Philippe Pouletaut

Estimation of accuracy of patient-specific musculoskeletal modelling: case study on a post polio residual paralysis subject

For patients with patterns ranging out of anthropometric standard values, patient-specific musculoskeletal modelling becomes crucial for clinical diagnosis and follow-up. However, patient-specific modelling using imaging techniques and motion capture systems is mainly subject to experimental errors. The aim of this study was to quantify these experimental errors when performing a patient-specific musculoskeletal model. CT scan data were used to personalise the geometrical model and its inertial properties for a post polio residual paralysis subject. After having performed a gait-based experimental protocol, kinematics data were measured using a VICON motion capture system with six infrared cameras. The musculoskeletal model was computed using a direct/inverse algorithm (LifeMod software). A first source of errors was identified in the segmentation procedure in relation to the calculation of personalised inertial parameters. The second source of errors was subject related, as it depended on the reproducibility of performing the same type of gait. The impact of kinematics, kinetics and muscle forces resulting from the musculoskeletal modelling was quantified using relative errors and the absolute root mean square error. Concerning the segmentation procedure, we found that the kinematics results were not sensitive to the errors (relative error < 1%). However, a strong influence was noted on the kinetics results (deviation up to 71%). Furthermore, the reproducibility error showed a significant influence (relative mean error varying from 5 to 30%). The present paper demonstrates that in patient-specific musculoskeletal modelling variations due to experimental errors derived from imaging techniques and motion capture need to be both identified and quantified. Therefore, the paper can be used as a guideline.

Viscoelastic shear properties of in vivo thigh muscles measured by MR elastography

To measure the viscoelastic properties of passive thigh muscles using multifrequency magnetic resonance elastography (MMRE) and rheological models.

Quantitative analysis of annulus fibrosus and nucleus pulposus derived from T2 mapping, diffusion-weighted and diffusion tensor MR imaging

Quantitative information on annulus fibrosus (AF) and nucleus pulposus (NP) is of interest in early diagnosis of degenerated intervertebral disc (IVD) disease. In this present study, we developed a practical framework to analyse and visualise three feature maps of the IVD: the T2 relaxation time, the apparent diffusion coefficient and the anisotropy. They are derived from advanced Magnetic Resonance Imaging sequences such as T2 mapping, diffusion-weighted and diffusion tensor sequences. These features were quantified using regression analysis with least squares and signal intensity-based direction-oriented methods. Based on these maps, a threshold-based geometrical computing procedure was developed to compute the geometrical repartition maps. As a first case study, in vivo features of AF and NP regions of the L4/5 IVD were analysed and visualised. Practical relevance of our developed framework for a clinical purpose is discussed. In fact, our framework allows useful information of IVD to be quantified and...

Impact of TIEG1 Deletion on the Passive Mechanical Properties of Fast and Slow Twitch Skeletal Muscles in Female Mice

As transforming growth factor (TGF)-β inducible early gene-1 is highly expressed in skeletal muscle, the effect of TIEG1 gene deletion on the passive mechanical properties of slow and fast twitch muscle fibers was analyzed. Twenty five muscle fibers were harvested from soleus (Sol) and extensor digitorum longus (EDL) muscles from TIEG1-/- (N = 5) and control (N = 5) mice. Mechanical tests were performed on fibers and the dynamic and static stresses were measured. A viscoelastic Hill model of 3rd order was used to fit the experimental relaxation test data. In parallel, immunohistochemical analyses were performed on three serial transverse sections to detect the myosin isoforms within the slow and fast muscles. The percentage and the mean cross sectional area of each fiber type were calculated. These tests revealed a significant increase in the mechanical stress properties for the TIEG1-/- Sol fibers while a significant decrease appeared for the TIEG1-/- EDL fibers. Hill model tracked the shape of the experimental relaxation curve for both genotypes and both fiber types. Immunohistochemical results showed hypertrophy of all fiber types for TIEG1-/- muscles with an increase in the percentage of glycolytic fibers (IIX, and IIB) and a decrease of oxidative fibers (I, and IIA). This study has provided new insights into the role of TIEG1, known as KLF10, in the functional (SoltypeI: more resistant, EDLtypeIIB: less resistant) and morphological (glycolytic hypertrophy) properties of fast and slow twitch skeletal muscles. Further investigation at the cellular level will better reveal the role of the TIEG1 gene in skeletal muscle tissue.

A Hertzian Integrated Contact Model of the Total Knee Replacement Implant for the Estimation of Joint Contact Forces

The prediction of lower limb muscle and contact forces may provide useful knowledge to assist the clinicians in the diagnosis as well as in the development of appropriate treatment for musculoskeletal disorders. Research studies have commonly estimated joint contact forces using model-based muscle force estimation due to the lack of a reliable contact model and material properties. The objective of this present study was to develop a Hertzian integrated contact model. Then, in vivo elastic properties of the Total Knee Replacement (TKR) implant were identified using in vivo contact forces leading to providing reliable material properties for modeling purposes. First, a patient specific rigid musculoskeletal model was built. Second, a STL-based implant model was designed to compute the contact area evolutions during gait motions. Finally, a Hertzian integrated contact model was defined for the in vivo identification of elastic properties (Young’s modulus and Poisson coefficient) of the instrumented TKR implant. Our study showed a potential use of a new approach to predict the contact forces without knowledge of muscle forces. Thus, the outcomes may lead to accurate and reliable prediction of human joint contact forces for new case study.

MRI-based finite element modeling of facial mimics: a case study on the paired zygomaticus major muscles

Abstract Finite element simulation of facial mimics provides objective indicators about soft tissue functions for improving diagnosis, treatment and follow-up of facial disorders. There is a lack of in vivo experimental data for model development and validation. In this study, the contribution of the paired Zygomaticus Major (ZM) muscle contraction on the facial mimics was investigated using in vivo experimental data derived from MRI. Maximal relative differences of 7.7% and 37% were noted between MRI-based measurements and numerical outcomes for ZM and skin deformation behaviors respectively. This study opens a new direction to simulate facial mimics with in vivo data.

IMAGE-BASED SKELETAL MUSCLE COORDINATION: CASE STUDY ON A SUBJECT SPECIFIC FACIAL MIMIC SIMULATION

Facial muscle coordination is a fundamental mechanism for facial mimics and expressions. The understanding of this complex mechanism leads to better diagnosis and treatment of facial disorders like facial palsy or disfigurement. The objective of this work was to use magnetic resonance imaging (MRI) technique to characterize the activation behavior of facial muscles and then simulate their coordination mechanism using a subject specific finite element model. MRI data of lower head of a healthy subject were acquired in neutral and in the pronunciation of the sound [o] positions. Then, a finite element model was derived directly from acquired MRI images in neutral position. Transversely-isotropic, hyperelastic, quasi-incompressible behavior law was implemented for modeling facial muscles. The simulation to produce the pronunciation of the sound [o] was performed by the cumulative coordination between three pairs of facial mimic muscles (Zygomaticus Major (ZM), Levator Labii Superioris (LLS), Levator Anguli O...