Static and dynamic error of a biplanar videoradiography system using marker-based and markerless tracking techniques

Abstract

The use of biplanar videoradiography technology has become increasingly popular for evaluating joint function in vivo. Two fundamentally different methods are currently employed to reconstruct 3D bone motions captured using this technology. Marker-based tracking requires at least three radio-opaque markers to be implanted in the bone of interest. Markerless tracking makes use of algorithms designed to match 3D bone shapes to biplanar videoradiography data. In order to reliably quantify in vivo bone motion, the systematic error of these tracking techniques should be evaluated. Herein, we present new markerless tracking software that makes use of modern GPU technology, describe a versatile method for quantifying the systematic error of a biplanar videoradiography motion capture system using independent gold standard instrumentation, and evaluate the systematic error of the W.M. Keck XROMM Facility's biplanar videoradiography system using both marker-based and markerless tracking algorithms under static and dynamic motion conditions. A polycarbonate flag embedded with 12 radio-opaque markers was used to evaluate the systematic error of the marker-based tracking algorithm. Three human cadaveric bones (distal femur, distal radius, and distal ulna) were used to evaluate the systematic error of the markerless tracking algorithm. The systematic error was evaluated by comparing motions to independent gold standard instrumentation. Static motions were compared to high accuracy linear and rotary stages while dynamic motions were compared to a high accuracy angular displacement transducer. Marker-based tracking was shown to effectively track motion to within 0.1 mm and 0.1 deg under static and dynamic conditions. Furthermore, the presented results indicate that markerless tracking can be used to effectively track rapid bone motions to within 0.15 deg for the distal aspects of the femur, radius, and ulna. Both marker-based and markerless tracking techniques were in excellent agreement with the gold standard instrumentation for both static and dynamic testing protocols. Future research will employ these techniques to quantify in vivo joint motion for high-speed upper and lower extremity impacts such as jumping, landing, and hammering.

Figures

Fig. 1

Illustrated representation of the experimental testing environment within the W. M. Keck Foundation XROMM Facility. The dynamic testing apparatus is positioned within the field of view determined by the overlapping X-ray beams. A representative X-ray beam is illustrated with dotted lines projecting from one of the X-ray sources.

Fig. 2

XROMM Autoscoper 3D software environment. This figure illustrates the before ((a) and (b)) and after ((c) and (d)) results obtained from the auto-registration algorithm using an initial guess that was extrapolated from the previous frames. Additionally, the constrained-axis rotation and translation manipulators are shown in (a) and (b) and (c) and (d), respectively. The Autoscoper software (executable and source) is publically available.

Fig. 3

Static (a) and dynamic (b) testing apparatus. Both apparatuses were rigidly fixed to a concrete pedestal (Fig. 1) for all static and dynamic testing.

Fig. 4

Images displaying the morphology of the three bones used in this study. Panels (a), (b), and (c) are the 3D CT models of the distal femur, distal radius, and distal ulna, respectively.

Fig. 5

Static error results. (a) and (b). Box and whiskers rotational (a) and translational (b) plot displaying range, 25–75 percentile, and median static error for each specimen. (c) and (d). Mean (+1 SD) rotational (c) and translational (d) absolute static error for each specimen.

Fig. 6

Cumulative distributions of all velocities and accelerations tested during the dynamic protocols. The full cumulative distributions of velocities and accelerations are shown in (a) and (c), respectively. The majority of velocities and accelerations are shown in (b) and (d), respectively. These data are taken from the data windowed by the vertical dotted lines present in (a) and (c).

Fig. 7

Dynamic error results. (a) Box and whiskers plot displaying range, 25–75 percentile, and median dynamic error for each specimen. (b) Mean (+1 SD) absolute dynamic error for each specimen.