Computed tomography-based joint locations affect calculation of joint moments during gait when compared to scaling approaches

Abstract

Hip joint moments are an important parameter in the biomechanical evaluation of orthopaedic surgery. Joint moments are generally calculated using scaled generic musculoskeletal models. However, due to anatomical variability or pathology, such models may differ from the patient's anatomy, calling into question the accuracy of the resulting joint moments. This study aimed to quantify the potential joint moment errors caused by geometrical inaccuracies in scaled models, during gait, for eight test subjects. For comparison, a semi-automatic computed tomography (CT)-based workflow was introduced to create models with subject-specific joint locations and inertial parameters. 3D surface models of the femora and hemipelves were created by segmentation and the hip joint centres and knee axes were located in these models. The scaled models systematically located the hip joint centre (HJC) up to 33.6 mm too inferiorly. As a consequence, significant and substantial peak hip extension and abduction moment differences were recorded, with, respectively, up to 23.1% and 15.8% higher values in the image-based models. These findings reaffirm the importance of accurate HJC estimation, which may be achieved using CT- or radiography-based subject-specific modelling. However, obesity-related gait analysis marker placement errors may have influenced these results and more research is needed to overcome these artefacts.

Reliability of functional and predictive methods to estimate the hip joint centre in human motion analysis in healthy adults

“…dwarfism) or induce asymmetry in the HJCs, and therefore would not be suitable for certain patient populations. Seventh, joint kinetic analyses were not included in this study although variations in the HJC location have been shown to impact on joint moments [28][29][30]. Eighth, different marker sets and/or marker locations could increase or decrease STA and therefore lead to slightly different results.…”

Accuracy and kinematics consistency of marker-based scaling approaches on a lower limb model: a comparative study with imagery data

“…Segment lengths of MB/MB a:m à : and MB l:a:m: models were compared to the EB/EB a:m à : reference models and to the literature (Bartels et al 2015;Kainz et al 2017): namely inter-hip distance (right to left HJC), femur lengths (HJC to KJC) and shank lengths (KJC to AJC). A non-parametric Friedman's test verified if the segment lengths of the five models among subjects were significantly different (p < 0.05).…”

“…Models based on medical images are the current gold standard, they provide the most accurate geometrical parameters (Scheys et al 2006;Blemker et al 2007;Valente et al 2014). In such methods, data from MRI (Kainz et al 2016(Kainz et al , 2017Halonen et al 2017), EOS V R (Cl ement et al 2015), or CT-scans (Bartels et al 2015;Marra et al 2015) was used to reconstruct 3D bone geometries through image segmentation manually (Valente et al 2014) or semi-automatically (Scheys et al 2005). Generally, imagery data acquisition and post-processing is time-consuming.…”

“…A few studies proposed direct validations of segment lengths scaled from optoelectronic data with medical images. For example, in Kainz et al (2017) and Bartels et al (2015), the Delp leg model (Delp et al 1990) was linearly scaled with the Opensim software and segment lengths were compared to CTscans and MRI extractions. The studies highlighted differences between linear scaling and medical imagery up to respectively 30 and 100 mm for the femur length.…”

In-vivo quantification of dynamic hip joint center errors and soft tissue artifact

“…Biomechanical models generate predictions of important scientific and clinical variables related to the study of human movement [1–3]. In these models, joint centers define the linkage between neighboring body segments.…”