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@INPROCEEDINGS{Bangun:1033896,
      author       = {Bangun, Arya and Cao, Zhuo and Quercia, Alessio and Scharr,
                      Hanno and Pfaehler, Elisabeth},
      title        = {{MRI} {R}econstruction with {R}egularized 3{D} {D}iffusion
                      {M}odel ({R}3{DM})},
      reportid     = {FZJ-2024-06734},
      pages        = {1-8},
      year         = {2025},
      abstract     = {Magnetic 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.},
      month         = {Feb},
      date          = {2025-02-28},
      organization  = {IEEE/CVF Winter Conference on
                       Applications of Computer Vision,
                       Tucson, Arizona (USA), 28 Feb 2025 - 4
                       Mar 2025},
      cin          = {IAS-8},
      cid          = {I:(DE-Juel1)IAS-8-20210421},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5112},
      typ          = {PUB:(DE-HGF)8},
      url          = {https://juser.fz-juelich.de/record/1033896},
}