Citations 1
Multi-Contrast MRI Inputs Enable Self-Consistent Tissue Segmentation & Robust Perivascular Space Identification
Gunter, Jeffrey L. , Preboske, Gregory M., Persons, Benjamin D., Przybelski, Scott A. , Petersen, Ronald, Jack, Clifford R, Gunter, Jeffrey L , Persons, Benjamin, Schwarz, Christopher G. , Low, Audrey , Vemuri, Prashanthi , Jack, Clifford R.
No container title available | April 7, 2026 | Volume: No volume available | Issue: No issue available | Pages: No page available
Abstract 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.