TRD3: Endoscopic and Probe-based Coherence Imaging

项目摘要 TRD 3 本TRD项目的目标是增强内窥镜和基于探头的OCT的功率和功能。 光纤OCT探头的小外形尺寸提供了到达人体远端器官的能力 使OCT能够常规用于冠状动脉、胃肠道和心脏的临床研究。 道和肺。然而，通过先进的OCT信号来提高图像对比度的许多策略 收集和处理与基于探针的OCT的空间和实际约束不兼容。 损害诊断性能和指导干预的反馈。TRD 3的重点是解决 这些限制。 OCT从组织的后向散射特性的变化中获得图像对比度，但是在组织的后向散射特性中的细微差异是不同的。 散射特性可能难以识别，因为来自地下微结构的信号加起来 相干地，导致散斑。偏振提供了一种互补的内源性对比机制， 提供在OCT的反向散射信号中不可辨别的组织之间的对比度。许多纸巾 纤维状结构表现出双折射和延迟光取决于其偏振态的排列 与纤维组织成分结合。 具体目标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的元素。这一点特别适用于 在脑中的立体定向神经外科手术期间成像双折射白色物质束。成像探针 包含两个不同照明角度的成像通道并通过多通道电机连接 驱动单元将被制造。利用多个成像角度并观察额外的 将开发连续性约束以重建3D矢量双折射。可视化3D方向 围绕颅内探针的轴突束的延伸将使得立体定向过程的显微镜引导成为可能， 例如植入用于深部脑刺激的刺激电极。 Specific Aim 2通过利用机器学习来应对OCT中斑点的持续挑战， 将基于硬件的斑点抑制的物理意义封装到训练算法中。一种新型 使用用于角度复合的样本倾斜来生成地面真实斑点抑制层析图像的方法 将被开发以实现深度神经网络的监督训练。部署的具体挑战 新成像系统的训练算法将通过开发监督和 无监督域自适应方法。改善图像对比度和斑点抑制对于 许多组织病理的解释，包括，例如，皮肤癌和口腔癌的诊断和分期。 结合起来，这些努力将提高基于探针的OCT可实现的对比度，从而增强其 实际应用，并将其实用性扩展到缺乏决定性对比的新应用中。

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.