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Abstract #2746

Weakly supervised learning improves vascular territorial mapping of random vessel-encoded ASL

Yining He1 and Lirong Yan1,2
1Radiology, Northwestern University, Chicago, IL, United States, 2Neurology, University of Southern California, Los Angeles, CA, United States

Synopsis

Keywords: Arterial spin labelling, PerfusionThis study proposed a weakly supervised learning algorithm for vascular territorial mapping with rVE-ASL. The territory maps generated by the proposed deep learning (DL) method was compared with the territories from the conventional rVE-ASL method by visual inspection and F1 score. Our initial results showed that the DL method outperformed the conventional rVE-ASL method in the vascular territory mapping with improved detection of VA territory. The DL method also provided reliable vascular territorial maps with reduced numbers of encodings, significantly reducing rVE-ASL scan time. These findings suggest that DL could be an effective approach for vascular territorial mapping of rVE-ASL.

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Keywords