High-quality, high-resolution medical imaging is essential for clinical care. Raman-based biomedical optical imaging uses non-ionizing infrared radiation to evaluate human tissues in real time and is used for early cancer detection, brain tumor diagnosis, and intraoperative tissue analysis. Unfortunately, optical imaging is vulnerable to image degradation due to laser scattering and absorption, which can result in diagnostic errors and misguided treatment. Restoration of optical images is a challenging computer vision task because the sources of image degradation are multi-factorial, stochastic, and tissue-dependent, preventing a straightforward method to obtain paired low-quality/high-quality data. Here, we present Restorative Step-Calibrated Diffusion (RSCD), an unpaired image restoration method that views the image restoration problem as completing the finishing steps of a diffusion-based image generation task. RSCD uses a step calibrator model to dynamically determine the severity of image degradation and the number of steps required to complete the reverse diffusion process for image restoration. RSCD outperforms other widely used unpaired image restoration methods on both image quality and perceptual evaluation metrics for restoring optical images. Medical imaging experts consistently prefer images restored using RSCD in blinded comparison experiments and report minimal to no hallucinations. Finally, we show that RSCD improves performance on downstream clinical imaging tasks, including automated brain tumor diagnosis and deep tissue imaging. Our code is available at https://github.com/MLNeurosurg/restorative_step-calibrated_diffusion.

使用非电离辐射的红外光对人体组织进行实时评估的基于拉曼的生物医学光学成像是早期癌症检测、脑肿瘤诊断和术中组织分析的关键技术。由于激光散射和吸收导致图像退化，光学成像容易出现图像质量下降，从而产生诊断错误和误导性治疗。本文提出了一种称为RSCD的对图像进行修复的方法，它通过动态地确定图像退化的严重程度和完成反扩散过程所需的步数来解决图像修复问题。RSCD在恢复光学图像的图像质量和感知评估指标方面优于其他常用的非配对图像修复方法。在盲目比较实验中，医学成像专家普遍倾向于选择使用RSCD修复的图像，并报道几乎没有幻觉。最后，我们证明了RSCD在自动脑肿瘤诊断和深层组织成像等临床成像任务中的性能改善。

基于步骤校准的生物医学光学图像恢复