Adaptive Optics for Medical Imaging

1)简要描述研究背景，包括潜在影响本研究项目涉及生物医学光学成像，主要涉及光学相干断层扫描（OCT）技术。目标应用是视网膜的医学成像，视网膜是眼睛后部的光敏组织。OCT是一种常用的诊断视力丧失疾病的技术。本研究项目的目的是通过提高成像系统的分辨率和推进所获得图像的分析来提高诊断能力。该研究将包括开发完整的OCT系统，包括用于医学图像采集的光学硬件（透镜、光纤、光电探测器）和控制软件（协调相机、处理和显示）。本项目还包括自适应光学（AO）与OCT的结合，目的是增强图像的清晰度，使视网膜中的细胞能够被分辨出来。将对图像进行采集后处理，以识别高分辨率图像中视网膜疾病的生物标志物。获取的OCT图像将使用常规编程和深度学习方法进行处理，以进行特征识别和健康和患病眼睛的诊断分类。细胞分辨率成像和图像分析在评估新的再生疗法中也很重要，并且通过提供新疗法效果的详细反馈，具有加速临床试验的重大潜力。这些图像将提供视网膜中是否存在目标细胞类型以及它们是否正常工作的指示。2)目的和目标本项目旨在开发一种新型生物医学光学成像系统OCT，该系统采用自适应光学和机器学习等分辨率增强技术。由此产生的眼睛非侵入性高分辨率成像将允许视网膜的细胞分辨率可视化。这对于在发生明显和不可逆转的视力丧失之前的早期阶段识别疾病进展具有重要意义。3)研究方法的新颖性该研究方法独特地将自适应光学（AO）与光学相干断层扫描（OCT）相结合，提出了一种提高图像清晰度的新方法。这种整合使视网膜的微小细胞结构成为清晰的焦点，超越了传统的成像能力。此外，将深度学习技术引入OCT图像分析是一个重大的进步。通过人工智能，该方法增强了诊断工具的深度和准确性，能够检测到以前使用传统方法无法检测到的复杂视网膜模式。4)与EPSRC的战略和研究领域保持一致该项目的重点是通过自适应光学（AO）和深度学习技术的整合来增强光学相干层析成像（OCT），这与EPSRC在工程和物理科学领域的世界领先和变革性研究的承诺相一致。该项目的跨学科融合了光学、计算和临床应用，体现了EPSRC促进跨学科合作以应对复杂挑战的愿景。英国国立卫生研究院穆尔菲尔德分校BRC和伦敦大学学院眼科研究所是该研究项目的共同资助者，并将为完成目标提供额外的研究基础设施。

1) Brief description of the context of the research including the potential impactThis research project is in biomedical optical imaging, and primarily on a technique called optical coherence tomography (OCT). The target application is medical imaging of the retina, the light sensitive tissue at the back of the eye. OCT is a commonly used technique to diagnose diseases causing vision loss. The purpose of this research project is to improve diagnostic capabilities by improving the resolution of the imaging system and advancing the analysis of the images acquired. The research will include the development of complete OCT systems, including optical hardware (lenses, fibre optics, photodetectors), and control software (coordinating the camera, processing, and display) for medical image acquisition. This project also includes the combination of adaptive optics (AO) with OCT, for the purpose of enhancing the sharpness of the images to enable the cells in the retina to be resolved. Post-acquisition processing of the images will be performed to identify biomarkers of retinal diseases in the high-resolution images. The OCT images that are acquired will be processed using conventional programming as well as Deep Learning methods for feature identification and for diagnostic classification of healthy and diseased eyes. Cellular resolution imaging and image analysis are also important in the evaluation of novel regenerative therapy and have significant potential to accelerate clinical trials by providing detailed feedback on the effects of the new therapy. The images will provide indications if the targeted cell types are present in the retina, and if they are functioning correctly.2) Aims and ObjectivesThe aim of this project is to develop a novel biomedical optical imaging system OCT with resolution enhancement technologies like Adaptive Optics and machine learning. The resulting high-resolution imaging in the eye non-invasively will permit the visualization of the retina with cellular resolution. This is significant for the identification of disease progression in the early phases before noticeable and irreversible loss of vision occurs.3) Novelty of Research MethodologyThe research methodology uniquely integrates Adaptive Optics (AO) with Optical Coherence Tomography (OCT), presenting a novel approach to amplify image clarity. This integration brings the minute cellular structures of the retina into sharp focus, surpassing traditional imaging capabilities. Additionally, the introduction of Deep Learning techniques to OCT image analysis represents a significant advancement. With artificial intelligence, the methodology enhances the depth and accuracy of the diagnostic tool, enabling the detection of intricate retinal patterns that were previously undetectable using conventional methods.4) Alignment to EPSRC's strategies and research areasThe project focuses on the enhancement of Optical Coherence Tomography (OCT) through the integration of Adaptive Optics (AO) and Deep Learning techniques, which aligns with EPSRC's commitment to world-leading and transformative research in the engineering and physical sciences. The project's interdisciplinary blend of optics, computation, and clinical applications embodies EPSRC's vision of fostering cross-disciplinary collaborations to address complex challenges.5) Any companies or collaborators involvedThe NIHR BRC at Moorfields and UCL Institute of Ophthalmology is a co-funder of this research project and will provide additional research infrastructure toward the completion of the objectives.