RESUMO
Osteophytes are routinely removed during total knee arthroplasty, yet the preoperative planning currently relies on preoperative computed tomography (CT) scans of the patient's osteoarthritic knee, typically including osteophytic features. This complicates the surgeon's ability to anticipate the exact biomechanical effects of osteophytes and the consequences of their removal before the operation. The aim of this study was to investigate the effect of osteophytes on ligament strains and kinematics, and ascertain whether the osteophyte volume and location determine the extent of this effect. We segmented preoperative CT scans of 21 patients, featuring different osteophyte severity, using image-based active appearance models trained to identify the osteophytic and preosteophytic bone geometries and estimate the cartilage thickness in the segmented surfaces. The patients' morphologies were used to scale a template musculoskeletal knee model. Osteophytes induced clinically relevant changes to the knee's functional behavior, but these were variable and patient-specific. Generally, severe osteophytic knees significantly strained the oblique popliteal ligament (OPL) and posterior capsule (PC) relative to the preosteophytic state. Furthermore, there was a marked effect on the lateral collateral ligament and anterolateral ligament (ALL) strains compared to mild and moderate osteophytic knees, and concurrent alterations in the tibial lateral-medial translation and external-internal rotation. We found a strong correlation between the OPL, PC, and ALL strains and posterolateral condylar and tibial osteophytes, respectively. Our findings may have implications for the preoperative planning in total knee arthroplasty, toward reproducing the physiological knee biomechanics as close as feasibly possible.
RESUMO
Robotic-assisted total knee arthroplasty can attain highly accurate implantation. However, the target for optimal positioning of the components remains debatable. One of the proposed targets is to recreate the functional status of the pre-diseased knee. The aim of this study was to demonstrate the feasibility of reproducing the pre-diseased kinematics and strains of the ligaments and, subsequently, use that information to optimize the position of the femoral and tibial components. For this purpose, we segmented the pre-operative computed tomography of one patient with knee osteoarthritis using an image-based statistical shape model and built a patient-specific musculoskeletal model of the pre-diseased knee. This model was initially implanted with a cruciate-retaining total knee system according to mechanical alignment principles; and an optimization algorithm was then configured seeking the optimal position of the components that minimized the root-mean-square deviation between the pre-diseased and post-operative kinematics and/or ligament strains. With concurrent optimization for kinematics and ligament strains, we managed to reduce the deviations from 2.4 ± 1.4 mm (translations) and 2.7 ± 0.7° (rotations) with mechanical alignment to 1.1 ± 0.5 mm and 1.1 ± 0.6°, and the strains from 6.5% to lower than 3.2% over all the ligaments. These findings confirm that adjusting the implant position from the initial plan allows for a closer match with the pre-diseased biomechanical situation, which can be utilized to optimize the pre-planning of robotic-assisted surgery.
