Estimating regional cerebral blood flow using resting-state functional MRI via machine learning.

ABSTRACT:

Background

Perfusion MRI is an important modality in many brain imaging protocols, since it probes cerebrovascular changes in aging and many diseases; however, it may not be always available.

New method

We introduce a new method that seeks to estimate regional perfusion properties using spectral information of resting-state functional MRI (rsfMRI) via machine learning. We used pairs of rsfMRI and arterial spin labeling (ASL) images from the same individuals with normal cognition and mild cognitive impairment (MCI), and built support vector machine models aiming to estimate regional cerebral blood flow (CBF) from the rsfMRI signal alone.

Results

This method demonstrated higher associations between the estimated CBF and actual CBF (ASL-CBF) at the total lobar gray matte

SUBMITTER: Chand GB 

PROVIDER: S-EPMC7216635 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature