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Abstract #2026

Theoretical and experimental optimization of random vessel-encoded ASL to improve vascular territorial mapping and CBF quantification

Yining He1, Jianing Tang1, Tianrui Zhao1, Maria Gamez2, and Lirong Yan1
1Biomedical Engineering, Northwestern University, Evanston, IL, United States, 2Radiology, Northwestern University, Chicago, IL, United States

Synopsis

Keywords: Arterial Spin Labelling, Arterial spin labelling

Motivation: Planning-free random vessel-encoded ASL (rVE-ASL) greatly simplifies the vessel-selective ASL scan settings and shows great potential for vascular territorial imaging in clinical applications.

Goal(s): To theoretically and experimentally optimize rVE-ASL to establish an efficient and reliable rVE-ASL protocol

Approach: Simulation and in-vivo experiments were conducted to evaluate and optimize rVE-ASL in terms of labeling efficiency, total number of encoding steps, and reliability of vascular territorial and CBF measurements.

Results: Reliable territorial mapping and CBF measurements from both ICA and VA can be achieved by optimized rVE-ASL. At least 20 encoding steps are needed to achieve reliable territorial mapping and CBF measurements in rVE-ASL.

Impact: We have theoretically and experimentally optimized rVE-ASL, which can provide reliable vascular territorial mapping and CBF quantification. The rVE-ASL technique holds a potential to be a useful imaging tool for assessing vascular territorial alterations and collaterals in various clinical applications.

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Keywords