Abstract #0751

Removal of Subcutaneous Lipid Signals from Spin-Echo 1H-MRSI Brain Data Using an FID Reference and Machine Learning

Yunpeng Zhang¹, Yibo Zhao^2,3, Yudu Li^2,3, Rong Guo^2,3, Yao Li¹, and Zhi-Pei Liang^2,3

¹School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 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

Spin-Echo (SE) MRSI can encode J-coupling information and is desirable for brain imaging applications. But it uses long TR, leading to long scan time and thus low resolution. Consequently, removal of subcutaneous lipid signals from low-resolution SE data is challenging. This paper presents a novel method to solve this problem. The proposed method uses a high-resolution FID reference and a neural network to transform it into SE signals, which are then used to construct a generalized-series model for lipid signal removal from the SE ¹H-MRSI data. The proposed method has been tested using in vivo data, producing very encouraging results.

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