Identification of hyperelastic properties of passive thigh muscle under compression with an inverse method from a displacement field measurement.

The mechanical behavior of muscle tissue is an important field of investigation with different applications in medicine, car crash and sport, for example. Currently, few in vivo imaging techniques are able to characterize the mechanical properties of muscle. Thus, this study presents an in vivo method to identify a hyperelatic behavior from a displacement field measured with ultrasound and Digital Image Correlation (DIC) techniques. This identification approach was composed of 3 inter-dependent steps. The first step was to perform a 2D MRI acquisition of the thigh in order to obtain a manual segmentation of muscles (quadriceps, ischio, gracilis and sartorius) and fat tissue, and then develop a Finite Element model. In addition, a Neo-Hookean model was chosen to characterize the hyperelastic behavior (C10, D) in order to simulate a displacement field. Secondly, an experimental compression device was developed in order to measure the in vivo displacement fields in several areas of the thigh. Finally, an inverse method was performed to identify the C10 and D parameters of each soft tissue. The identification procedure was validated with a comparison with the literature. The relevance of this study was to identify the mechanical properties of each investigated soft tissues.

Use of digital image correlation and ultrasound: analysis of thigh muscle displacement fields.

The understanding of the mechanical behavior of the muscle tissue is an important field of investigation with different applications in medicine, car crash and sport. Currently, few in vivo imaging techniques are able to characterize the mechanical properties of muscle. Thus, the development of an in vivo identification method is a current thematic where the displacement field measurements could be used for further interpretations. This study aims at presenting the displacement fields measured in the anterior, posterior, lateral and medial parts of the thigh muscles using ultrasound and Digital Image Correlation (DIC) techniques. The results of the displacement field measurements confirmed and are correlated with the ultrasound observations.

Development of an inverse approach for the characterization of in vivo mechanical properties of the lower limb muscles.

The purpose of this study was to develop an inverse method, coupling imaging techniques with numerical methods, to identify the muscle mechanical behavior. A finite element model updating (FEMU) was developed in three main interdependent steps. First, a 2D FE modeling, parameterized by a Neo-Hookean behavior (C10 and D), was developed from a segmented thigh muscle 1.5T MRI (magnetic resonance imaging). Thus, a displacement field was simulated for different static loadings (contention, compression, and indentation). Subsequently, the optimal mechanical test was determined from a sensitivity analysis. Second, ultrasound parameters (gain, dynamic, and frequency) were optimized on the thigh muscles in order to apply the digital image correlation (DIC), allowing the measurement of an experimental displacement field. Third, an inverse method was developed to identify the Neo-Hookean parameters (C10 and D) by performing a minimization of the distance between the simulated and measured displacement fields. To replace the experimental data and to quantify the identification error, a numerical example was developed. The result of the sensitivity analysis showed that the compression test was more adapted to identify the Neo-Hookean parameters. Ultrasound images were recorded with a frequency, gain, and dynamic of 9 MHz, 34 dB, 42 dB, respectively. In addition, the experimental noise on displacement field measurement was estimated to be 0.2 mm. The identification performed on the numerical example revealed a low error for the C10 (<3%) and D (<7%) parameters with the experimental noise. This methodology could have an impact in the scientific and medical fields. A better knowledge of the muscle behavior will help to follow treatment and to ensure accurate medical procedures during the use of robotic devices.