops sequence-phase optimal (SPO) [delta]B₀ shimming methods to reduce lipid contamination and improve brain metabolite spectra in proton spectroscopic imaging. A rapidly reconfigurable 32-channel, local-multi-coil-shim-array is used to enhance lipid suppression and narrow metabolite linewidth in magnetic resonance spectroscopic imaging (MRSI) of the brain. The array is optimally reconfigured dynamically during each MRSI repetition period, first during the lipid-suppression phase, by widening the spectral gap between spatially separate lipid and metabolite regions, and then to narrow metabolite linewidth during readout, by brain-only [delta]B₀ homogenization. This sequence-phase-optimal (SPO) shimming approach is demonstrated on four volunteer subjects using a commercial 3T MRI outfitted with a 32-channel integrated RF receive and local multi-coil shim array. This proposed sequence-phase-optimal shimming significantly improves brain-metabolite MRSI in vivo, as measured by lipid suppression, brain metabolite chemical shift, and line widths. The time required to compute patient specific SPO shims negligibly impacted scan time. Sequence-phase-optimal shimming reduced lipid energy in the brain volume across four subjects by 88%, improved NAA FWHM by 23%, and dramatically reduced lipid ringing artifacts in quantified NAA and Glutamate metabolites, without increasing scan time or SAR.
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020Cataloged from PDF version of thesis. [d̳e̳l̳t̳a̳] in title on title page appears as upper case Greek letter.Includes bibliographical references (pages 33-35).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.