Incorporating Prior Knowledge of Surgical Devices in CBCT-Guided Interventions

描述（由申请人提供）：锥形束CT（CBCT）在图像引导手术中的使用越来越多，包括脊柱融合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摄影视场内的手术装置（例如，工具、植入物或假体），并且这些部件具有已知的组成、尺寸和形状，因此存在将这样的信息整合到重建方法中的独特机会。研究人员开发了一种新的基于模型的方法，称为已知成分重建（KCR），该方法利用已知的衰减分布，对由已知成分（具有未知姿态）组成的对象以及未知的背景解剖结构进行建模。这是一种新的范例，将先验对象知识纳入重建框架，其中该算法联合估计背景衰减和登记的已知组件。该技术特别适用于缺失数据和低信噪比，这在介入成像中很常见，因为金属器械。传统的重建方法容易出现严重的金属条纹伪影（特别是在低剂量下），器械近端位置的图像质量最差，而器械近端位置通常恰好是图像质量要求最高的感兴趣区域（例如，附近关键结构或植入物界面的可视化）。初步研究表明，KCR能够基本上消除与金属相关的伪影，并允许对工具或植入物边界的物体进行可视化。我们假设，基于广义KCR框架和已知器械组件库的集成系统可以在外科植入物附近提供无伪影重建，促进介入CBCT中的高精度器械放置和剂量降低方案。具体目标如下：（1）建立KCR的一般分析框架。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的一类不精确已知组件（例如，脊柱融合中的固定杆，其在手术过程中变形以加强特定的脊柱弯曲）。2.）为九广铁路建立一个综合系统。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、计算效率高的算法和硬件，以及从KCR中联合计算的组件配准评估设备放置中几何精度的工具。3.）第三章在临床前实验和模拟程序中评估KCR。工作包括一系列系统的实验，使用幻影和尸体与多个组件，可变形的结构，和条件，强调噪音，剂量，对象大小和植入物大小的限制。结果测量将包括定量成像性能指标、医生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。

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.