中南民族大学学报（自然科学版）2026，Vol.45Issue(2)：202-211,10.DOI:10.20056/j.cnki.ZNMDZK.20250840
基于扩散先验的脑部MRI超分辨率重建
Brain MRI super-resolution reconstruction based on diffusion priors
摘要
Abstract
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.关键词
MRI超分辨率/扩散先验/置换自注意力/深度可分离卷积Key words
MRI super-resolution/diffusion prior/permuted self-attention/depthwise separable convolution分类
信息技术与安全科学引用本文复制引用
熊承义,曹雨轩,高志荣..基于扩散先验的脑部MRI超分辨率重建[J].中南民族大学学报（自然科学版）,2026,45(2):202-211,10.基金项目
多谱信息处理技术国家重点实验室基金资助项目(6142113210303) （6142113210303）
中央高校基本科研业务费专项资金资助项目(CZZ25010) （CZZ25010）
