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.

翻译：高质量、高分辨率的医学成像对临床诊疗至关重要。基于拉曼效应的生物医学光学成像利用非电离红外辐射实时评估人体组织，可用于早期癌症检测、脑肿瘤诊断及术中组织分析。然而，由于激光散射和吸收导致的光学图像退化，可能引发诊断错误和治疗偏差。光学图像恢复是一项具有挑战性的计算机视觉任务，因为图像退化的成因具有多因素性、随机性和组织依赖性，难以直接获取配对的低质量/高质量数据。本文提出恢复性阶梯校准扩散（RSCD）方法，这是一种非配对图像恢复方法，将图像恢复问题视为完成基于扩散的图像生成任务的最终步骤。RSCD利用阶梯校准器模型动态确定图像退化程度，以及完成逆向扩散过程进行图像恢复所需的步骤数。在恢复光学图像的图像质量和感知评估指标上，RSCD均优于其他广泛使用的非配对图像恢复方法。医学影像专家在盲法对比实验中一致偏好RSCD恢复的图像，并报告极少甚至无幻觉现象。最后，我们证明RSCD可提升下游临床成像任务的性能，包括自动脑肿瘤诊断和深层组织成像。我们的代码已开源至https://github.com/MLNeurosurg/restorative_step-calibrated_diffusion。