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Abstract #0751

Denoising ASL Images Using Distribution Remapping-Based Deep Learning

Ziyang Xu1,2, Rong Guo1,3, Ziwen Ke1,4, Yudu Li1,5,6, Yibo Zhao1, Wen Jin1,2, Ziyu Meng4, Yao Li4, and Zhi-Pei Liang1,2
1Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana Champaign, Urbana, IL, United States, 2Department of Electrical and Computer Engineering, University of Illinois, Urbana Champaign, Urbana, IL, United States, 3Siemens Medical Solutions USA, Inc, St Louis, MO, United States, 4School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 5National Center for Supercomputing Applications, University of Illinois, Urbana Champaign, Urbana, IL, United States, 6Department of Bioengineering, University of Illinois, Urbana Champaign, Urbana, IL, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Arterial spin labelling, Denoising, Diffusion Model

Motivation: ASL images often have low SNR. Improving their SNR can enhance the practical utility of ASL in various brain imaging applications.

Goal(s): To develop a deep learning-based ASL denoising method with very limited training data available.

Approach: To generate diverse sets of ASL training images for deep-learning denoiser for special ASL sequences, we employed Schrödinger-Bridge-based networks to map the large public ASL datasets to match both intensity and noise distributions of target ASL sequence. The training data was used to train deep-learning models for ASL denoising.

Results: The proposed method was evaluated using simulation and in vivo experimental data, showing excellent denoising performance.

Impact: Our proposed method addresses the issue of limited target ASL training datasets for deep learning-based ASL denoising and demonstrates excellent denoising performance. It can be generalized for the practical utility in both research and clinical applications.

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Keywords