O-PRESS: Boosting OCT axial resolution with Prior guidance, Recurrence, and Equivariant Self-Supervision

IF 10.7 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Medical image analysis Pub Date : 2024-08-28 DOI:10.1016/j.media.2024.103319
Kaiyan Li , Jingyuan Yang , Wenxuan Liang , Xingde Li , Chenxi Zhang , Lulu Chen , Chan Wu , Xiao Zhang , Zhiyan Xu , Yueling Wang , Lihui Meng , Yue Zhang , Youxin Chen , S. Kevin Zhou
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Abstract

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.

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O-PRESS:利用先验引导、复现和等变量自我监督提高 OCT 轴向分辨率
光学相干断层扫描(OCT)是一种无创技术,可对组织微观结构进行实时成像。OCT 的轴向分辨率受到所使用光源光谱带宽的内在限制,同时还要保持特定应用的固定中心波长。在物理上扩展这一带宽面临很大的限制,并且需要大量成本。我们提出了一种名为 O-PRESS 的新型计算方法,利用先验引导、循环机制和等变自我监督来提高 OCT 的轴向分辨率。与依赖物理模型或数据驱动技术的传统解卷积方法不同,我们的方法无缝集成了 OCT 建模和深度学习,使我们能够完全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项主要任务。这两项任务都是以自我监督的方式执行的,等差线性成像和自由空间先验指导着各自的过程。包括定量指标和视觉评估在内的实验评估一致验证了我们方法的有效性和优越性,其性能与完全监督方法不相上下。重要的是,我们模型的稳健性得到了肯定,展示了其在提高轴向分辨率的同时改善信噪比的双重能力。
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
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