Unsupervised individual-specific in vivo parcellation of the human cerebral cortex using magnetic resonance fingerprinting

Motivation: In vivo MR image resolution limitations hinder precise characterization of individual gray matter microstructure. Addressing this unmet need can particularly enhance neurosurgery precision and assist neurological disorder monitoring. Goal(s): We aimed to investigate the feasibility of developing an unsupervised method for individual-specific voxel-wise cortical mapping by extending the MR fingerprinting (MRF) residual analysis framework. Approach: We employed k-shape clustering to leverage the wealth of cortical area-specific microstructural information in voxel-wise MRF residual timeseries for training an unsupervised model. Results: High sensitivity (75.7%) and specificity (88.3%) of our method demonstrated the feasibility of unsupervised in vivo microstructural cortical mapping using on MRF residual signals. Impact: Our unsupervised MR fingerprinting residual-based method offers a transformative approach to individual-specific human cortical mapping, potentially enhancing neurosurgical precision and neurological disorder diagnosis. This lays the foundation for exploring novel questions in microstructural neuroimaging and cerebral cortex developments in individuals.