Incorporating Prior Knowledge of Surgical Devices in CBCT-Guided Interventions

将手术器械的先验知识纳入 CBCT 引导干预中

• 批准号: 8445513
• 负责人: JOSEPH Webster STAYMAN
• 金额: $ 24.3万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8445513
• 关键词: Acceleration; Adopted; Algorithms; Anatomy; Area; Arthroplasty; Bone Screws; Cadaver; Clinical; Data; Detection; Development; Devices; Dose; Feedback; Generations; Genes; Hip Prosthesis; Hip region structure; Image; Imagery; Implant; Intervention; Knowledge; Libraries; Light; Location; Manufacturer Name; Medical Imaging; Metals; Methods; Metric; Modeling; Morphologic artifacts; Needles; Noise; Operative Surgical Procedures; Orthopedic Procedures; Orthopedic Surgery procedures; Orthopedics; Outcome Measure; Patients; Performance; Photons; Physicians; Physics; Plague; Probability; Procedures; Prosthesis; Protocols documentation; Radiation; Research; Research Design; Research Personnel; Retinal Cone; Series; Shapes; Signal Transduction; Simulate; Spinal Curvatures; Spinal Fusion; Statistical Models; Stress; Structure; Surgeon; Surgical Models; System; Techniques; Testing; Tissues; Translating; Vertebral column; Work; X-Ray Computed Tomography; arm; attenuation; base; cone-beam computed tomography; image reconstruction; improved; interest; intraoperative imaging; novel; pre-clinical; public health relevance; reconstruction; research study; retinal rods; sample fixation; statistics; success; tomography; tool; trend

DESCRIPTION (provided by applicant): Cone-beam CT (CBCT) is finding increased use in image-guided procedures, including orthopaedic surgeries such as spine fusion and total hip arthroplasty. Since intraoperative imaging is particularly likely to include surgical devices (e.g. tools, implants, or prostheses) within the tomographic field-of-view and these components have known composition, size, and shape, there is a unique opportunity to integrate such information in a re- construction approach. The investigators have developed a novel model-based approach called known- component reconstruction (KCR) that leverages known attenuation distributions, modeling an object comprised of known components (with unknown pose), as well as an unknown background anatomy. This is a new paradigm for incorporating prior object knowledge into a reconstruction framework where the algorithm jointly estimates both the background attenuation and the registers the known components. The technique is particularly well-suited to missing data and low signal-to-noise, as is common in interventional imaging due to metallic devices. Traditional reconstruction approaches are prone to severe metal streak artifacts (especially at low doses) with the poorest image quality in locations proximal to the device, which is often precisely the area of interest with the greatest image quality demands (e.g. visualization of nearby critical structures or interfaces of implants). Preliminary studies demonstrate that KCR is able to essentially eliminate artifacts associated with metal and allows for visualization of the object right up to the boundary of the tool or implant. We hypothesize tha an integrated system based on a generalized KCR framework with a library of known device components can provide artifact-free reconstructions in proximity to surgical implants, facilitatin high- precision device placement and dose reduction protocols in interventional CBCT. The following Specific Aims are proposed: 1.) Build a generalized analytic framework for KCR. Studies include development of a complete physics model for interventional CBCT, leveraging KCR's unique integration of component know- ledge, and adopting a deformable transformation model to allow for a broad class of inexactly known components (e.g., fixation rods in spine fusions that are deformed during a procedure to enforce a specific spine curvature). 2.) Create an integrated system for KCR. The development includes methods for generation of high- fidelity parameterized component models from CAD files or physical devices, computationally efficient algorithms and hardware, and tools for assessment of geometric accuracy in device placement from the component registration computed jointly in KCR. 3.) Evaluate KCR in pre-clinical experiments and simulated procedures. Work includes a systematic series of experiments using phantoms and cadavers with multiple components, deformable constructs, and conditions that stress the limits of noise, dose, object size, and implant size. Outcome measures will include quantitative imaging performance metrics, physician scoring, and registration error analysis, as well as the relation of these metrics to minimum-dose acquisition protocols.

描述（由申请人提供）：锥束 CT (CBCT) 在图像引导手术中的应用越来越多，包括脊柱融合术和全髋关节置换术等骨科手术。由于术中成像特别可能包括断层扫描视野内的手术设备（例如工具、植入物或假体），并且这些组件具有已知的成分、尺寸和形状，因此有一个独特的机会将这些信息集成到重建方法。研究人员开发了一种称为已知组件重建（KCR）的新型基于模型的方法，该方法利用已知的衰减分布，对由已知组件（具有未知姿态）以及未知背景解剖结构组成的对象进行建模。这是一种将先验对象知识纳入重建框架的新范例，其中算法联合估计背景衰减并记录已知分量。该技术特别适合丢失数据和低信噪比，这在金属设备的介入成像中很常见。传统的重建方法容易出现严重的金属条纹伪影（尤其是在低剂量时），并且在靠近设备的位置图像质量最差，而这通常正是图像质量要求最高的感兴趣区域（例如附近关键结构的可视化或植入物的接口）。初步研究表明，KCR 能够从根本上消除与金属相关的伪影，并允许可视化直至工具或植入物边界的物体。我们假设基于通用 KCR 框架和已知设备组件库的集成系统可以在手术植入物附近提供无伪影重建，促进介入 CBCT 中的高精度设备放置和剂量减少方案。提出以下具体目标： 1.) 建立 KCR 的通用分析框架。研究包括开发用于介入 CBCT 的完整物理模型，利用 KCR 独特的组件知识集成，并采用可变形变换模型以允许广泛类别的不完全已知的组件（例如，脊柱融合中的固定杆在手术过程中变形）。强制特定脊柱弯曲的程序）。 2.) 为 KCR 创建集成系统。该开发包括从 CAD 文件或物理设备生成高保真参数化组件模型的方法、计算高效的算法和硬件，以及根据 KCR 中联合计算的组件配准评估设备放置几何精度的工具。 3.) 在临床前实验和模拟程序中评估 KCR。工作包括使用具有多个组件、可变形结构以及强调噪声、剂量、物体尺寸和植入物尺寸限制的条件的模型和尸体进行一系列系统实验。结果测量将包括定量成像性能指标、医师评分和配准误差分析，以及这些指标与最小剂量采集协议的关系。