Abstract #0984

Model-Assisted Deep Learning-Based Reconstruction: Does the Model Help?

Yue Guan¹, Yudu Li^2,3, Ruihao Liu^1,2, Ziyu Meng¹, Yao Li¹, Yiping P. Du¹, and Zhi-Pei Liang^2,4

¹School of Biomedical Engineering, Institute for Medical Imaging Technology, School 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, ³National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁴Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Image ReconstructionDeep learning-based image reconstruction has two known practical issues: (a) sensitivity to data perturbations, and (b) poor generalization. An approach to addressing these issues is to use classical signal models to assist/constrain deep learning. This paper performs a systematic analysis of the role of signal models in model-assisted deep learning-based reconstruction. Our results show that signal models (e.g., subspace model or sparse model) can substantially reduce the sensitivity of deep learning-based reconstruction to data perturbations; they can also help improve generalization capability.

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