  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.
