Nicolas Bochud

Robust parametrization for non-destructive evaluation of composites using ultrasonic signals

Anticipating and characterizing damages in layered carbon fiber-reinforced polymers is a challenging problem. Non-destructive evaluation using ultrasonic signals is a well-established method to obtain physically relevant parameters to characterize damages in isotropic homogeneous materials. However, ultrasonic signals obtained from composites require special care in signal interpretation due to their structural complexity. In this paper, some enhancements on the interpretation are done by adapting classical parametrization techniques to extract relevant features from the ultrasonic signals. Thus, a cepstral-based feature extractor is firstly designed and optimized by using a classification system based on cepstral distances. Then, this feature extractor is applied in an analysis-by-synthesis scheme which, by using a numerical model of the specimen, infers the values of the damage parameters.

Predicting bone strength with ultrasonic guided waves

Recent bone quantitative ultrasound approaches exploit the multimode waveguide response of long bones for assessing properties such as cortical thickness and stiffness. Clinical applications remain, however, challenging, as the impact of soft tissue on guided waves characteristics is not fully understood yet. In particular, it must be clarified whether soft tissue must be incorporated in waveguide models needed to infer reliable cortical bone properties. We hypothesize that an inverse procedure using a free plate model can be applied to retrieve the thickness and stiffness of cortical bone from experimental data. This approach is first validated on a series of laboratory-controlled measurements performed on assemblies of bone- and soft tissue mimicking phantoms and then on in vivo measurements. The accuracy of the estimates is evaluated by comparison with reference values. To further support our hypothesis, these estimates are subsequently inserted into a bilayer model to test its accuracy. Our results show that the free plate model allows retrieving reliable waveguide properties, despite the presence of soft tissue. They also suggest that the more sophisticated bilayer model, although it is more precise to predict experimental data in the forward problem, could turn out to be hardly manageable for solving the inverse problem.

Mechanical assessment of cervical remodelling in pregnancy: insight from a synthetic model.

During the gestation and the cervical remodelling, several changes occur progressively in the structure of the tissue. An increase in the hydration, disorganisation of collagen network and decrease in elasticity can be observed. The collagen structure disorganisation is particularly complex: collagen fibres turn thicker and more wavy as the gestation progresses in a transition from relatively straight fibres to wavy fibres, while pores between collagen fibres become larger and separated. Shear wave elastography is a promising but not yet fully understood tool to assess these structural changes and the cervix׳s ability to dilate. To this end, a numerical histo-mechanical model is proposed in the present study, which aims at linking variations in the microscopic histo-biomechanical processes with shear wave propagation characteristics. Parametric simulations are carried out for a broad range of mechanical and geometrical parameters. Results show a direct relationship between the histological and morphological changes during pregnancy and the viscoelastic behaviour of the tissue.

Probabilistic inverse problem to characterize tissue-equivalent material mechanical properties

The understanding of internal processes that affect the changes of consistency of soft tissue is a challenging problem. An ultrasound-monitoring Petri dish has been designed to monitor the evolution of relevant mechanical parameters during engineered tissue formation processes in real time. A better understanding of the measured ultrasonic signals required the use of numerical models of the ultrasound-tissue interactions. The extraction of relevant data and its evolution with sufficient sensitivity and accuracy is addressed by applying well-known signal processing techniques to both the experimental and numerically predicted measurements. In addition, a stochastic model-class selection formulation is used to rank which of the proposed interaction models are more plausible. The sensitivity of the system is verified by monitoring a gelation process.

Assessing viscoelasticity of shear wave propagation in cervical tissue by multiscale computational simulation.

The viscoelastic properties are recently being reported to be particularly sensitive to the gestation process, and to be intimately related to the microstructure of the cervical tissue. However, this link is not fully understood yet. In this work, we explore the importance of the heterogeneous multi-scale nature of cervical tissue for quantifying both elasticity and viscosity from shear waves velocity. To this end, shear wave propagations are simulated in a microscopic cervical tissue model using the finite difference time domain technique, over a wide frequency range from 15 to 200 kHz. Three standard rheological models (Voigt, Maxwell and Zener) are evaluated regarding their ability to reproduce the simulated dispersion curves, and their plausibility to describe cervical tissue is ranked by a stochastic model-class selection formulation. It is shown that the simplest model, i.e. that with less parameters, which best describes the simulated dispersion curves in cervical tissue is the Maxwell model. Furthermore, results show that the excitation frequency determines which rheological model can be representative for the tissue. Typically, viscoelastic parameters tend to converge for excitation frequencies over 100 kHz.

