Multi-modal Tracking of In Vivo Skeletal Structures and Implants

体内骨骼结构和植入物的多模式跟踪

• 批准号: 10610317
• 负责人: Joseph J Crisco
• 金额: $ 76.64万
• 依托单位: RHODE ISLAND HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10610317
• 关键词: 3-Dimensional; Adoption; Affect; Age; Aging; Agreement; Algorithmic Analysis; Algorithms; Ankle; Arthritis; Biomedical Engineering; Bone structure; Cadaver; Clinic; Clinical; Code; Collaborations; Communities; Complex; Computer software; Custom; Data; Data Set; Degenerative polyarthritis; Detection; Development Plans; Disease; Documentation; Ecosystem; Educational Models; Educational workshop; Feedback; Fostering; Freedom; Generations; Goals; Hand; Image; Implant; Individual; Industry Standard; Infrastructure; Injury; Joints; Knee joint; Laboratories; Letters; Licensing; Magnetic Resonance Imaging; Maintenance; Measures; Medical Imaging; Methodology; Methods; Modeling; Motion; Musculoskeletal; Musculoskeletal Diseases; ORALIT; Osteoporosis; Output; Pathology; Persons; Pilot Projects; Population; Positioning Attribute; Process; Public Domains; Publications; Replacement Arthroplasty; Research; Research Personnel; Research Project Grants; Resource Sharing; Roentgen Rays; Shoulder; Site; Spinal cord injury; Surface; Surveys; System; Techniques; Technology; Testing; Thumb structure; Training; Training and Education; Translations; Trauma; Universities; Update; Vertebral column; Visualization; Work; Wrist; Writing; X-Ray Computed Tomography; anterior cruciate ligament reconstruction; base; bone; bone imaging; clinical implementation; clinical translation; experience; foot; graphical user interface; image registration; image visualization; imaging Segmentation; imaging modality; imaging program; imaging software; improved; in vivo; innovation; interest; joint function; kinematics; ligament injury; multimodality; multiple datasets; novel; open source; open source library; programs; quality assurance; research and development; research study; response; sharing platform; skeletal; software development; tool development; treatment effect; user-friendly; web site

Abstract The goal of this R01 application is to develop state-of-the-art, open-source software for image-based analysis of skeletal kinematics. Worldwide, over 250 million people are affected by musculoskeletal disorders, including arthritis, trauma, osteoporosis, and spine pathology, a number that is projected to increase as the population ages. The in-depth understanding of normal joint function and the changes associated with aging, injury and disease requires the ability to quantitatively measure skeletal kinematics. The current state-of-the art for quantifying skeletal kinematics – especially the complex motion at the joint surface, called arthrokinematics – is image-based object tracking performed with datasets from biplane videoradiography (BVR), and static and dynamic computed tomography (3DCT and 4DCT, respectively). Regardless of the imaging modality, image- based skeletal tracking involves image segmentation and bone model generation, bone image registration, coordinate system selection, and data presentation. Software and computing infrastructure are critical for accuracy and efficiency. The lack of “industry-standard” software or templates for workflow are major obstacles to progress in the field. Laboratories use their own combination of commercial, public-domain, and custom- written code. The current individualized implementation model is inefficient, duplicates effort, and impedes collaboration, and, importantly, the sharing of software and technical advances. Recent focus workshops and surveys demonstrate clear interest in better solutions. Accordingly, based on our longstanding expertise in image-based tracking, we will develop an open source program for image-based skeletal motion tracking capable of accepting as input all of the commonly used imaging modalities (videoradiography, 3DCT, and 4DCT). Our long-term objective is to build a world-wide user base of collaborators and contributors to foster innovation and inquiry in musculoskeletal research. In our first Aim we will partner with Kitware, Inc. an experienced and successful open-source software development company, to refine and enhance Autoscoper, and integrate it into the 3D Slicer platform to yield SlicerAutoscoperM (SAM). Autoscoper is an existing BVR software program developed at Brown University to semi-automatically align skeletal structures (bones and implants) to x-ray videos. SAM will be refined with input from the project’s co-investigators and an established core user base. In Aim 2 we will determine the agreement and accuracy of SAM by comparing its outputs to those of obtained using legacy methods, using data from existing studies performed in four independent laboratories. Finally, in Aim 3 we will use a synthetic model to evaluate the accuracy of SAM in round-robin testing in four labs (Brown, Cleveland Clinic, Mayo Clinic, and Queens Universiyt) using image data from 3DCT, 4DCT and BVR. The work outlined in this proposal will yield a state-of-the-art, open-source software solution that will accept datasets from multiple imaging modalities. SAM will simplify and improve image-based skeletal tracking, facilitate the sharing of novel analysis algorithms, methodologies, and data, and hasten the translation to clinical implementation.

抽象的 该R01应用程序的目的是开发最先进的开源软件，用于基于图像的分析 骨骼运动学。在全球范围内，超过2.5亿人患有肌肉骨骼疾病，包括 关节炎，创伤，骨质疏松症和脊柱病理学，这一数字预计会随着人群而增加 年龄。对正常关节功能的深入了解以及与衰老，伤害和相关的变化 疾病需要定量测量骨骼运动学的能力。当前的最新技术 量化骨骼运动学，尤其是关节表面的复杂运动，称为节肢动物 - 是 基于图像的对象跟踪通过Biplane视频摄影（BVR）的数据集和静态和 动态计算机断层扫描（分别为3DCT和4DCT）。无论成像方式如何，图像 - 基于的骨骼跟踪涉及图像分割和骨骼模型的产生，骨骼图像登记， 坐标系统选择和数据表示。软件和计算基础架构对于 准确性和效率。缺乏“行业标准”软件或工作流的模板是主要障碍 在现场进展。实验室使用自己的商业，公共域和定制的结合 - 书面代码。当前的个性化实施模型效率低下，重复努力并阻碍 协作，重要的是，软件和技术进步的共享。最近的焦点研讨会和 调查对更好的解决方案表现出明显的兴趣。彼此之间，根据我们的长期专业知识 基于图像的跟踪，我们将开发一个开源程序，用于基于图像的骨骼运动跟踪 接受作为输入的所有常用成像方式（视频摄影，3DCT和4DCT）。我们的 长期目标是建立一个全球合作者的用户群，以及促进创新和贡献者 肌肉骨骼研究的询问。在我们的第一个目标中，我们将与Kitware，Inc。合作。 成功的开源软件开发公司，完善和增强自动生态，并将其集成到 3D切片机平台生产Slicerautoscoperm（SAM）。 Autoscoper是现有的BVR软件程序 在布朗大学开发至半自动的骨骼结构（骨头和凹凸）与X射线 视频。 SAM将通过项目的共同投资者和已建立的核心用户群的输入来完善。在 AIM 2我们将通过将其输出与使用的输出进行比较来确定其一致性和准确性 传统方法，使用来自四个独立实验室进行的现有研究的数据。最后，在目标3中 我们将使用合成模型来评估SAM在四个实验室中的旋转蛋白测试中的准确性（棕色， Cleveland Clinic，Mayo Clinic和Queens Universiyt使用来自3DCT，4DCT和BVR的图像数据。工作 在此提案中概述将产生最先进的开源软件解决方案，该解决方案将接受来自 多种成像方式。 SAM将简化并改善基于图像的骨骼跟踪，促进共享 新颖的分析算法，方法和数据，并加快转化为临床实施。