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Abstract #1034

In vivo voxel-wise parcellation of the human cerebral cortex using 3D MR fingerprinting (MRF) and supervised machine learning classification

Shahrzad Moinian1, Viktor Vegh1,2, and David Reutens1,2
1Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia, 2ARC Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia

The human cerebral cortex may be divided into functionally different, microarchitectonically distinct areas. While quantitative multi-modal MRI methods can reveal microstructural characteristics of cortical tissue, accurate microarchitectural parcellation of the entire cortex is yet to be attained. Here, we examine a novel method of automated in vivo voxel-wise cortical parcellation which exploits the area-specific microstructural information present in MR fingerprinting (MRF) signals. A Radial Basis Function Support Vector Machine (RBF-SVM) classifier, trained with a volume-based feature representation, achieved a macro-average area under the Receiver Operating Characteristic curve (ROC-AUC) of 0.83.

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