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001033896 037__ $$aFZJ-2024-06734
001033896 1001_ $$0P:(DE-Juel1)184644$$aBangun, Arya$$b0$$ufzj
001033896 1112_ $$aIEEE/CVF Winter Conference on Applications of Computer Vision$$cTucson, Arizona$$d2025-02-28 - 2025-03-04$$gWACV 2025$$wUSA
001033896 245__ $$aMRI Reconstruction with Regularized 3D Diffusion Model (R3DM)
001033896 260__ $$c2025
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001033896 520__ $$aMagnetic Resonance Imaging (MRI) is a powerful imaging technique widely used for visualizing structures within the human body and in other fields such as plant sciences. However, there is a demand to develop fast 3D-MRI reconstruction algorithms to show the fine structure of objects from under-sampled acquisition data, i.e., k-space data. This emphasizes the need for efficient solutions that can handle limited input while maintaining high-quality imaging. In contrast to previous methods only using 2D, we propose a 3D MRI reconstruction method that leverages a regularized 3D diffusion model combined with optimization method. By incorporating diffusion-based priors, our method improves image quality, reduces noise, and enhances the overall fidelity of 3D MRI reconstructions. We conduct comprehensive experiments analysis on clinical and plant science MRI datasets. To evaluate the algorithm effectiveness for under-sampled k-space data, we also demonstrate its reconstruction performance with several undersampling patterns, as well as with in- and out-ofdistribution pre-trained data. In experiments, we show that our method improves upon tested competitors.
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001033896 7001_ $$0P:(DE-Juel1)199019$$aCao, Zhuo$$b1$$ufzj
001033896 7001_ $$0P:(DE-Juel1)188471$$aQuercia, Alessio$$b2$$ufzj
001033896 7001_ $$0P:(DE-Juel1)129394$$aScharr, Hanno$$b3$$ufzj
001033896 7001_ $$0P:(DE-Juel1)191494$$aPfaehler, Elisabeth$$b4$$ufzj
001033896 8564_ $$uhttps://juser.fz-juelich.de/record/1033896/files/MRI_Reconstruction_with_Regularized_3D_Diffusion_Model_R3DM.pdf$$yRestricted
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001033896 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129394$$aForschungszentrum Jülich$$b3$$kFZJ
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001033896 9141_ $$y2025
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