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.

通过使用多种扩散模型提取 k 空间域中的结构和细节特征，利用虚拟二进制模态掩码和倒金字塔结构来增强 MRI 重建质量，方法通过逐步细化逼近细节并采用级联训练数据分布来表示多尺度数据，从而有效地捕捉和呈现高分辨率的 MRI 图像。