Preprint / Version 1

Ultrasound-based Dynamic Bone Tracking to Enhance Clinical Assessments of Knee Kinematics


  • Matthew Blomquist University of Wisconsin-Madison
  • Joshua Roth Orthopedics and Rehabilitation



Purpose: Measuring joint kinematics in the clinic is important for diagnosing injuries, tracking healing, and guiding treatments. However, current methods for measuring joint kinematics are limited by accuracy and/or feasibility of widespread clinical adoption. Therefore, the purpose of this study was to develop and validate an ultrasound-based bone-tracking algorithm to track bone motion and assess kinematics during simulated clinical assessments.

Methods: We mimicked four standard laxity assessments (i.e., anterior, posterior, varus, valgus) on five human cadaver knees using a robotic testing system. We simultaneously collected B-mode cine loops with an ultrasound transducer aligned to image in the plane of the applied load. We used the bone-tracking algorithm to estimate the change in knee kinematics throughout each laxity test to assess the potential of using ultrasound to estimate dynamic knee kinematics. Additionally, we conducted additional studies to test the repeatability of measuring laxity with the transducer at the same position and the reproducibility of measuring laxity at different transducer positions.

Results: Using the bone-tracking algorithm to estimate changes in knee kinematics, we computed the maximum root-mean-square errors of our bone-tracking algorithm to be 2.2 mm and 1.2° for the anterior-posterior and varus-valgus laxity assessments, respectively. Repeated laxity assessments proved to have good to excellent repeatability, while ICCs from repositioning the transducer varied more widely, ranging from poor to good reproducibility.

Conclusions: Ultrasound is an imaging modality capable of tracking knee kinematics in both anterior-posterior and varus-valgus degrees-of-freedom. Since ultrasound is widely used in both clinical and research settings, our ultrasound-based bone-tracking algorithm has the potential to assess knee kinematics in a variety of applications, such as diagnosing disorders, monitoring healing, and informing rehabilitation.


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