Development of beam-offset optical coherence tomography

光束偏移光学相干断层扫描技术的发展

基本信息

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
项目类别：
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

项目摘要

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.

项目概要对于深部组织的细胞成像，自适应光学 OCT (AO-OCT) 已由重塑照明光束的波前，将光束聚焦到衍射极限点扩散目标区域中的函数（PSF）。由于其复杂性、成本和尺寸，基于波前传感器的 AO-OCT 转化为临床具有挑战性。不太复杂的无传感器 AO-OCT(SAO-OCT) 优化使用图像度量的 PSF，但无法确保全局优化并且容易受到运动伪影的影响因为在优化迭代过程中图像指标必须强大且稳定。经过训练的人工神经元网络（ANN）可以立即优化波前，比传统的方法更有效通过多次迭代进行优化。然而，使用图像度量训练 ANN 限制了 ANN 的一般性。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，访问技术尚未提出在散射介质中使用 OCT 的 PSF/MTF。OCT 图像源自由于组织中折射率的变化而产生反向散射光子。新对比，组织特性相关光学衰减系数（OAC）已被广泛研究以提高诊断能力 OCT 能力。然而，推导OAC主要基于单散射模型，忽略了 MSP，传统OCT无法区分LSP和MSP。此外，单散射模型依赖于至少三个相互依赖的参数。需要先验知识才能确保导出 OAC 成功，但在临床环境中获得它是不切实际的。这些限制禁止 OAC 测量转化为临床。在这里，我们建议重建反向散射光子使用光束偏移 OCT (BO-OCT) 在散射介质中进行分布 (BPD) 来解决上述问题挑战。在传统的 OCT 中，照明光束和检测光束共享相同的光路。在 BO-OCT，检测光束在与照明光束偏移的位置处获取图像。 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的分布相当于深度分辨MTF，表明可以实施SAO-OCT 使用 MTF 作为衡量标准。通过 BPD，我们还证明了分离 LSP 和 MSP 是可行的，从而允许仅使用 LSP 来拟合单散射模型即可准确地检索 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，提出了一种不受运动伪影影响的新方法。