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Abstract #3612

An Information Theoretical Framework for Machine Learning Based MR Image Reconstruction

Yudu Li1,2, Yue Guan3, Ziyu Meng2,3, Fanyang Yu2,4, Rong Guo1,2, Yibo Zhao1,2, Tianyao Wang5, Yao Li3, and Zhi-Pei Liang1,2
1Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 2Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 3Institute for Medical Imaging Technology (IMIT), School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 4Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 5Department of Radiology, The Fifth People's Hospital of Shanghai, Shanghai, China

Machine learning (ML) based MR image reconstruction leverages the great power and flexibility of deep networks in representing complex image priors. However, ML image priors are often inaccurate due to limited training data and high dimensionality of image functions. Therefore, direct use of ML-based reconstructions or treating them as statistical priors can introduce significant biases. To address this limitation, we treat ML-based reconstruction as an initial estimate and use an information theoretical framework to incorporate it into the final reconstruction, which is optimized to capture novel image features. The proposed method may provide an effective framework for ML-based image reconstruction.

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