Multi-modal tracking of in vivo skeletal structures and implants

Ended Project Grant Awarded Last action

Award ID
R01AR078924
Recipient
Awarding agency
Obligated
$9,299,677

Award facts

Completion date
Originally
Funding
  • $8,460,858.00 Federal
  • $838,819.00 Non-federal
Place of performance
Rhode Island, USA

Award details come from USAspending.gov.

Description

As the agency reported it.

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.

Transactions

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Subawards

Subrecipient Amount Awarded
Queens University At Kingston Multi-modal Tracking of In Vivo Skeletal Structures and Implants $42,860
Mayo Clinic $67,000
Kitware, Inc. $274,397

Quick answers

Who received award R01AR078924?

R01AR078924 was awarded to Rhode Island Hospital by National Insititue of Arthritis and Musculoskeletal and Skin Diseases (National Institutes of Health) on April 15, 2022.

How much is award R01AR078924 worth?

$9,299,677 has been obligated on R01AR078924: $8,460,858 in federal funding and $838,819 in non-federal funding.

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