Abstract #3697

Improved ASL in Water-Unsuppressed MRSI Using Generalized Series Modeling and Statistical Learning

Rong Guo^1,2, Ziyang Xu^2,3, Yudu Li^2,4,5, Yibo Zhao², Wen Jin^2,3, Ziyu Meng⁶, Yao Li⁶, Danny JJ Wang⁷, and Zhi-Pei Liang^2,3,5

¹Siemens Healthineers, St Louis, MO, United States, ²Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ³Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁴Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁵National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁶School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, ⁷Laboratory of FMRI technology (LOFT), USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States

Synopsis

Keywords: Arterial Spin Labelling, Arterial spin labelling

Motivation: Distortion-free ASL integrated with MRSI has been recently proposed. However, the ASL component requires several minutes of averaging to achieve sufficient SNR.

Goal(s): To improve the SNR of ASL thereby reducing its scan time.

Approach: In acquisition, a short-TE sampling of (k, t)-space was used to improve SNR efficiency. In processing, generalized series modeling and statistical learning were applied for reconstruction and denoising.

Results: The proposed method significantly enhanced the SNR of ASL, enabling a fourfold reduction in scan time to less than one minute. This enabled the acquisition of ASL at 2×2×2 mm³ and MRSI at 2×3×3 mm³ resolution within 8 minutes.

Impact: With the enhanced SNR and imaging speed, this integrated ASL and MRSI method offers a powerful multi-modal imaging tool for investigating cerebral blood flow and brain metabolism under healthy and disease conditions.

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