用残差误差迁移加速MRI超分辨重建，4步完成且细节清晰。
MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting
- 将残差误差迁移融入扩散过程，实现高效高保真重建。
- 仅需4次采样，单切片重建时间低于1秒，比传统方法快20倍。
- 适合追求快速高质MRI重建的临床与科研人员使用。
本研究提出一种残差误差迁移机制，显著减少采样步骤的同时保留关键解剖细节，从而加速MRI重建。我们构建了基于扩散模型的超分辨新框架Res-SRDiff，将残差误差迁移整合进前向扩散过程，通过对齐降质高分辨率（HR）与低分辨率（LR）分布实现高效重建。在超高场脑部T1 MP2RAGE图谱和前列腺T2加权图像上评估该方法，对比双三次插值、Pix2pix、CycleGAN及基于视觉变换器骨干的去噪扩散概率模型（TM-DDPM），采用峰值信噪比（PSNR）、结构相似性指数（SSIM）、梯度幅值相似性偏差（GMSD）和学习感知图像块相似性（LPIPS）等定量指标。结果表明，Res-SRDiff在两个数据集上均显著优于所有对比方法（p值<<0.05），在仅4次采样下实现高保真恢复，单切片重建时间低于1秒，远快于传统TM-DDPM约20秒/切片。定性分析显示，该模型有效保留了脑部和盆腔MRI图像中的精细解剖结构与病灶形态。本研究证明Res-SRDiff是一种高效准确的MRI超分辨方法，大幅提升了计算效率与图像质量，可增强临床MRI工作流程并推动医学影像研究发展。
原文摘要 · Abstract (English)
Objective:This study introduces a residual error-shifting mechanism that drastically reduces sampling steps while preserving critical anatomical details, thus accelerating MRI reconstruction. Approach:We propose a novel diffusion-based SR framework called Res-SRDiff, which integrates residual error shifting into the forward diffusion process. This enables efficient HR image reconstruction by aligning the degraded HR and LR distributions.We evaluated Res-SRDiff on ultra-high-field brain T1 MP2RAGE maps and T2-weighted prostate images, comparing it with Bicubic, Pix2pix, CycleGAN, and a conventional denoising diffusion probabilistic model with vision transformer backbone (TM-DDPM), using quantitative metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), gradient magnitude similarity deviation (GMSD), and learned perceptual image patch similarity (LPIPS). Main results: Res-SRDiff significantly outperformed all comparative methods in terms of PSNR, SSIM, and GMSD across both datasets, with statistically significant improvements (p-values<<0.05). The model achieved high-fidelity image restoration with only four sampling steps, drastically reducing computational time to under one second per slice, which is substantially faster than conventional TM-DDPM with around 20 seconds per slice. Qualitative analyses further demonstrated that Res-SRDiff effectively preserved fine anatomical details and lesion morphology in both brain and pelvic MRI images. Significance: Our findings show that Res-SRDiff is an efficient and accurate MRI SR method, markedly improving computational efficiency and image quality. Integrating residual error shifting into the diffusion process allows for rapid and robust HR image reconstruction, enhancing clinical MRI workflows and advancing medical imaging research. The source at:https://github.com/mosaf/Res-SRDiff
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