DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion Models
<|reference_start|>DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion Models: 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.<|reference_end|>
@article{geng2024dp-mdm:,
    title={DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion
  Models},
    author={Mengxiao Geng, Jiahao Zhu, Xiaolin Zhu, Qiqing Liu, Dong Liang, Qiegen
  Liu},
    journal={arXiv preprint arXiv:2405.05763},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05763},
    primaryClass={cs.CV cs.AI}
}