Optical coherence tomography (OCT) is a noninvasive technology that enables real-time imaging of tissue microanatomies. The axial resolution of OCT is intrinsically constrained by the spectral bandwidth of the employed light source while maintaining a fixed center wavelength for a specific application. Physically extending this bandwidth faces strong limitations and requires a substantial cost. We present a novel computational approach, called as O-PRESS, for boosting the axial resolution of OCT with Prior guidance, a Recurrent mechanism, and Equivariant Self-Supervision. Diverging from conventional deconvolution methods that rely on physical models or data-driven techniques, our method seamlessly integrates OCT modeling and deep learning, enabling us to achieve real-time axial-resolution enhancement exclusively from measurements without a need for paired images. Our approach solves two primary tasks of resolution enhancement and noise reduction with one treatment. Both tasks are executed in a self-supervised manner, with equivariance imaging and free space priors guiding their respective processes. Experimental evaluations, encompassing both quantitative metrics and visual assessments, consistently verify the efficacy and superiority of our approach, which exhibits performance on par with fully supervised methods. Importantly, the robustness of our model is affirmed, showcasing its dual capability to enhance axial resolution while concurrently improving the signal-to-noise ratio.

中文翻译：

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O-PRESS：通过事先指导、复发和等变自我监督提高 OCT 轴向分辨率


光学相干断层扫描 （OCT） 是一种无创技术，可实现组织显微解剖结构的实时成像。OCT 的轴向分辨率本质上受到所用光源的光谱带宽的限制，同时为特定应用保持固定的中心波长。物理扩展此带宽面临强大的限制，并且需要大量成本。我们提出了一种称为 O-PRESS 的新型计算方法，用于通过 Prior guidance、Recurrent 机制和 Equivariant Self-Supervision 提高 OCT 的轴向分辨率。与依赖物理模型或数据驱动技术的传统反卷积方法不同，我们的方法无缝集成了 OCT 建模和深度学习，使我们能够仅通过测量实现实时轴向分辨率增强，而无需配对图像。我们的方法通过一次处理解决了分辨率增强和降噪两个主要任务。这两项任务都以自我监督的方式执行，等方差成像和自由空间先验指导各自的过程。实验评估，包括定量指标和视觉评估，始终验证我们方法的有效性和优越性，该方法表现出与完全监督方法相当的性能。重要的是，我们模型的稳健性得到了肯定，展示了其提高轴向分辨率同时提高信噪比的双重能力。