MRI Reconstruction with Regularized 3D Diffusion Model (R3DM)

TL;DR

This paper introduces a novel 3D MRI reconstruction method using a regularized diffusion model that enhances image quality and fidelity from under-sampled data, outperforming existing techniques.

Contribution

The paper presents a new 3D MRI reconstruction approach that combines a regularized diffusion model with optimization, addressing limitations of 2D methods and improving reconstruction quality.

Findings

Improves image quality and reduces noise in 3D MRI reconstructions.

Effective with various undersampling patterns and datasets.

Outperforms tested competing methods in experiments.

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-of-distribution pre-trained data. In experiments, we show that our method improves upon tested competitors.