Improving estimates of brain metabolite concentrations in MR spectroscopic imaging (MRSI) through MRI content

Juliana Fontolan; Danilo Pereira Sr.; Roberto Souza; Simone Appenzeller; Letícia Rittner

doi:10.1117/12.2513101

15 March 2019 Improving estimates of brain metabolite concentrations in MR spectroscopic imaging (MRSI) through MRI content

Juliana Fontolan, Danilo Pereira Sr., Roberto Souza, Simone Appenzeller, Letícia Rittner

Author Affiliations +

Proceedings Volume 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging; 109530V (2019) https://doi.org/10.1117/12.2513101
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Magnetic resonance spectroscopy (MRS) has been widely used for studying metabolic changes in rheumatic, neurodegenerative diseases and several other types of pathologies. Nevertheless, the accurate measurement of brain metabolite concentrations is still problematic and challenging, specially for multivoxel MR Spectroscopic Imaging (MRSI) data. There is a collection of artifacts and spectra are acquired from a region containing mixed tissues: white matter (WM), grey matter (GM) and cerebrospinal uid (CSF) composition. However, the studies are interested in analyzing metabolite changes in a particular brain tissue or structure. Therefore, our work proposes a pipeline for automatic selection of spectra of interest, a subset of spectra from MRSI acquisitions based on MRI content analysis and spectral quality metrics. The proposed pipeline helps to improve multivoxel spectroscopy analysis and estimates of metabolite concentrations, by eliminating spectra outside the tissue or structure of interest and identifying noisy spectra.

Conference Presentation

Citation Download Citation

Juliana Fontolan, Danilo Pereira Sr., Roberto Souza, Simone Appenzeller, and Letícia Rittner "Improving estimates of brain metabolite concentrations in MR spectroscopic imaging (MRSI) through MRI content", Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 109530V (15 March 2019); https://doi.org/10.1117/12.2513101

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