Abstract
INTRODUCTION
To assess and compare the effect of new orthopedic surgical procedures, in vitro evaluation remains critical during the pre-clinical validation. Focusing on reconstruction surgery, the ability to restore normal kinematics and stability is thereby of primary importance. Therefore, several simulators have been developed to study the kinematics and create controlled boundary conditions.
To simultaneously capture the kinematics in six degrees of freedom as outlined by Grood & Suntay, markers are often rigidly connected to the moving bone segments. The position of these markers can subsequently be tracked while their position relative to the bones is determined using computed tomography (CT) of the test specimen with the markers attached. Although this method serves as golden standard, it clearly lacks real-time feedback. Therefore, this paper presents the validation of a newly developed real-time framework to assess knee kinematics at the time of testing.
MATERIALS & METHODS
A total of five cadaveric fresh frozen lower limb specimens have been used to quantitatively assess the difference between the golden standard, CT based, method and the newly developed real-time method. A schematic of the data flow for both methods. Prior to testing, both methods require a CT scan of the full lower limb. During the tests, the proximal femur and distal tibia are necessarily resected to fit the knees in the test setup, thus also removing the anatomical landmarks needed to evaluate their mechanical axis. Subsequently, a set of three passive markers are rigidly attached to the femur and tibia, referred to as M3F and M3T respectively. For the CT based method, the marker positions are captured during the tests and a second CT scan is eventually performed to link the marker positions to the knee anatomy. Using in-house developed software, this allowed to offline evaluate the knee kinematics in six degrees of freedom by combining both CT datasets with the tracked marker positions. For the newly developed real-time method, a calibration procedure is first performed. This calibration aims to link the position of the 3D reconstructed bone and landmarks with the attached markers. A set of bone surface points is therefore registered. These surface points are obtained by tracking the position of a pen while touching the bone surface. The pen's position is thereby tracked by three rigidly attached markers, denoted M3P. The position of the pen tip is subsequently calculated from the known pen geometry. The iterative closest point (ICP) algorithm is then used to match the 3D reconstructed bone to the registered surface points. Two types of 3D reconstructions have therefore been considered. First, the original reconstructions were used, obtained from the CT data. Second, a modified reconstruction was used. This modification accounted for the finite radius (r = 1.0 mm) of the registration pen, by shifting the surface nodes 1.0 mm along the direction of the outer surface normal. During the tests, the positions of the femur and tibia markers are tracked and streamed in real-time to an in-house developed, Matlab based software framework (MathWorks Inc., Natick, Massachussets, USA). This software framework simultaneously calculates the bone positions and knee kinematics in six degrees of freedom, displaying this information to the surgeons and operators. To assess the accuracy, all knee specimens have been subjected to passive flexion-extension movement ranging from 0 to 120 degrees of flexion. For each degree of freedom, the average root mean square (RMS) difference between both measurement methods has been evaluated during this movement. In addition, the distribution of the registered surface points has been assessed along the principal directions of the uniformly meshed 3D reconstructions (average mesh size of 1.0 mm).
RESULTS
The root mean square difference between both measurements indicates a strong dependency on the variance of the registered points. This dependency is particularly pronounced when using the original 3D reconstructions in combination with the ICP algorithm, with an R2 = 0.76 and 0.85 for the translational and rotational degrees of freedom respectively. When using the modified 3D reconstructions, which compensates for the finite radius of the marker tip, this dependency becomes negligible (R2 = 0.10 and 0.05). Using this modified 3D reconstruction, the average difference between both measurements is also reduced to an average value of 1.20 degrees and 1.47 mm.
DISCUSSION
The difference in kinematic parameters between both measurement techniques is an order of magnitude lower than the claimed accuracy of the motion tracking cameras. However, the difference is in line with the inter- and intra- observer variability when identifying bony landmarks around the knee. Since these landmarks are essential to calculate knee kinematics, it is understood that the proposed real-time system is sufficiently accurate to study these kinematics.
