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Abstract #4638

Fast and Accurate Brain Tissue Segmentation with Polarity Categorization (POLCAT)

Steven Kecskemeti 1,2 and Andrew L Alexander 3,4

1 Waisman Center, University of Wisconsin, Madison, WI, United States, 2 Radiology, University of Wisconsin, Madison, WI, United States, 3 Medical Physics, University of Wisconsin, WI, United States, 4 Psychiatry, University of Wisconsin, Madison, United States

Intensity-based brain tissue segmentation algorithms rely on post-hoc image intensity at a single point along the relaxation recovery curve of MPRAGE exams, making them very sensitive to unexpected signal variations such as the spatial heterogeneity of radio-frequency (RF) coil sensitivities. This work develops a novel, robust and efficient method for brain tissue segmentation that relies on intrinsic properties such as T1 and is insensitive to variations in RF receiver coil bias. The method assigns the tissue class according to the sign of the real signal intensity after voxel-wise complex-multiplication of inversion recovery images with different inversion times.

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