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Abstract #1772

Learn to Better Regularize in Constrained Reconstruction

Yue Guan1, Yudu Li2,3, Xi Peng4, Yao Li1, Yiping P. Du1, and Zhi-Pei Liang2
1Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 3Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 4Mayo Clinic, Rochester, MN, United States

Selecting good regularization parameters is essential for constrained reconstruction to produce high-quality images. Current constrained reconstruction methods either use empirical values for regularization parameters or apply some computationally expensive test, such as L-curve or cross-validation, to select those parameters. This paper presents a novel learning-based method for determination of optimal regularization parameters. The proposed method can not only determine the regularization parameters efficiently but also yield more optimal values in terms of reconstruction quality. The method has been evaluated using experimental data in three constrained reconstruction scenarios, producing excellent reconstruction results using the selected regularization parameters.

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