Estimation of spinal joint centers from external back profile and anatomical landmarks

Abstract

Defining a subject-specific model of the human body is required for motion analysis in many fields, such as in ergonomics and clinical applications. However, locating internal joint centers from external characteristics of the body still remains a challenging issue, in particular for the spine. Current methods mostly require a set of rarely accessible (3D back or trunk surface) or operator dependent inputs (large number of palpated landmarks and landmarks-based anthropometrics). Therefore, there is a need to provide an alternative way to estimate joint centers only using a limited number of easily palpable landmarks and the external back profile. Two methods were proposed to predict the spinal joint centers: one using only 6 anatomical landmarks (ALs) (2 PSIS, T8, C7, IJ and PX) and one using both 6 ALs and the external back profile. Regressions were established using the X-ray based 3D reconstructions of 80 subjects and evaluated on 13 additional subjects of variable anthropometry. The predicted location of joint centers showed an average error 9.7 mm (±5.0) in the sagittal plane for all joints when using the external back profile. Similar results were obtained without using the external back profile, 9.5 mm (±5.0). Compared to other existing methods, the proposed methods offered a more accurate prediction with a smaller number of palpated points. Additional methods have to be developed for considering postures other than standing, such as a sitting position.

Validity of flexicurve and motion capture for measurements of thoracic kyphosis vs standing radiographic measurements

“…A limitation of the prediction techniques P.Nerot , P.Nerot.Ext , and P.Furlanetto is that they were developed using only radiographic data, with “virtual palpation” of skin surface points 23,24 . We utilized motion analysis markers as the input data for these predictions, but the potential errors arising from this is unknown.…”

“…We utilized motion analysis markers as the input data for these predictions, but the potential errors arising from this is unknown. Additionally, Nerot et al 24 used the location of the xyphoid process as an input, but xyphoid process location was not measured in this study. This deviation from the published procedure may have degraded the accuracy of our vertebral body centroid and kyphosis angle estimates.…”

“…Three different prediction techniques were used to predict a kyphosis angle based on non‐radiographic measures. Internal spine profile ( P.Nerot; P.Nerot.Ext ): Nerot et al 24 provided two sets of prediction equations that determined spine joint centers. Both sets of equations use body weight, height, and distances between certain boney landmarks.…”

“…Greendale et al 22 calibrated the flexicurve kyphosis angle to the radiographic Cobb angle, while Furlanetto et al 23 calibrated skin‐surface curvature to the centroid angle. Nerot et al 24 established methods to predict the location of intervertebral joint centers from motion‐analysis marker data. These joint centers can be used to calculate a kyphosis angle.…”

Quasi-automated reconstruction of the femur from bi-planar X-rays

“…A first Gaussian Process denoted GP 1 is built from 120 femurs manually reconstructed from bi-planar X-rays (Nerot et al 2017). They are aligned on their barycentre, encoding this way the rigid rotation prior.…”