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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