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Abstract #2496

Improved Vessel-Encoded Dynamic Arterial Spin Labeling (VE-DASL) for Vascular Territory Mapping

Hongwei Li1, Peng Wu2, Weibo Chen2, He Wang1,3, and Zhensen Chen1,3
1Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China, 2Philips Healthcare, Shanghai, China, 3Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China

Synopsis

Keywords: Data Processing, Perfusion, ASL

Motivation: VE-DASL is promising in achieving fast vascular territory mapping by using short labeling duration and post-labeling delay, but the accuracy is limited, especially in the border zones.

Goal(s): To achieve a robust vascular territories separation using VE-DASL.

Approach: We adopted optimal encoding scheme and simulated the signal for each territory. The voxels that best matched the simulated signal were identified and their signal was used as the reference. The vascular territories were obtained using matrix inversion or correlation analysis.

Results: The proposed method achieved results comparable to VEASL and demonstrated the capability to differentiate the four vascular territories.

Impact: We improved VE-DASL by using OES and the proposed vessel-decoding method. This approach enabled us to achieve results comparable to VEASL while offering the potential for extension to more complex vascular scenarios.

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