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Abstract #1294

Quantifying Cerebral Small Vessel Geometry with Self-Supervised Shape-Aware 3D U-Net on 7T TOF MRI: Associations with Age and Cognitive Function

Jianing Tang1,2, Zhiwei Deng3, Elizabeth Joe4, Helena Chui4, Yonggang Shi3, and Lirong Yan1,2
1Department of Radiology, Northwestern University, Chicago, IL, United States, 2Department of Biomedical Engineering, Northwestern University, Chicago, IL, United States, 3Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States, 4Department of Neurology, University of Southern California, Los Angeles, CA, United States

Synopsis

Keywords: Analysis/Processing, Blood vessels

Motivation: Aging and cerebral vascular disease can both contribute to structural alterations of intracranial vessels.

Goal(s): Our study aims to evaluate the role of vessel geometry of cerebral small vessels in an aged cohort with aging, cognitive function, and vascular risk factors

Approach: Vessel density, mean diameter and length were automatically extracted from high resolution TOF using shape-aware 3D segmentation with local contrast guided attention

Results: All derived vascular matrics are associated with cognitive function and highly correlated with body mass index (BMI). Vessel mean diameter is associated with pulse pressure and hyperlipidemia. Vessel density and mean length are related with reduced GM perfusion.

Impact: Quantitative morphological measures of cerebral small vessels can be effectively segmented using deep learning, which can be used as sensitive markers of aging, cognitive dysfunction, and brain vascular function

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Keywords