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: 8588925
• 负责人: JOSEPH Webster STAYMAN
• 金额: $ 19.64万
• 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 2015-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8588925
• 关键词: Acceleration; Adopted; Algorithms; Anatomy; Area; Arthroplasty; Bone Screws; Cadaver; Clinical; Cone; 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; 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）正在发现在图像引导程序中的使用增加，包括骨科手术，例如脊柱融合和总髋关节置换术。由于术中成像尤其可能包括在层析成像视野内的手术设备（例如工具，植入物或假体），并且这些组件具有已知的组成，大小和形状，因此有一个独特的机会，可以将此类信息集成到重新构造方法中。研究人员开发了一种基于新型模型的方法，称为已知成分重建（KCR），该方法利用已知的衰减分布，建模由已知成分（具有未知姿势）组成的对象以及未知的背景解剖。这是将先前的对象知识纳入重建框架中的新范式，该算法共同估算背景衰减和寄存器的已知组件。该技术特别适合缺少数据和低信噪比，这在金属设备引起的介入成像中很常见。传统的重建方法容易发生严重的金属条纹伪像（尤其是在低剂量下），其位置的图像质量最差，该设备的位置通常是最大的图像质量需求（例如，附近的关键结构或植入物的界面的可视化图像）。初步研究表明，KCR能够基本上消除与金属相关的伪影，并允许对物体可视化，直到工具或植入物的边界。我们假设基于具有已知设备组件库的广义KCR框架的集成系统可以提供无伪影的重建，以靠近手术植入物，可加方素抑制素的高精度设备放置和减少剂量的介入方案。提出了以下特定目标：1。）为KCR构建广义分析框架。研究包括开发用于干预CBCT的完整物理模型，利用KCR的独特组成部分集成，并采用可变形的转换模型，以允许在棘突中具有广泛的固定成分（例如，在脊柱融合中的固定杆（例如，在过程中都会在过程中都变形以强化特定的脊柱曲率））。 2.）为KCR创建一个集成系统。该开发包括从CAD文件或物理设备，计算有效的算法和硬件中生成高保真参数化组件模型的方法，以及评估来自KCR中共同计算的组件注册的设备位置中几何准确性的工具。 3.）评估KCR进行临床前实验和模拟程序。工作包括一系列系统的实验，使用具有多个组件，可变形构建体以及压力噪声，剂量，对象大小和植入物尺寸的条件的幻象和尸体。结果指标将包括定量成像性能指标，医师评分和注册误差分析，以及这些指标与最低剂量获取方案的关系。

项目成果

Deformable Known Component Model-Based Reconstruction for Coronary CT Angiography.

基于可变形已知组件模型的冠状动脉 CT 血管造影重建。

• DOI: 10.1117/12.2255303
• 发表时间: 2017
• 期刊: Proceedings of SPIE--the International Society for Optical Engineering
• 作者: Zhang,X;Tilley,S;Xu,S;Mathews,A;McVeigh,ER;Stayman,JW
• 通讯作者: Stayman,JW

Integration of Component Knowledge in Penalized-Likelihood Reconstruction with Morphological and Spectral Uncertainties.

具有形态和光谱不确定性的惩罚似然重建中的成分知识的整合。

• 发表时间: 2014
• 期刊: Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography
• 作者: Stayman,JWebster;Tilley2nd,Steven;Siewerdsen,JeffreyH
• 通讯作者: Siewerdsen,JeffreyH

Generalized Least-Squares CT Reconstruction with Detector Blur and Correlated Noise Models.

• DOI: 10.1117/12.2043067
• 发表时间: 2014-03-19
• 期刊: Proceedings of SPIE--the International Society for Optical Engineering
• 作者: Stayman JW;Zbijewski W;Tilley S 2nd;Siewerdsen J
• 通讯作者: Siewerdsen J