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Abstract #3414

Investigation of automated brain region segmentation based on proton density maps

Janick Schmid1,2, Milena Capiglioni3, Nils Plähn1,2,4, and Jessica Bastiaansen1,2
1Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Bern, Switzerland, 2Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland, 3Support Center for Advanced Neuroimaging (SCAN), University of Bern, Bern, Switzerland, 4Graduate School for Cellular and Biomedical Sciences (GCB), University of Bern, Bern, Switzerland

Synopsis

Keywords: Segmentation, Segmentation, Morphometry, Brain, Quantitative Imaging

Motivation: Brain segmentation accuracy often relies on clear tissue contrast, yet standard T1-weighted images are susceptible to magnetic field inhomogeneities, and their contrast varies with acquisition parameters. We investigated proton density maps as a more robust alternative.

Goal(s): To test the use of proton density maps obtained from phase-cycled bSSFP data as input for automated brain region segmentation.

Approach: Proton density maps derived from phase-cycled bSSFP data were used as input for brain segmentation and compared with MP2RAGE-based segmentation.

Results: Proton-density-based segmentation agrees with the reference up to a dice coefficient of 0.93, which is promising but further optimization is needed.

Impact: Phase-cycled bSSFP-derived PD maps can be used for brain segmentation. This approach is independent of magnetic field strength, enabling more consistent contrast images for automatic brain segmentation tools.

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