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Abstract #3818

Denoising MRSI Data Using Subspace Model-Assisted Langevin Dynamics with Side Information

Wen Jin1,2, Ziyang Xu1,2, Yudu Li1,3,4, Yibo Zhao1, Rong Guo1,5, Yao Li6,7, and Zhi-Pei Liang1,2
1Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 2Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 3Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 4National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 5Siemens Medical Solutions USA, Inc., St Louis, MO, United States, 6School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 7Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction

Motivation: Enhancing the signal-to-noise (SNR) of MRSI signals will improve detection sensitivity for clinical and research applications.

Goal(s): To leverage recent advances in machine learning to improve the SNR of MRSI.

Approach: A physics-based machine learning method was proposed for denoising MRSI data, incorporating side information such as PD, T1, T2, QSM, and MWF. A deep conditional generative model was trained to capture the relationship between water and metabolite signals. Denoised signals were obtained via subspace-assisted Langevin dynamics.

Results: The method was validated using both simulated and in vivo data obtained from healthy subjects and patients with tumor or stroke, demonstrating substantial noise reduction.

Impact: This proposed method provides an effective way to enhance the sensitivity of MRSI using physics-based machine learning incorporating side information. The method may further enhance the practical utility of MRSI for research and clinical applications.

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Keywords