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Abstract #2768

A Unified Reconstruction Framework for Arbitrarily Accelerated MR Imaging

Zhongjian Jiang1, Kaicong Sun1, and Dinggang Shen1,2,3
1School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China, 2Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China, 3Shanghai Clinical Research and Trial Center, Shanghai, China

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction

Motivation: MRI reconstruction models are typically trained for specific acceleration factors. Multiple model variants are required to accommodate varying acceleration factors in practice.

Goal(s): We aim to develop a unified reconstruction model capable of handling arbitrarily accelerated MRI scans.

Approach: We first use self-supervised learning to obtain feature representations of fully-sampled and arbitrarily sub-sampled data. Then, we employ latent diffusion model to map feature representations of sub-sampled data to those of fully-sampled data.

Results: Experiments show that the use of feature transferring in our unified model brings an average performance gain of 3.86dB in PSNR for acceleration factors of 2x, 3x, and 4x.

Impact: We adopt feature representation transfer in the field of MRI reconstruction to address a practical issue largely overlooked by existing studies. Our adaptive reconstruction model can significantly simplify the deployment of MR reconstruction model and reduce the development costs.

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Keywords