DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion Models

• URL: http://arxiv.org/abs/2405.05763v1
• Date: Thu, 9 May 2024 13:37:18 GMT
• Title: DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion Models
• Authors: Mengxiao Geng, Jiahao Zhu, Xiaolin Zhu, Qiqing Liu, Dong Liang, Qiegen Liu,
• Abstract summary: We propose a comprehensive detail-preserving reconstruction method using multiple diffusion models. The framework effective-ly represents multi-scale sampled data, taking into ac-count the sparsity of the inverted pyramid architecture. The proposed method was evaluated by con-ducting experiments on clinical and public datasets.
• Score: 7.601874398726257
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detail features of magnetic resonance images play a cru-cial role in accurate medical diagnosis and treatment, as they capture subtle changes that pose challenges for doc-tors when performing precise judgments. However, the widely utilized naive diffusion model has limitations, as it fails to accurately capture more intricate details. To en-hance the quality of MRI reconstruction, we propose a comprehensive detail-preserving reconstruction method using multiple diffusion models to extract structure and detail features in k-space domain instead of image do-main. Moreover, virtual binary modal masks are utilized to refine the range of values in k-space data through highly adaptive center windows, which allows the model to focus its attention more efficiently. Last but not least, an inverted pyramid structure is employed, where the top-down image information gradually decreases, ena-bling a cascade representation. The framework effective-ly represents multi-scale sampled data, taking into ac-count the sparsity of the inverted pyramid architecture, and utilizes cascade training data distribution to repre-sent multi-scale data. Through a step-by-step refinement approach, the method refines the approximation of de-tails. Finally, the proposed method was evaluated by con-ducting experiments on clinical and public datasets. The results demonstrate that the proposed method outper-forms other methods.

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