Contribution to a conference proceedings

FZJ-2024-06734

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png MRI Reconstruction with Regularized 3D Diffusion Model (R3DM)

Bangun, A.FZJ* ; Cao, Z.FZJ* ; Quercia, A.FZJ* ; Scharr, H.FZJ* ; Pfaehler, E.FZJ*

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.

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Contributing Institute(s):

1. Datenanalyse und Maschinenlernen (IAS-8)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)

Appears in the scientific report 2025

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