Multi-Contrast MRI Inputs Enable Self-Consistent Tissue Segmentation & Robust Perivascular Space Identification
Jeffrey L. Gunter, Gregory M. Preboske, Benjamin P. Persons, Scott A. Przybelski, Christopher G. Schwarz, Audrey Low, Prashanthi Vemuri, Ronald Petersen, Clifford R Jack · bioRxiv (Cold Spring Harbor Laboratory) · 2026
Different MRI image contrasts are designed to highlight various tissue properties and combining them allows extension of probabilistic segmentation beyond the commonly used "gray-white-CSF" models. This work describes a fully automated method that combines T1-weighted, T2-FLAIR, and conventional T2-weighted images to provide internal consistency across prediction of tissue segmentations including segmentation of superficial and deep gray matter, white matter hyperintensities, and MR-visible perivascular spaces. Results from 773 imaging datasets from 403 participants in the Mayo Clinic Study of Aging and Mayo Clinic Alzheimer's Disease Research Center (ADRC) are presented.