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Abstract #1849

Deep learning segmentation of small blood vessels and vessel density mapping based on high resolution black blood MRI

Steve Mendoza1, Zidong Yang1, Jesse Lamas1, Kay Jann1, Michael Harrington1, John Ringman1, Yonggang Shi1, and Danny Wang1
1USC, Los Angeles, CA, United States

Synopsis

Keywords: Analysis/Processing, Analysis/Processing, Vascular Biomarker

Motivation: Cerebral small vessel disease (cSVD) is linked to cognitive decline in older populations. Robust imaging of the small vessels can serve as a biomarker for cognitive decline.

Goal(s): We aim to build a cerebral small vessel segmentation model robust to motion and image quality based on high resolution black blood MRI.

Approach: We train a nnUNet model using in-house high quality data with simulated motion artifacts and evaluated trained model in a cohort of elderly persons at risk of cSVD.

Results: The trained model showed higher performance than Jerman filter, and vessel density in the hippocampus was correlated with cognitive scores.

Impact: A robust cerebral small vessel density estimation method can help for large-scale analysis of black blood MRI data to look for possible biomarkers of early cognitive decline. Our pipeline will be shared with the community.

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Keywords