用10步采样实现MRI超分辨率，恢复更清晰的脑和前列腺图像。
MRI super-resolution in ten sampling steps using a diffusion bridge model
- 通过扩散桥模型将低分辨与高分辨图像分布连接，从真实数据初始化而非随机噪声。
- 在脑和前列腺数据上均达到最高峰值信噪比（27.66~27.87 dB）和结构相似性（0.96~0.80）。
- 仅需10步采样即可生成高质量图像，适合临床快速成像场景。
MRI具有优异的软组织对比度，但扫描时间长易引发患者不适和运动伪影，导致空间分辨率与扫描时间之间的权衡。基于扩散的超分辨率（SR）可从低分辨率（LR）输入重建高分辨率（HR）图像，但通常需要大量采样步骤，并从不适用于图像修复的高斯先验开始。我们开发了一种高效扩散框架，直接从低分辨率数据重建高分辨率MRI。提出超分辨率扩散桥模型（SR-DBM），将超分辨率视为在低分辨率与高分辨率图像分布间的随机传输过程。通过均值回复随机微分方程的Doob h变换，使过程在两端分别锚定于配对的高、低分辨率图像，从实际测量的解剖结构初始化重建，而非高斯噪声。通过仅10步采样的确定性逆轨迹，由网络预测每一步的干净图像。在7T脑部T1 MP2RAGE图和盆腔T2加权前列腺图像上，对比九种方法，使用PSNR、SSIM、GMSD和LPIPS评估。SR-DBM在两个数据集上均取得最高PSNR（脑：27.66±1.52 dB，前列腺：27.87±2.29 dB）、最高SSIM（脑：0.96±0.02，前列腺：0.80±0.05）和最低GMSD（脑：7.96±1.86，前列腺：8.38±1.44），且所有结果经双侧威尔科克森符号秩检验（霍尔姆校正）均显著优于其他方法（p<0.05）。最强基线模型SR-EMamba排名第二。定性分析显示，SR-DBM残差最小，精细结构和病灶保留最佳。
原文摘要 · Abstract (English)
Objective. MRI provides excellent soft-tissue contrast, but long acquisition times can cause patient discomfort and lead to motion artifacts, forcing a trade-off between spatial resolution and scan time. Diffusion-based super-resolution (SR) reconstructs high-resolution (HR) images from low-resolution (LR) inputs, but typically needs many sampling steps and initializes from a Gaussian prior ill-suited to image restoration. We developed an efficient diffusion framework that reconstructs HR MRI directly from LR data. Approach. We propose super-resolution diffusion bridge model (SR-DBM), a super-resolution diffusion bridge model that casts SR as a stochastic transport between the LR and HR image distributions. Through a Doob's h-transform of a mean-reverting stochastic differential equation, SR-DBM pins the process to the paired HR and LR images at its endpoints, initializing reconstruction from the measured anatomy rather than from Gaussian noise. The HR image is recovered by a deterministic reverse trajectory in which a network predicts the clean image at each of only ten sampling steps. We evaluated SR-DBM on ultra-high-field 7T brain T1 MP2RAGE maps and pelvic T2-weighted prostate images against nine comparison methods using PSNR, SSIM, GMSD, and LPIPS. Main results. SR-DBM attained the highest PSNR and SSIM and the lowest GMSD on both datasets (brain: 27.66+-1.52 dB, 0.96+-0.02, 7.96+-1.86$; prostate: 27.87+-2.29 dB, 0.80+-0.05, 8.38+- 1.44), with statistically significant gains over every comparison method (two-sided Wilcoxon signed-rank test with Holm correction, p<0.05). The strongest baseline, SR-EMamba, ranked second. Qualitatively, SR-DBM produced the smallest residual errors and best preserved fine structures and lesions.
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