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bioRxiv · 10.64898/2026.04.03.716409

Multi-Contrast MRI Inputs Enable Self-Consistent Tissue Segmentation & Robust Perivascular Space Identification

Gunter, J. L.·Preboske, G. M.·Persons, B.·Przybelski, S. A.·Schwarz, C. G.·Low, A.·Vemuri, P.·Petersen, R.·Jack, C. R.
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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 Alzheimers Disease Research Center (ADRC) are presented.

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Gunter, J. L., Preboske, G. M., Persons, B., Przybelski, S. A., Schwarz, C. G., Low, A., Vemuri, P., Petersen, R., Jack, C. R.. 2026-04-07. Multi-Contrast MRI Inputs Enable Self-Consistent Tissue Segmentation & Robust Perivascular Space Identification. https://doi.org/10.64898/2026.04.03.716409

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