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Ultra-high resolution brain metabolite mapping at 7 T by short-TR Hadamard-encoded FID-MRSI.

Authors :
Hangel, Gilbert
Strasser, Bernhard
Považan, Michal
Heckova, Eva
Hingerl, Lukas
Boubela, Roland
Gruber, Stephan
Trattnig, Siegfried
Bogner, Wolfgang
Source :
NeuroImage. Mar2018, Vol. 168, p199-210. 12p.
Publication Year :
2018

Abstract

MRSI in the brain at ≥7 T is a technique of great promise, but has been limited mainly by low B 0 /B 1 + -homogeneity, specific absorption rate restrictions, long measurement times, and low spatial resolution. To overcome these limitations, we propose an ultra-high resolution (UHR) MRSI sequence that provides a 128×128 matrix with a nominal voxel volume of 1.7×1.7×8 mm 3 in a comparatively short measurement time. A clinically feasible scan time of 10–20 min is reached via a short TR of 200 ms due to an optimised free induction decay-based acquisition with shortened water suppression as well as parallel imaging (PI) using Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration (CAIPIRINHA). This approach is not limited to a rectangular region of interest in the centre of the brain, but also covers cortical brain regions. Transversal pulse-cascaded Hadamard encoding was able to further extend the coverage to 3D-UHR-MRSI of four slices (100×100×4 matrix size), with a measurement time of 17 min. Lipid contamination was removed during post-processing using L2-regularisation. Simulations, phantom and volunteer measurements were performed. The obtained single-slice and 3D-metabolite maps show the brain in unprecedented detail (e.g., hemispheres, ventricles, gyri, and the contrast between grey and white matter). This facilitates the use of UHR-MRSI for clinical applications, such as measurements of the small structures and metabolic pathologic deviations found in small Multiple Sclerosis lesions. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10538119
Volume :
168
Database :
Academic Search Index
Journal :
NeuroImage
Publication Type :
Academic Journal
Accession number :
128542469
Full Text :
https://doi.org/10.1016/j.neuroimage.2016.10.043