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

• URL: http://arxiv.org/abs/2411.03758v1
• Date: Wed, 06 Nov 2024 08:33:07 GMT
• Title: Sub-DM:Subspace Diffusion Model with Orthogonal Decomposition for MRI Reconstruction
• Authors: Yu Guan, Qinrong Cai, Wei Li, Qiuyun Fan, Dong Liang, Qiegen Liu,
• Abstract summary: Sub-DM is a subspace diffusion model that restricts the diffusion process via projections onto subspace as the k-space data distribution evolves toward noise. It circumvents the inference challenges posed by the com-plex and high-dimensional characteristics of k-space data. 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.
• Score: 13.418240070456987
• License:
• 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.

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