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Abstract #3823

Unsupervised Multi-Contrast MRI Super-Resolution with the Implicit Feature Sampling and Reciprocal Framework

Wenxuan Chen1, Yonghong Fan2, Zhongsen Li1, Chuyu Liu1, Yulin Wang3, Qiyuan Tian1, Dinggang Shen3,4,5, and Xiaolei Song1
1School of Biomedical Engineering, Tsinghua University, Beijing, China, 2Weixian College, Tsinghua University, Beijing, China, 3School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China, 4Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China, 5Shanghai Clinical Research and Trial Center, Shanghai, China

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, Super-Resolution, Unsupervised Learning

Motivation: Multi-contrast MRI super-resolution (MCSR) can effectively shorten the MRI acquisition time. Yet, existing deep learning-based approaches requiring paired low-resolution (LR) and high-resolution (HR) images for training are impractical in clinical settings.

Goal(s): We aim to propose an unsupervised model that can achieve MCSR without the need for ground-truth HR images.

Approach: We construct a network to generate HR images from its LR counterparts and reference images. Meanwhile, a cycle-consistency network and a reciprocal network are proposed to constrain the outputs.

Results: Experiments on two datasets demonstrate that our proposed model effectively restores HR images with clear anatomic details.

Impact: Our model facilitates multi-contrast MRI super-resolution in the absence of ground-truth HR images, which not only substantially reduces MRI acquisition time, but enables the obtaining of certain HR sequences that are difficult to acquire in clinical settings.

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Keywords