Abstract #2150

Semi-automatic Arterial Territorial Segmentation using ASL-based Dynamic 4D MRA without Vessel-Encoded Labeling

Soroush Heidari Pahlavian^1,2, Oren Geri³, Jonathan Russin², Dafna Ben-Bashat⁴, Xingfeng Shao^1,2, Samantha Ma^1,2, Songlin Yu⁵, Arun Amar², Danny J.J. Wang^1,2, and Lirong Yan^1,2

¹USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States, ²Department of Neurology, University of Southern California, Los Angeles, CA, United States, ³Razor Labs, Tel Aviv, Israel, ⁴Medicine & Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel, ⁵Peking Union Medical College Hospital, Beijing, China

Characterizing vascular territorial structures and hemodynamics from a single artery can provide crucial information for the assessment and treatment of cerebrovascular disorders such as arteriovenous malformations, Moyamoya disease, and aneurysms. In a different approach compared to vessel-selective MR angiography (MRA), here we presented a semi-automatic post-processing technique to segment vascular territories using pulsed arterial spin labeling 4D MRA. Our results demonstrated the feasibility of using 4D MRA in conjunction with arterial territorial segmentation to visualize vascular territories and quantify blood flow supply from individual arteries.

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