Abstract
Three-dimensional (3D) kinematic analysis plays an important role in improving diagnosis and in the evaluation of treatments and surgical procedures. For example, measuring the 3D kinematics of knee joints is essential for understanding their normal function and diagnosing any pathology, such as ligament injury and osteoarthritis. Image registration is a method which can be used to compute kinematic measurements without involving the introduction of instruments into the body. However, in these techniques, the trade-off between accuracy and computation time is still a challenging problem which needs to be addressed. In this paper, a fast and robust registration method is proposed for the measurement of post-operative knee joint kinematics. Using this method, after total knee arthroplasty (TKA) surgery, a 3D knee implant model can be registered with a number of single-plane fluoroscopy frames of the patients’ knee. Generally, when the number of fluoroscopy frames is quite high, the computation cost for the registration between the frames and a 3D model is expensive. Therefore, in order to speed up the registration process, we apply an interpolation-based prediction method, to initialize and estimate the 3D positions of the 3D model in each fluoroscopy frame. The estimated 3D positions are then fine-tuned. The experimental results, which were performed on the knee joints of 18 patients post-surgery, show that the computational time required to register each frame for each bone using our proposed method is only 67 seconds, which is much faster (almost 6.5 times faster) than the best existing registration method (a registration method based on sum of conditional variance (SCV) similarity measure) while maintaining almost the same accuracy. The average of the mean difference ± standard deviation of the proposed method for femoral and tibial bones for translation and rotation parameters are 0.0603 ± 0.2966 (mm) and −0.0069 ± 0.2922 (degree) respectively.
Highlights
T HE success of total knee arthroplasty (TKA), which is a surgical procedure for restoring the function of a knee joint, is commonly evaluated using the level of satisfaction with the outcome reported by the patient
INVESTIGATION ON TRANSFORMATION PARAMETERS The mean difference (MD), the standard deviation and the mean of the absolute difference (MAD) between the registration transformation parameters computed by the proposed methods and the method base on SCV are shown in table 2
Analysing human joint kinematics plays an important role in many clinical settings and 2D to 3D registration has been shown to be an effective method for measuring these parameters
Summary
T HE success of total knee arthroplasty (TKA), which is a surgical procedure for restoring the function of a knee joint, is commonly evaluated using the level of satisfaction with the outcome reported by the patient. The 3D search ranges of the in-plane parameters Tx, Ty and Rz were set to [-4,4] pixels, [-4,4] pixels and [-6,6] degrees respectively where the number of values searched in each range of Tx, Ty is 9, and in the range for Rz is 25 (the difference between each consecutive value for Tx, Ty is 1 and 0.5 for Rz ) This step improves the results because, when there is a large displacement between the model’s positions, the appearance of the DRR generated by translating a 2D DRR can be distorted when compared with the real DRR generated by projecting the displaced 3D model using a perspective projection. In addition to the curves of the six transformation parameters, to assess the estimated registration results for the new frames, an NEPD value curve for the previous transformed registered frames is calculated and updated after each iteration It uses measurements of the NEPD values which compute the similarity between the DRRs of the transformed 3D models and the fluoroscopy frames which are registered.
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