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 对于深层组织中的细胞成像，自适应光学OCT（AO-OCT）已通过 重塑照明光束的波前，将光束聚焦到衍射限制点扩散 目标区域中的功能（PSF）。由于其复杂性，成本和尺寸，基于波前传感器的AO-OCT 将被转化为诊所的具有挑战性。较不复杂的无传感器AO-OCT（SAO-OCT）优化 使用图像指标的PSF，但无法确保全局优化，并且容易受到运动伪像 因为在优化迭代期间，图像指标必须坚固且稳定。训练有素的人工神经元 网络（ANN）可以立即优化波前，比传统 通过多次迭代进行优化。但是，具有图像指标的训练ANN限制了 安的普遍性。我们认为，SAO-OCT最好的指标应该是PSF或ITS 频域等效，调制传输函数（MTF），因为它们是优化目标 并且独立于成像的受试者和系统光学。但是，访问的技术 尚未提出具有OCT的散射介质中的PSF/MTF。oct图像起源于 由于组织的折射率变化而导致的反向散射光子。新的对比，组织属性有关 光学衰减系数（OAC）已进行了广泛的研究以改善诊断 OCT的能力。但是，衍生OAC主要基于单碎片模型，该模型忽略了 MSP，因为常规OCT无法区分LSP和MSP。另外，单散射模型 依赖至少三个相互依赖的参数。需要先验知识以确保得出OAC 成功，但是在临床环境中获得它是不切实际的。这些限制已禁止OAC 从转化为诊所的测量。在这里，我们建议重建反向散射光子 分布（BPD）在具有光束偏移OCT（bo-oct）的散射培养基中以解决上述 挑战。在常规OCT中，照明和检测光束共享相同的光路。在 BO-OCT，检测光束从照明梁从偏置位置获取图像。 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的分布等同于深度分辨的MTF，这表明可以实施SAO-OCT 使用MTF作为度量。使用BPD，我们还表明将LSP和MSP分开是可行的 为了准确检索OAC，仅使用LSP拟合单散射模型。实时访问 通过BPD通过BPD的焦点深度和瑞利范围，可以将这些参数的变化纳入 建模，表明一种新方法免于运动伪影。