MRI Reconstruction with Regularized 3D Diffusion Model (R3DM)
Journal:
arXiv
Published Date:
Dec 25, 2024
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.
