Sub-DM:Subspace Diffusion Model with Orthogonal Decomposition for MRI Reconstruction

TL;DR

This paper introduces Sub-DM, a subspace diffusion model with orthogonal decomposition for MRI reconstruction, significantly improving speed and quality by efficiently handling complex k-space data through projections and wavelet-based strategies.

Contribution

The novel Sub-DM method restricts diffusion via subspace projections and orthogonal decomposition, enabling faster and more accurate MRI reconstruction compared to existing approaches.

Findings

Sub-DM achieves faster MRI reconstruction with high quality.

The method outperforms state-of-the-art techniques in experiments.

Orthogonal decomposition reduces information loss during diffusion.

Abstract

Diffusion model-based approaches recently achieved re-markable success in MRI reconstruction, but integration into clinical routine remains challenging due to its time-consuming convergence. This phenomenon is partic-ularly notable when directly apply conventional diffusion process to k-space data without considering the inherent properties of k-space sampling, limiting k-space learning efficiency and image reconstruction quality. To tackle these challenges, we introduce subspace diffusion model with orthogonal decomposition, a method (referred to as Sub-DM) that restrict the diffusion process via projections onto subspace as the k-space data distribution evolves toward noise. Particularly, the subspace diffusion model circumvents the inference challenges posed by the com-plex and high-dimensional characteristics of k-space data, so the highly compact subspace ensures that diffusion process requires only a few simple iterations to produce accurate prior information. Furthermore, the orthogonal decomposition strategy based on wavelet transform hin-ders the information loss during the migration of the vanilla diffusion process to the subspace. Considering the strate-gy is approximately reversible, such that the entire pro-cess can be reversed. As a result, it allows the diffusion processes in different spaces to refine models through a mutual feedback mechanism, enabling the learning of ac-curate prior even when dealing with complex k-space data. Comprehensive experiments on different datasets clearly demonstrate that the superiority of Sub-DM against state of-the-art methods in terms of reconstruction speed and quality.