Spatiospectral image processing workflow considerations for advanced MR spectroscopy of the brain

Leon Y. Cai; Stephanie N. Del Tufo; Laura Barquero; Micah D'Archangel; Lanier Sachs; Laurie E. Cutting; Nicole Glaser; Simona Ghetti; Sarah S. Jaser; Adam W. Anderson; Lori C. Jordan; Bennett A. Landman

doi:10.1117/12.3005391

2 April 2024 Spatiospectral image processing workflow considerations for advanced MR spectroscopy of the brain

Leon Y. Cai, Stephanie N. Del Tufo, Laura Barquero, Micah D'Archangel, Lanier Sachs, Laurie E. Cutting, Nicole Glaser, Simona Ghetti, Sarah S. Jaser, Adam W. Anderson, Lori C. Jordan, Bennett A. Landman

Author Affiliations +

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 129260R (2024) https://doi.org/10.1117/12.3005391
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Magnetic resonance spectroscopy (MRS) is one of the few non-invasive imaging modalities capable of making neurochemical and metabolic measurements in vivo. Traditionally, the clinical utility of MRS has been narrow. The most common use has been the "single-voxel spectroscopy" variant to discern the presence of a lactate peak in the spectra in one location in the brain, typically to evaluate for ischemia in neonates. Thus, the reduction of rich spectral data to a binary variable has not classically necessitated much signal processing. However, scanners have become more powerful and MRS sequences more advanced, increasing data complexity and adding 2 to 3 spatial dimensions in addition to the spectral one. The result is a spatially- and spectrally-variant MRS image ripe for image processing innovation. Despite this potential, the logistics for robustly accessing and manipulating MRS data across different scanners, data formats, and software standards remain unclear. Thus, as research into MRS advances, there is a clear need to better characterize its image processing considerations to facilitate innovation from scientists and engineers. Building on established neuroimaging standards, we describe a framework for manipulating these images that generalizes to the voxel, spectral, and metabolite level across space and multiple imaging sites while integrating with LCModel, a widely used quantitative MRS peak-fitting platform. In doing so, we provide examples to demonstrate the advantages of such a workflow in relation to recent publications and with new data. Overall, we hope our characterizations will lower the barrier of entry to MRS processing for neuroimaging researchers.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Leon Y. Cai, Stephanie N. Del Tufo, Laura Barquero, Micah D'Archangel, Lanier Sachs, Laurie E. Cutting, Nicole Glaser, Simona Ghetti, Sarah S. Jaser, Adam W. Anderson, Lori C. Jordan, and Bennett A. Landman "Spatiospectral image processing workflow considerations for advanced MR spectroscopy of the brain", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129260R (2 April 2024); https://doi.org/10.1117/12.3005391

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