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Abstract #3971

Unconditional Diffusion Model for 3D MRI Artifact Removal and Detail Refinement

Haowen Deng1, Zihao Zhu1, Lin Zhang2, Mingwen Yang2, Zuozhen Lan2, Meimei Yang2, Shuting Wang2, Jungang Liu2, and Han Zhang1,3,4
1School of Biomedical Engineering , ShanghaiTech University, Shanghai, 201210, China, Shanghai, China, 2Department of Radiology, Xiamen Children's Hospital, Children's Hospital of Fudan University at Xiamen, Xiamen 361006, Fujian, China, Xiamen, China, 3Shanghai Clinical Research and Trial Center, Shanghai, 201210, China, Shanghai, China, 4State Key Laboratory of Advanced Medical Materials and Devices, Shanghai, 201210, China, Shanghai, China

Synopsis

Keywords: AI Diffusion Models, AI/ML Image Reconstruction

Motivation: A considerable number of images being marred by motion artifacts. These artifacts substantially compromise the utility of MRI in clinical diagnostics and scientific research.

Goal(s): To develop a robust and generalizable tool for infant brain MRI artifact reduction.

Approach: We introduce an Artifact Removal (AR) method with a Detail Refinement (DR) module. The AR model employs an unconditional diffusion process trained solely on artifact-free images to improve generalization, while the DR module minimizes residual discrepancies to ensure high structural fidelity.

Results: Our method effectively eliminates motion artifacts while preserving the structural integrity and fidelity of the images, surpassing the performance of popular methods.

Impact: We propose a motion artifact reduction method based on unconditional DPM with a supervised fine-tuning module, DR. This approach demonstrates significant accuracy and robustness. Our method is highly valuable to neuroscience and clinical studies on existing and future large-scale datasets.

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