Abstract #0735

MOdulation-Guided ENcoding (MOGEN) Scheme for Vessel-Encoded Arterial Spin Labeling Planning

Hongwei Li¹, Thomas W. Okell², Joseph G. Woods², Yang Ji³, Yuriko Suzuki², Ziqi Sun¹, Ying-Hua Chu⁴, Yi-Cheng Hsu⁴, He Wang^1,5, and Zhensen Chen^1,5

¹Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China, ²Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, ³Department of Electronic Engineering and Information Science, School of Information Science and Technology, University of Science and Technology of China, Hefei, China, ⁴MR Research Collaboration Team, Siemens Healthineers Ltd., Shanghai, China, ⁵MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, China

Synopsis

Keywords: Blood Vessels, Perfusion, ASL

Motivation: The OES method relies on Fourier-transform and does not consider VEASL spatial modulation information, making its effectiveness uncertain if the modulation deviates from a cosine function.

Goal(s): Propose a new method, MOGEN, that utilizes modulation information to further enhance SNR and labeling accuracy.

Approach: A dictionary containing all possible configurations is constructed first using the spatial modulation and matched it with the ideal encoding scheme to infer the VEASL parameters. We combined simulations and in-vivo experiments to demonstrate the effectiveness of the MOGEN across different scenarios.

Results: MOGEN improves SNR, accommodates a broader range of PCASL parameters, and enhances the vessel-decoding robustness.

Impact: Our study proposed a novel VEASL encoding method, MOGEN, guided by modulation information which is better suited to various PCASL parameter setting, further enhancing the SNR.

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