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Abstract #4769

SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for efficient and robust MR image reconstruction

Fang Liu1, Lihua Chen2,3, Richard Kijowski1, and Li Feng4

1Radiology, University of Wisconsin-Madison, Madison, WI, United States, 2Radiology, Southwest Hospital, Chongqing, China, 3Radiology, PLA 101st Hospital, Wuxi, China, 4Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States

The purpose of this work was to develop and evaluate a new deep-learning based image reconstruction framework, termed as Sampling-Augmented Neural neTwork with Incoherent Structure (SANTIS) for MR image reconstruction. Our approach combines efficient end-to-end CNN mapping with k-space consistency using the concept of cyclic loss to enforce data fidelity. Adversarial training is implemented for maintaining high quality perceptional image structure and incoherent k-space sampling is used to improve reconstruction accuracy and robustness. The performance of SANTIS was demonstrated for reconstructing vast undersampled Cartesian knee images and golden-angle radial liver images. Our study demonstrated that the proposed SANTIS framework represents a promising approach for efficient and robust MR image reconstruction at vast acceleration rate.

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