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Abstract #1972

Deep Laplacian Pyramid Networks for Fast MRI Reconstruction with Multiscale T1 Priors

Xiaoxin Li1,2, Xinjie Lou1, Junwei Yang3, Yong Chen4, and Dinggang Shen2,5
1College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China, 2School of Biomedical Engineering, ShanghaiTech University, Shanghai, China, 3Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom, 4Case Western Reserve University, Cleveland, OH, United States, 5Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China

Recent studies explored the merits of using T1-weighted image (T1WI) as a guidance to reconstruct other undersampled MRI modalities via convolutional neural networks (CNNs). However, even aided by T1WI, the reconstruction of highly undersampled MRI data is still suffering from aliasing artifacts, due to the one-upsampling style of existing CNN architectures and not fully using the T1 information. To address these issues, we propose a deep Laplacian pyramid MRI reconstruction framework (LapMRI), which performs progressive upsampling while integrating multiscale prior of T1WI. We show that LapMRI consistently outperforms state-of-the-art methods and can preserve anatomical structure faithfully up to 12-fold undersampling.

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