基于扩散先验的脑部MRI超分辨率重建OA

Transformer-based MRI super-resolution methods offer strong global modeling capabilities but often overlook the role of deep prior constraints.To address this problem,a brain MRI super-resolution method based on diffusion priors is proposed,where a latent diffusion model generates structural priors to guide the Transformer in restoring fine details.A two-stage training strategy is adopted:the first stage constructs a content prior from ground-truth latent encodings to pretrain the reconstruction network;the second stage introduces diffusion-based priors and jointly optimizes the denoising and reconstruction processes under unsupervised conditions.Additionally,depthwise separable convolutions and permuted self-attention are employed to enhance modeling efficiency and expand the receptive field.Experiments on the IXI multi-modal MRI dataset(4×SR)demonstrate superior reconstruction quality and efficiency of the method over existing methods.