Multi-modal Tracking of In Vivo Skeletal Structures and Implants

摘要 这个R01应用程序的目标是开发最先进的、开源的软件，用于基于图像的分析 骨骼运动学。在全世界，超过2.5亿人受到肌肉骨骼疾病的影响，包括 关节炎、创伤、骨质疏松症和脊柱病理，这些疾病的数量预计会随着人口的增加而增加 年龄。深入了解正常的关节功能以及与衰老、损伤和 疾病需要定量测量骨骼运动学的能力。目前最先进的技术 量化骨骼运动学-特别是关节表面的复杂运动，称为关节运动学-是 使用来自双平面视频放射成像(BVR)的数据集执行的基于图像的对象跟踪，以及静态和 动态CT(3DCT和4DCT)。不管是哪种成像方式，图像- 基于骨骼的跟踪包括图像分割和骨骼模型生成，骨骼图像配准， 坐标系选择和数据表示。软件和计算基础设施对于 精确度和效率。缺乏用于工作流程的“行业标准”软件或模板是主要障碍 在这一领域取得进步。实验室使用自己的商业、公共领域和定制的组合- 编写代码。当前的个性化实施模式效率低下，重复劳动，阻碍了 合作，以及，重要的是，分享软件和技术进步。最近的焦点研讨会和 调查显示，人们显然对更好的解决方案感兴趣。因此，基于我们在以下领域的长期专业知识 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功能 接受所有常用的成像方式(视频放射摄影、3DCT和4DCT)作为输入。我们的 长期目标是建立全球范围的合作者和贡献者用户基础，以促进创新和 肌肉骨骼研究中的问题。在我们的第一个目标中，我们将与Kitware，Inc.合作，一个经验丰富的 成功的开源软件开发公司，对Autoscope进行提炼和增强，并将其集成到 3D切片器平台，以产生切片器自动运行管理(SAM)。Autoscoper是一个现有的BVR软件程序 由布朗大学开发，用于半自动将骨骼结构(骨骼和植入物)与X射线对准 录像。SAM将根据项目合作调查员的意见和已建立的核心用户基础进行完善。在……里面 目标2我们将通过比较SAM的输出和使用的结果来确定SAM的一致性和准确性 遗留方法，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在四个实验室的循环测试中评估SAM的准确性(Brown， 克利夫兰诊所、梅奥诊所和皇后大学)使用来自3DCT、4DCT和BVR的图像数据。这项工作 此建议书中概述的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将简化和改进基于图像的骨骼跟踪，方便共享 对新的分析算法、方法和数据进行分析，并加速将其转化为临床实施。

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.