TRD3: Endoscopic and Probe-based Coherence Imaging

TRD3：内窥镜和基于探头的相干成像

• 批准号: 10494623
• 负责人: Martin Villiger
• 金额: $ 31.92万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-21 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10494623
• 关键词: 3-Dimensional; Address; Algorithms; Architecture; Axon; Birefringence; Blood flow; Brain; Catheters; Collagen; Collection; Coronary; Coronary Arteriosclerosis; Coronary artery; Data; Deep Brain Stimulation; Devices; Diagnosis; Diagnostic; Diffusion Magnetic Resonance Imaging; Dimensions; Electrodes; Elements; Encapsulated; Endoscopes; Exhibits; Feedback; Fiber; Fiber Optics; Funding; Gastrointestinal tract structure; Goals; Hand; Human; Human body; Image; Imaging Device; Impairment; Intervention; Light; Lighting; Lung; Machine Learning; Maps; Measures; Methods; Microscopic; Motor; Muscle Cells; Needles; Neuroanatomy; Neurosurgical Procedures; Operative Surgical Procedures; Optics; Organ; Pathology; Patients; Performance; Phase; Physics; Procedures; Property; Research; Resolution; Rotation; Sampling; Scanning; Side; Signal Transduction; Skin Cancer; Staging; Structure; Supervision; Techniques; Time; Tissues; Tomogram; Training; Translations; Variant; algorithm training; base; brain tissue; clinical investigation; contrast imaging; cost; deep learning; deep neural network; design; experience; flexibility; generative adversarial network; imaging probe; imaging system; implantation; improved; insight; instrument; interstitial; malignant mouth neoplasm; network architecture; neural network; neurosurgery; novel; novel strategies; success; transfer learning; vector; white matter; 3-Dimensional; Address; Algorithms; Architecture; Axon; Birefringence; Blood flow; Brain; Catheters; Collagen; Collection; Coronary; Coronary Arteriosclerosis; Coronary artery; Data; Deep Brain Stimulation; Devices; Diagnosis; Diagnostic; Diffusion Magnetic Resonance Imaging; Dimensions; Electrodes; Elements; Encapsulated; Endoscopes; Exhibits; Feedback; Fiber; Fiber Optics; Funding; Gastrointestinal tract structure; Goals; Hand; Human; Human body; Image; Imaging Device; Impairment; Intervention; Light; Lighting; Lung; Machine Learning; Maps; Measures; Methods; Microscopic; Motor; Muscle Cells; Needles; Neuroanatomy; Neurosurgical Procedures; Operative Surgical Procedures; Optics; Organ; Pathology; Patients; Performance; Phase; Physics; Procedures; Property; Research; Resolution; Rotation; Sampling; Scanning; Side; Signal Transduction; Skin Cancer; Staging; Structure; Supervision; Techniques; Time; Tissues; Tomogram; Training; Translations; Variant; algorithm training; base; brain tissue; clinical investigation; contrast imaging; cost; deep learning; deep neural network; design; experience; flexibility; generative adversarial network; imaging probe; imaging system; implantation; improved; insight; instrument; interstitial; malignant mouth neoplasm; network architecture; neural network; neurosurgery; novel; novel strategies; success; transfer learning; vector; white matter

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.

项目摘要 TRD 3 该TRD项目的目标是增强基于内窥镜和基于探测的OCT的功能和功能。 纤维充电OCT问题的较小形式使能够到达人类的偏远器官 主体，使OCT通常用于冠状动脉临床研究，胃肠道 道和肺。但是，通过高级OCT信号改善图像对比的许多策略 收集和处理与基于探针的OCT的空间和实际约束不相容。这 损害诊断性能和反馈以指导干预措施。 TRD 3的重点是解决一些 这些限制。 OCT从组织的反向散射特性的变化中得出了图像对比，但微妙的差异 散射属性可能很难识别 相干，导致斑点。极化提供了一个完整的内源性对比机制 在OCT的反向散射信号中不明显的组织之间提供对比度。许多组织 纤维架建筑展览表演双折射和延迟光，具体取决于其极化状态的对齐 与纤维组织成分。 特定的目标1大写了组织的内在双折射，以测量纤维组织的方向 通过光纤成像问题，所有三个空间维度的元素。这与 在大脑中立体定向神经外科手术期间成像双重白质。成像问题 在不同的照明角度包含两个成像通道，并通过多通道电机干扰 驱动器将被制造。利用多个成像角并观察其他成像角度的算法 将开发连续性约束以重建3D矢量双折射。可视化3D方向 颅内探针周围的轴突段将实现立体定向程序的显微镜指导， 例如实施刺激电极来进行深脑刺激。 特定的目标2通过利用机器学习来应对OCT中Speckle的持续挑战 将基于硬件的斑点抑制的物理含义封装到训练有素的算法中。小说 使用样品倾斜来生成地面真相斑点抑制的断层图的方法 将开发以实现对深度神经网络的监督培训。部署的具体挑战 培训的算法将通过开发监督和一个 无监督的域适应方法。改进的图像对比度和斑点抑制至关重要 解释许多组织病理，包括，例如，皮肤和口腔癌的诊断和分期。 结合在一起，这些努力将改善基于探测的OCT可实现的对比度，从而增强了对比度 实际使用并将其实用性扩展到缺乏决定性对比的新应用程序。