MRI Reconstruction with Regularized 3D Diffusion Model (R3DM)

2024-12-25

AI Summary

To address structural distortion and noise sensitivity in undersampled 3D MRI reconstruction, this work proposes the first end-to-end, physics-informed reconstruction framework integrating a 3D diffusion prior. Methodologically, a 3D diffusion model is embedded into k-space optimization, jointly enforcing data consistency and structural regularization; multi-modal training on both clinical and plant MRI datasets enhances generalizability. The key contribution is the first incorporation of a 3D diffusion prior into MRI reconstruction, coupled tightly with the physical forward model via joint optimization. Experiments demonstrate that our method achieves significant PSNR/SSIM improvements over state-of-the-art methods across high acceleration factors (4×–8×) and out-of-distribution scenarios. It consistently delivers superior noise suppression and faithful recovery of fine anatomical structures on both clinical and plant MRI data.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Multimodal Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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.

Problem

• MRI image enhancement
• 3D MRI quality improvement
• data incompleteness or poor quality

Innovation

• 3D Model Repair
• MRI Image Enhancement
• Incomplete Data Handling