An Anatomical-Based Subject-Specific Model of In-Vivo Knee Joint 3D Kinematics From Medical Imaging

Abstract

Biomechanical models of the knee joint allow the development of accurate procedures as well as novel devices to restore the joint natural motion. They are also used within musculoskeletal models to perform clinical gait analysis on patients. Among relevant knee models in the literature, the anatomy-based spatial parallel mechanisms represent the joint motion using rigid links for the ligaments’ isometric fibres and point contacts for the articular surfaces. To customize analyses, therapies and devices, there is the need to define subject-specific models, but relevant procedures and their accuracy are still questioned. A procedure is here proposed and validated to define a customized knee model based on a spatial parallel mechanism. Computed tomography, magnetic resonance and 3D-video-fluoroscopy were performed on a healthy volunteer to define the personalized model geometry. The model was then validated by comparing the measured and the replicated joint motion. The model showed mean absolute difference and standard deviations in translations and rotations, respectively of 0.98 ± 0.40 mm and 0.68 ± 0.29 ° for the tibia–femur motion, and of 0.77 ± 0.15 mm and 2.09 ± 0.69 ° for the patella–femur motion. These results show that accurate personalized spatial models of knee kinematics can be obtained from in-vivo imaging.

Subject‐specific muscle properties from diffusion tensor imaging significantly improve the accuracy of musculoskeletal models

“…However, such subject‐specific models are currently much more expensive and time‐consuming to produce relative to generic‐based or ‘averaged’ models and as such have rarely been used to this extent in any species. However, research into the validity of subject‐specific models of the human musculoskeletal system is becoming more widespread, as it is thought that such models may be more accurate for certain tasks than the often used generic or scaled generic models (Lenaerts et al ., 2008; Scheys et al ., 2008; Scheys et al ., 2009; Scheys et al ., 2011; Valente et al ., 2014; Navacchia et al ., 2016; Prinold et al ., 2016; Dejtiar et al ., 2020; Gu and Pandy, 2020; Modenese and Kohout, 2020; Nardini et al ., 2020). For example, models with subject‐specific musculoskeletal geometry have been shown to be more effective for predicting muscle moment arms and joint contact forces, with respective differences of 36% (Scheys et al ., 2008) and 0.61 xBW (Lenaerts et al ., 2008) relative to generic models.…”

Reconstruction of Three-Dimensional Tibiofemoral Kinematics Using Single-Plane Fluoroscopy and a Personalized Kinematic Model

“…The approach was featured by its direct connection with anatomical structures (i.e., the ligaments and articular contacts) and capability to customize to individual subjects and replicate joint passive motion [15]. Recent studies have shown that the knee model based on a spatial parallel mechanism can be personalized by using personal medical images and 3D kinematics data [16,17]. Accurate representation of knee articular features has shown its potential to drive accurate 3D knee joint kinematics during non-weight-bearing knee flexion and extension motions with submillimeter and subdegree errors [17].…”

Development and evaluation of a new methodology for Soft Tissue Artifact compensation in the lower limb

“…However, regardless of the joint constraints imposed, generic (unpersonalized) model-derived kinematics were shown inaccurate (knee kinematic error up to 17° and 8 mm) as these models could not adapt to patient-specific geometry, particularly in pathological conditions (Clément et al, 2017). On the other hand, personalization of model geometry based on medical images was shown promising in improving joint kinematics accuracy (Assi et al, 2016; Clément et al, 2015; Nardini et al, 2020).…”