A Novel Vascular Risk Scoring Framework for Quantifying Sex-Specific Cerebral Perfusion from 3D pCASL MRI
Sneha Noble, Neelam Sinha, Vaanathi Sundareshan, Thomas Gregor Issac

TL;DR
This study develops a novel framework using 3D pCASL MRI and deep learning to quantify sex-specific cerebral perfusion patterns and vascular risk, enabling personalized assessment of hypoperfusion in healthy individuals.
Contribution
The paper introduces a new supervoxel-based CNN approach for sex classification and a vascular risk score derived from normative perfusion profiles, advancing personalized neurovascular assessment.
Findings
Achieved 95% accuracy in sex classification using supervoxel features.
Identified distinct regional CBF differences between sexes.
Quantified individual perfusion deficits with a personalized vascular risk score.
Abstract
The influence of sex and age on cerebral perfusion is recognized, but the specific impacts on regional cerebral blood flow (CBF) and vascular risk remain to be fully characterized. In this study, 3D pseudo-continuous arterial spin labeling (pCASL) MRI was used to identify sex and age related CBF patterns, and a vascular risk score (VRS) was developed based on normative perfusion profiles. Perfusion data from 186 cognitively healthy participants (89 males, 97 females; aged 8 to 92 years), obtained from a publicly available dataset, were analyzed. An extension of the 3D Simple Linear Iterative Clustering (SLIC) supervoxel algorithm was applied to CBF maps to group neighboring voxels with similar intensities into anatomically meaningful regions. Regional CBF features were extracted and used to train a convolutional neural network (CNN) for sex classification and perfusion pattern analysis.…
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Taxonomy
TopicsAdvanced MRI Techniques and Applications · Cardiac Imaging and Diagnostics · MRI in cancer diagnosis
