MRI Reconstruction with Regularized 3D Diffusion Model (R3DM)
This addresses the need for efficient 3D reconstruction in clinical and plant science MRI, though it appears incremental as it builds on existing diffusion models by extending them to 3D.
The paper tackled the problem of fast 3D-MRI reconstruction from under-sampled k-space data by proposing a regularized 3D diffusion model combined with optimization, resulting in improved image quality, reduced noise, and enhanced fidelity compared to competitors.
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-of-distribution pre-trained data. In experiments, we show that our method improves upon tested competitors.