Abstract
Finite element (FE) modeling is becoming an increasingly popular method for analyzing knee joint mechanics and biomechanical mechanisms leading to osteoarthritis (OA). The most common and widely available imaging method for knee OA diagnostics is planar X-ray imaging, while more sophisticated imaging methods, e.g., magnetic resonance imaging (MRI) and computed tomography (CT), are seldom used. Hence, the capability to produce accurate biomechanical knee joint models directly from X-ray imaging would bring FE modeling closer to clinical use. Here, we extend our atlas-based framework by generating FE knee models from X-ray images (N = 28). Based on measured anatomical landmarks from X-ray and MRI, knee joint templates were selected from the atlas library. The cartilage stresses and strains of the X-ray-based model were then compared with the MRI-based model during the stance phase of the gait. The biomechanical responses were statistically not different between MRI- vs. X-ray-based models when the template obtained from X-ray imaging was the same as the MRI template. However, if this was not the case, the peak values of biomechanical responses were statistically different between X-ray and MRI models. The developed X-ray-based framework may pave the way for a clinically feasible approach for knee joint FE modeling.
Highlights
Osteoarthritis (OA) is one of the major causes of disability in elderly people.[17]
There was no significant difference (p > 0.05) between the mean values of maximum medial (4.40 vs. 4.38 mm [95% confidence intervals (CIs) for the difference 2 0.06, 0.02]) and lateral (5.29 vs. 5.25 mm [95% CI 2 0.002, 0.941]) joint space width (JSW) between X-ray and magnetic resonance (MR) images
The mean values of maximum AP dimensions of both medial (63.72 vs. 55.87 mm [95% CI 7.15, 8.54]) and lateral (66.43 vs. 63.72 mm [95% CI 2.24, 3.16]) elliptical-shaped condyles were greater in X-ray compared to magnetic resonance imaging (MRI) (p < 0.001)
Summary
Osteoarthritis (OA) is one of the major causes of disability in elderly people.[17]. Computational finite element (FE) models have been utilized to quantitatively estimate biomechanical factors applied to the soft tissue of the knee joint during different loading conditions.[1,2,13] There is considerable evidence that local tissue stresses and strains are one of the driving factors for knee OA.[3,11] In recent years, these simulated biomechanical responses have been utilized in predictive FE models to predict biomechanically-driven progression of knee OA based on clinical imaging data.[10,33] In those studies, subject-specific 3-D joint geometries of the models have been based on computed tomography (CT) or magnetic resonance imaging (MRI). There is considerable evidence that local tissue stresses and strains are one of the driving factors for knee OA.[3,11]. In recent years, these simulated biomechanical responses have been utilized in predictive FE models to predict biomechanically-driven progression of knee OA based on clinical imaging data.[10,33]. These simulated biomechanical responses have been utilized in predictive FE models to predict biomechanically-driven progression of knee OA based on clinical imaging data.[10,33] In those studies, subject-specific 3-D joint geometries of the models have been based on computed tomography (CT) or magnetic resonance imaging (MRI). Planar radiographs are the primary imaging modality for knee OA diagnostics,[16] their compatibility in the modeling workflow would be a valuable asset. The capability to generate 3-D knee joint models from 2-D radiographs could increase the scalability of the FE modeling to large patient groups
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