Model-based cepstral analysis for ultrasonic non-destructive evaluation of composites

The use of model-based cepstral features has been shown as an effective characterization of damaged materials tested with ultrasonic non-destructive evaluation (NDE) techniques. In this work, we focus our study on carbon-fiber reinforced polymer plates and show that the use of signal models with physical meaning can provide a cepstral representation with a high discriminative power. First, we introduce a complete digital signal model based on a physical analysis of wave propagation inside the plate. The resulting model has several drawbacks: a high number of parameters to estimate and the difficulty of expressing it as a classical rational transfer function, which does not allow a model parameter estimation through classical least-squares signal modeling techniques. In order to overcome these problems, we propose two simplifications of the physical model also based on a mechanical analysis of the system. We carry out a set of damage recognition experiments showing that cepstra extracted from these models are more discriminative than other previously used methods such as the LPC cepstrum (all-pole model) or a simple FFT cepstrum.

A sparse digital signal model for ultrasonic nondestructive evaluation of layered materials

Signal modeling has been proven to be an useful tool to characterize damaged materials under ultrasonic nondestructive evaluation (NDE). In this paper, we introduce a novel digital signal model for ultrasonic NDE of multilayered materials. This model borrows concepts from lattice filter theory, and bridges them to the physics involved in the wave-material interactions. In particular, the proposed theoretical framework shows that any multilayered material can be characterized by a transfer function with sparse coefficients. The filter coefficients are linked to the physical properties of the material and are analytically obtained from them, whereas a sparse distribution naturally arises and does not rely on heuristic approaches. The developed model is first validated with experimental measurements obtained from multilayered media consisting of homogeneous solids. Then, the sparse structure of the obtained digital filter is exploited through a model-based inverse problem for damage identification in a carbon fiber-reinforced polymer (CFRP) plate.

Sparse signal model for ultrasonic nondestructive evaluation of CFRP composite plates

Signal processing has been proven to be an useful tool to characterize damaged materials under ultrasonic nondestructive evaluation. In this work, we hypothesize that the transfer function of multilayered materials for a through-transmission configuration can be represented as a classical all-pole model with sparse coefficients. To test this hypothesis, we propose an analysis-by-synthesis scheme which, by assuming an underlying sparse digital signal model of the specimen, infers the order and extent of the model parameters corresponding to a certain impact damage level. Then, we exploit the sparse structure of the obtained digital filter for practical NDE applications, with emphasis on impact damage identification of carbon-fiber reinforced polymer plates.

Dispersive model selection and reconstruction for tissue culture ultrasonic monitoring

The evolution of relevant mechanical parameters during tissue engineering formation processes can provide useful information for their control in real-time, as well as a new dimension in the understanding of internal processes and their final quality. Since ultrasonics are mechanical waves, they are ideally well suited for probing certain mechanical properties. An ultrasound monitoring Petri dish has been designed for this purpose. The readings from the ultrasonic sensors need a detailed analysis, based on numerical models of the ultrasound-tissue interaction, and a stochastic treatment, in order to extract the relevant information and its evolution with sufficient sensitivity. In addition, a stochastic model-class selection formulation is used to rank which one of the proposed interaction models are more plausible. To verify the sensitivity of the system, a gelation process is monitored.

A STOCHASTIC MODEL FOR TISSUE CONSISTENCE EVOLUTION BASED ON THE INVERSE PROBLEM

An inverse-stochastic framework is proposed to reproduce the pattern evolution and predict the mechanical properties of a tissue-engineered culture from ultrasonic measurements in an in-vitro culture. A Markovian type of evolution is expected in tissue cultures for mechanical properties such us bulk modulus (K) or attenuation coefficient (AC), under the hypothesis that the future of the process depends only upon its present state, and not upon past states. Additionally a spread in the evolution histories for different repetitions of the same process is expected, consequently stochastic models such us Markov chains [Gallager, 1996] seems to be more suitable. The method proposed is predictive in nature and can be applicable to any measurable biomechanical process, under the assumption that the process shows Markovian evolution.