TRD3: Endoscopic and Probe-based Coherence Imaging

项目摘要 TRD3 该TRD项目的目标是增强基于内窥镜和探头的OCT的能力和功能。 光纤OCT探头的小尺寸使其能够到达人类遥远的器官 身体，使OCT能够常规用于冠状动脉、胃肠道的临床研究 肠道和肺部。然而，许多通过高级OCT信号来提高图像对比度的策略 采集和处理与基于探头的OCT的空间和实际约束不相容。这 影响诊断性能和指导干预的反馈。Trd3的重点是解决一些 这些限制。 OCT的图像对比度来自组织的后向散射特性的变化，但 散射特性可能很难识别，因为来自亚表面微结构的信号累积起来 连贯性的，导致斑点。极化提供了一种互补的内生对比机制，可以 在OCT的后向散射信号中无法分辨的组织之间提供对比度。许多纸巾上都有 纤维状结构表现出双折射和延迟光，这取决于其偏振态的排列 纤维状组织成分。 特定目标1利用组织的固有双折射来测量纤维组织的取向 通过光纤成像探头在所有三个空间维度上的元素。这与以下方面特别相关 脑部立体定向神经外科手术中双折射白质束成像。成像探头 包含两个不同照明角度的成像通道，并通过多通道马达接口 驱动单元将被制造出来。利用多个成像角度的算法并观察其他 连续性约束将被用来重建三维矢量双折射。可视化3D方向 围绕着颅内探头的轴突束将使立体定向手术的显微指导成为可能， 例如植入刺激电极进行脑深部刺激。 特定目标2通过利用机器学习来应对OCT中持续存在的斑点挑战 将基于硬件的斑点抑制的物理意义封装到训练算法中。一本小说 利用倾斜样本进行角度合成生成地面真实相干斑抑制层析图像的方法 将被开发成能够对深度神经网络进行监督训练。部署的具体挑战 新成像系统的训练算法将通过开发有监督的和 一种无监督的领域自适应方法。改善图像对比度和斑点抑制对于 解释许多组织病理学，包括皮肤癌和口腔癌的诊断和分期。 综合起来，这些努力将改善与基于探测器的OCT可实现的对比度，从而增强其 实际使用，并将其用途扩展到缺乏决定性对比的新应用。

Project Summary TRD 3 The goal of this TRD project is to enhance the power and functionality of endoscopic and probe-based OCT. The small form factor of fiber-optic OCT probes affords the capacity to reach remote organs of the human body, enabling OCT to be routinely used for clinical investigation of the coronary arteries, the gastrointestinal tract, and the lung. However, many strategies to improve image contrast through advanced OCT signal collection and processing are incompatible with the spatial and practical constraints of probe-based OCT. This impairs diagnostic performance and feedback to guide interventions. The focus of TRD 3 is to address some of these limitations. OCT derives image contrast from variations in the tissue’s backscattering properties, but subtle differences in the scattering properties can be difficult to identify because the signal from subsurface microstructure adds up coherently, resulting in speckle. Polarization offers a complementary endogenous contrast mechanism that can afford contrast between tissues that are indiscernible in OCT’s backscattering signal. Many tissues with a fibrillar architecture exhibit birefringence and delay light depending on the alignment of its polarization state with the fibrillar tissue components. Specific Aim 1 capitalizes on tissue’s intrinsic birefringence to measure the orientation of fibrillar tissue elements in all three spatial dimensions through fiber-optic imaging probes. This is specifically relevant for imaging birefringent white matter tracts during stereotactic neurosurgery in the brain. Imaging probes containing two imaging channels at distinct illumination angles and interfaced through a multi-channel motor drive unit will be fabricated. Algorithms that leverage the multiple imaging angles and observe additional continuity constraints will be developed to reconstruct 3D vectorial birefringence. Visualizing the 3D orientation of axonal tracts surrounding an intracranial probe will enable microscopic guidance of stereotactic procedures, such as the implantation of stimulation electrodes for deep brain stimulation. Specific Aim 2 responds to the persistent challenge of speckle in OCT by leveraging machine learning to encapsulate the physical meaning of hardware-based speckle suppression into a trained algorithm. A novel method to generate ground truth speckle-suppressed tomograms using sample tilting for angular compounding will be developed to enable supervised training of a deep neural network. The specific challenge of deploying the trained algorithm to new imaging systems will be addressed by developing both a supervised and an unsupervised method for domain adaptation. Improved image contrast and speckle suppression are critical for interpretation of many tissue pathologies, including, e.g., the diagnosis and staging of skin and oral cancer. Combined, these efforts will improve the contrast achievable with probe-based OCT, thereby enhancing its practical use and extending its utility to new applications where decisive contrast has been lacking.