MRI Reconstruction with Regularized 3D Diffusion Model (R3DM)

Arya Bangun, Zhuo Cao, Alessio Quercia, Hanno Scharr, Elisabeth Pfaehler; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 700-710

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