Development of beam-offset optical coherence tomography

光束偏移光学相干断层扫描技术的发展

• 批准号: 10666910
• 负责人: Hui Wang
• 金额: $ 57.49万
• 依托单位: MIAMI UNIVERSITY OXFORD
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-21 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10666910
• 关键词: Burn injury; Classification; Clinic; Clinical; Consumption; Data; Derivation procedure; Dermatology; Detection; Development; Ensure; Frequencies; Future; Goals; Heterogeneity; Human; Image; Immune; Knowledge; Lateral; Lighting; Measures; Methods; Modeling; Monitor; Morphologic artifacts; Motion; Natural regeneration; Neurons; Newts; Ophthalmology; Optical Coherence Tomography; Optics; Patients; Photons; Positioning Attribute; Predisposition; Property; Refractive Indices; Resolution; Retina; Retrieval; Shapes; Skin; Speed; System; Technology; Time; Tissue imaging; Tissues; Training; Translating; Variant; Voice; adaptive optics; attenuation; burn wound; cellular imaging; clinical diagnostics; cost; design; diagnostic value; healing; human imaging; imaging modality; improved; in vivo; in vivo evaluation; in vivo imaging; long short term memory; medical specialties; method development; non-invasive monitor; novel; photon-counting detector; retinal imaging; retinal regeneration; sensor

Project Summary For cellular imaging in deep tissue, adaptive optics OCT (AO-OCT) has been intensively developed by reshaping the wavefront of the illumination beam to focus the beam to diffraction-limited point spread function (PSF) in a targeted region. Due to its complexity, cost, and size, wavefront sensor-based AO-OCT is challenging to be translated into clinics. Less complicated sensorless AO-OCT(SAO-OCT) optimizes the PSF using image metrics, but cannot ensure global optimization and is susceptible to motion artifacts because image metrics must be strong and steady during the optimizing iteration. A trained Artificial neuron network (ANNs) can optimize the wavefront immediately, much more efficiently than the conventional optimization through multiple iterations. However, training ANNs with the image metric limits the generality of the ANN. We believe that the best metric for SAO-OCT should be either the PSF or its frequency domain equivalent, modulated transfer function (MTF), as they are the goals for optimization and are independent of the imaged subjects and system optics. However, the technology of accessing PSF/MTF in a scattering medium with OCT has not been proposed.OCT images originate from backscattered photons due to refractive index variation in tissue. New contrast, tissue property-related optical attenuation coefficient (OAC), has been extensively investigated to improve the diagnostic capability of OCT. However, deriving OAC is mainly based on the single-scattering model, which ignores MSPs, as conventional OCT cannot distinguish LSPs and MSPs. In addition, the single-scattering model relies on at least three interdependent parameters. Prior knowledge is needed to ensure deriving OAC successfully, but obtaining it in a clinical setting is not practical. These limitations have prohibited OAC measuring from being translated into clinics. Here, we propose reconstructing backscattered photon distribution(BPD) in a scattering medium with beam-offset OCT (BO-OCT) to resolve the above challenges. In conventional OCT, the illumination and detection beams share the same optical paths. In BO-OCT, the detection beam acquires images at offset positions from the illumination beam. The BPD can then be reconstructed with the offset images. Our theoretical prediction and preliminary data show that the distribution of LSPs is equivalent to the depth-resolved MTF, suggesting SAO-OCT can be implemented using the MTF as the metric. With the BPD, we also show it is feasible to separate LSPs and MSPs, allowing for accurately retrieving OAC by using just the LSPs to fit the single-scattering model. Real-time accessing focal depth and Rayleigh range through the BPD allow incorporating the variation of these parameters into modeling, suggesting a new method immune from motion artifacts.

项目摘要 对于深部组织中的细胞成像，自适应光学OCT（AO-OCT）已经被广泛开发， 对所述照明光束的所述波前进行整形以将所述光束聚焦到衍射受限点扩展 在目标区域中的函数（PSF）。由于其复杂性、成本和尺寸，基于波前传感器的声光OCT 很难将其转化为诊所。更简单的无传感器AO-OCT（SAO-OCT）优化了 PSF使用图像度量，但不能确保全局优化，并且容易受到运动伪影的影响 因为图像度量在优化迭代期间必须是强的和稳定的。经过训练的人工神经元 人工神经网络（ANN）可以立即优化波前，比传统的更有效 通过多次迭代进行优化。然而，用图像度量训练ANN限制了 我们认为，SAO-OCT的最佳度量应该是PSF或其 频域等效调制传递函数（MTF），因为它们是优化的目标 并且独立于成像对象和系统光学器件。然而，接入技术 还没有提出具有OCT的散射介质中的PSF/MTF。 由于组织中的折射率变化而导致的反向散射光子。新造影剂，组织特性相关 光学衰减系数（OAC），已被广泛研究，以提高诊断 然而，推导OAC主要基于单散射模型，该模型忽略了 MSP，因为常规OCT不能区分LSP和MSP。此外，单次散射模型 依赖于至少三个相互依赖的参数。需要先验知识以确保导出OAC 成功地，但在临床环境中获得它是不实际的。这些限制禁止OAC 从转化到诊所。在这里，我们提出重建后向散射光子 图10示出了利用光束偏移OCT（BO-OCT）在散射介质中的BPD分布（BPD）来解决上述问题的方法。 挑战在常规OCT中，照明光束和检测光束共享相同的光路。在 在BO-OCT中，检测光束在偏离照明光束的位置处获取图像。BPD可以 然后用偏移图像重建。我们的理论预测和初步数据表明， LSP的分布与深度分辨MTF相当，表明可以实现SAO-OCT 使用MTF作为度量。利用BPD，我们还表明分离LSP和MSP是可行的， 通过仅使用LSP来拟合单散射模型来精确地检索OAC。实时访问 通过BPD的焦深和瑞利范围允许将这些参数的变化合并到 建模，提出了一种新的方法免疫运动伪影。