Multi-modal Tracking of In Vivo Skeletal Structures and Implants

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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.