Assessment of knee kinematics with dynamic radiostereometry: Validation of an automated model‐based method of analysis using bone models

Abstract

Radiostereometic analysis (RSA) is a precise method for the functional assessment of joint kinematics. Traditionally, the method is based on tracking of surgically implanted bone markers and analysis is user intensive. We propose an automated method of analysis based on models generated from computed tomography (CT) scans and digitally reconstructed radiographs. The study investigates method agreement between marker‐based RSA and the CT bone model‐based RSA method for assessment of knee joint kinematics in an experimental setup. Eight cadaveric specimens were prepared with bone markers and bone volume models were generated from CT‐scans. Using a mobile fixture setup, dynamic RSA recordings were obtained during a knee flexion exercise in two unique radiographic setups, uniplanar and biplanar. The method agreement between marker‐based and CT bone model‐based RSA methods was compared using bias and LoA. Results obtained from uniplanar and biplanar recordings were compared and the influence of radiographic setup was considered for clinical relevance. The automated method had a bias of −0.19 mm and 0.11° and LoA within ±0.42 mm and ±0.33° for knee joint translations and rotations, respectively. The model pose estimation of the tibial bone was more precise than the femoral bone. The radiographic setup had no clinically relevant effect on results. In conclusion, the automated CT bone model‐based RSA method had a clinical precision comparable to that of marker‐based RSA. The automated method is non‐invasive, fast, and clinically applicable for functional assessment of knee kinematics and pathomechanics in patients.

Distal radioulnar joint stabilization with open foveal reinsertion versus tendon graft reconstruction: an experimental study using radiostereometry

“…This study demonstrates the feasibility of radiostereometric imaging and AutoRSA analysis in an experimental setup, a non-invasive CT bone model-based method, for precise quantification of DRUJ translation before and after surgical treatment. Dynamic radiostereometry and AutoRSA analysis is an innovative method that has been proven feasible for studies of kinematics of other joints [10,13]. In a clinical perspective, a valid imaging and analysis method for examination of DRUJ translation in patients is demanded.…”

Patients with knee osteoarthritis can be divided into subgroups based on tibiofemoral joint kinematics of gait – an exploratory and dynamic radiostereometric study

“…The gait trials were recorded with a dedicated dRSA system (AdoraRSA; NRT X-Ray A/S, Hasselager, Denmark) with a previously described biplanar setup 13 . The radiation exposure parameters were set at 90 kVp, 600 mA, and a pulse-width of 2.5 ms, utilizing the highest frame rate of 15 Hz without compromising the image size.…”

“…The series of stereoradiographs were analyzed using an automated software system developed at our institution (AutoRSA software, Orthopaedic Research Unit, Aarhus, Denmark). The software utilized a digitally reconstructed radiographic (DRR) registration method to estimate bone pose from virtually generated projections using mathematical optimization algorithms, as previously described in more detail 13,16,18 . The optimal pose was found by minimizing the difference between virtually generated DRR images from a 3D volume to actual stereoradiographs using a normalized gradient correlation approach 13,18,19 .…”

“…The software utilized a digitally reconstructed radiographic (DRR) registration method to estimate bone pose from virtually generated projections using mathematical optimization algorithms, as previously described in more detail 13,16,18 . The optimal pose was found by minimizing the difference between virtually generated DRR images from a 3D volume to actual stereoradiographs using a normalized gradient correlation approach 13,18,19 . Prior to the DRR registration process, the automated bone pose initialization between frames deviated from previously described studies 13,16,18 by utilizing the skin-attached marker trajectories.…”

“…The optimal pose was found by minimizing the difference between virtually generated DRR images from a 3D volume to actual stereoradiographs using a normalized gradient correlation approach 13,18,19 . Prior to the DRR registration process, the automated bone pose initialization between frames deviated from previously described studies 13,16,18 by utilizing the skin-attached marker trajectories.…”