TRD3: Endoscopic and Probe-based Coherence Imaging

TRD3：内窥镜和基于探头的相干成像

基本信息

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
项目类别：
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

项目摘要

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.

项目概要 TRD 3 该 TRD 项目的目标是增强内窥镜和基于探头的 OCT 的性能和功能。光纤 OCT 探头外形小巧，能够到达人体的远程器官使 OCT 能够常规用于冠状动脉、胃肠道的临床检查然而，有许多策略可以通过先进的 OCT 信号来提高图像对比度。采集和处理与基于探针的 OCT 的空间和实际限制不相容。损害诊断性能和指导干预措施的反馈。TRD 3 的重点是解决其中的一些问题。这些限制。 OCT 从组织反向散射特性的变化中得出图像对比度，但细微的差异散射特性可能很难识别，因为来自地下微观结构的信号会累加起来相干地产生散斑，提供了一种互补的内源对比度机制。提供 OCT 反向散射信号中难以辨别的组织之间的对比度。纤维结构表现出双折射并延迟光，具体取决于其偏振态的排列与纤维组织成分。具体目标 1 利用组织的固有双折射来测量纤维组织的方向通过光纤成像探头在所有三个空间维度上的元素这特别相关。在大脑立体定向神经外科手术中对双折射白质束进行成像。包含两个具有不同照明角度的成像通道，并通过多通道电机连接将制造利用多个成像角度并观察额外的算法。将开发连续性约束来重建 3D 矢量双折射，使 3D 方向可视化。颅内探针周围的轴突束将能够实现立体定向手术的显微引导，例如植入刺激电极进行深部脑刺激。具体目标 2 通过利用机器学习来应对 OCT 中散斑的持续挑战将基于硬件的散斑抑制的物理意义封装到训练有素的算法中。使用样本倾斜进行角度复合来生成地面真实散斑抑制断层图的方法将开发用于实现深度神经网络的监督训练的具体挑战。新成像系统的训练算法将通过开发监督和改进图像对比度和斑点抑制的无监督方法对于域适应至关重要。对许多组织病理学的解释，包括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 所能实现的对比度，从而增强其实际用途并将其实用性扩展到缺乏决定性对比的新应用。