No abstract

The aim of this study was to propose a novel method for reconstructing the external body envelope from the low dose biplanar X-rays of a person. The 3D body envelope was obtained by deforming a template to match the surface profiles in two X-rays images in three successive steps: global morphing to adopt the position of a person and scale the template׳s body segments, followed by a gross deformation and a fine deformation using two sets of pre-defined control points. To evaluate the method, a biplanar X-ray acquisition was obtained from head to foot for 12 volunteers in a standing posture. Up to 172 radio-opaque skin markers were attached to the body surface and used as reference positions. Each envelope was reconstructed three times by three operators. Results showed a bias lower than 7mm and a confidence interval (95%) of reproducibility lower than 6mm for all body parts, comparable to other existing methods matching a template onto stereographic photographs. The proposed method offers the possibility of reconstructing body shape in addition to the skeleton using a low dose biplanar X-rays system.

The Gould modification of Broström ligament repair may be a favorable operative procedure for the restoration of subtalar and ankle joint kinematics.

A combination of wearable sensors’ data and Machine Learning (ML) techniques has been used in many studies to predict specific joint angles and moments. The aim of this study was to compare the performance of four different non-linear regression ML models to estimate lower-limb joints’ kinematics, kinetics, and muscle forces using Inertial Measurement Units (IMUs) and electromyographys’ (EMGs) data. Seventeen healthy volunteers (9F, 28 ± 5 years) were asked to walk over-ground for a minimum of 16 trials. For each trial, marker trajectories and three force-plates data were recorded to calculate pelvis, hip, knee, and ankle kinematics and kinetics, and muscle forces (the targets), as well as 7 IMUs and 16 EMGs. The features from sensors’ data were extracted using the Tsfresh python package and fed into 4 ML models; Convolutional Neural Networks (CNN), Random Forest (RF), Support Vector Machine, and Multivariate Adaptive Regression Spline for targets’ prediction. The RF and CNN models outperformed the other ML models by providing lower prediction errors in all intended targets with a lower computational cost. This study suggested that a combination of wearable sensors’ data with an RF or a CNN model is a promising tool to overcome the limitations of traditional optical motion capture for 3D gait analysis.

Subtalar joint instability is hypothesized to occur after injuries to the calcaneofibular ligament (CFL) in isolation or in combination with the cervical and the talocalcaneal interosseous ligaments. A common treatment for hindfoot instability is the application of an ankle brace. However, the ability of an ankle brace to promote subtalar joint stability is not well established. We assessed the kinematics of the subtalar joint, ankle, and hindfoot in the presence of isolated subtalar instability, investigated the effect of bracing in a CFL deficient foot and with a total rupture of the intrinsic ligaments, and evaluated how maximum inversion range of motion is affected by the position of the ankle in the sagittal plane. Kinematics from nine cadaveric feet were collected with the foot placed in neutral, dorsiflexion, and plantar flexion. Motion was applied with and without a brace on an intact foot and after sequentially sectioning the CFL and the intrinsic ligaments. Isolated CFL sectioning increased ankle joint inversion, while sectioning the CFL and intrinsic ligaments affected subtalar joint stability. The brace limited inversion at the subtalar and ankle joints. Additionally, examining the foot in dorsiflexion reduced ankle and subtalar joint motion. ß

Body segment parameters (BSP) for each body׳s segment are needed for biomechanical analysis. To provide population-specific BSP, precise estimation of body׳s segments volume and density are needed. Widely used uniform densities, provided by cadavers׳ studies, did not consider the air present in the lungs when determining the thorax density. The purpose of this study was to propose a new uniform thorax density representative of the living population from 3D external body shape modeling. Bi-planar X-ray radiographies were acquired on 58 participants allowing 3D reconstructions of the spine, rib cage and human body shape. Three methods of computing the thorax mass were compared for 48 subjects: (1) the Dempster Uniform Density Method, currently in use for BSPs calculation, using Dempster density data, (2) the Personalized Method using full-description of the thorax based on 3D reconstruction of the rib cage and spine and (3) the Improved Uniform Density Method using a uniform thorax density resulting from the Personalized Method. For 10 participants, comparison was made between the body mass obtained from a force-plate and the body mass computed with each of the three methods. The Dempster Uniform Density Method presented a mean error of 4.8% in the total body mass compared to the force-plate vs 0.2% for the Personalized Method and 0.4% for the Improved Uniform Density Method. The adjusted thorax density found from the 3D reconstruction was 0.74g/cm(3) for men and 0.73g/cm(3) for women instead of the one provided by Dempster (0.92g/cm(3)), leading to a better estimate of the thorax mass and body mass.

Finite element models (FEM) derived from qCT-scans were developed as a clinical tool to evaluate vertebral strength. However, the high dose, time and cost of qCT-scanner are limitations for routine osteoporotic diagnosis. A new approach considers using bi-planar dual energy (BP2E) X-rays absorptiometry to build vertebral FEM using synchronized sagittal and frontal plane radiographs. The purpose of this study was to compare the performance of the areal bone mineral density (aBMD) measured from DXA, qCT-based FEM and BP2E-based FEM in predicting experimental vertebral strength. Twenty eight vertebrae from eleven lumbar spine segments were imaged with qCT, DXA and BP2E X-rays before destructively tested in anterior compression. FEM were built based on qCT and BP2E images for each vertebra. Subject-specific FEM were built based on 1) the BP2E images using 3D reconstruction and volumetric BMD distribution estimation and 2) the qCT scans using slice by slice segmentation and voxel based calibration. Linear regression analysis was performed to find the best predictor for experimental vertebral strength (F); aBMD, modeled vertebral strength and vertebral stiffness. Areal BMD was moderately correlated with F (R = 0.74). FEM calculations of vertebral strength were highly to strongly correlated with F (R = 0.84, p < 0.001 for BP2E model and R = 0.95, p < 0.001 for qCT model). The results of this study suggest that aBMD accounted for only 74% of F variability while FE models accounted for at least 84%. For anterior compressive loading on isolated vertebral bodies, simplistic loading condition aimed to replicate anterior wedge fractures, both FEM were good predictors of F. Therefore FEM based on BP2E X-rays absorptiometry could be a good alternative to replace qCT-based models in the prediction of vertebral strength. However future work should investigate the performance of the BP2E-based model in vivo in discriminating patients with and without vertebral fracture in a prospective study.

Available methods for generating paediatric musculoskeletal geometry are to scale generic adult geometry, which is widely accessible but can be inaccurate, or to obtain geometry from medical imaging, which is accurate but time-consuming and costly. A population-based shape model is required to generate accurate and accessible musculoskeletal geometry in a paediatric population. The pelvis, femur, and tibia/fibula were segmented from 333 CT scans of children aged 4–18 years. Bone morphology variation was captured using principal component analysis (PCA). Subsequently, a shape model was developed to predict bone geometry from demographic and linear bone measurements and validated using a leave one out analysis. The shape model was compared to linear scaling of adult and paediatric bone geometry. The PCA captured growth-related changes in bone geometry. The shape model predicted bone geometry with root mean squared error (RMSE) of 2.91 ± 0.99 mm in the pelvis, 2.01 ± 0.62 mm in the femur, and 1.85 ± 0.54 mm in the tibia/fibula. Linear scaling of an adult mesh produced RMSE of 4.79 ± 1.39 mm in the pelvis, 4.38 ± 0.72 mm in the femur, and 4.39 ± 0.86 mm in the tibia/fibula. We have developed a method for capturing and predicting lower limb bone shape variation in a paediatric population more accurately than linear scaling without using medical imaging.