Interactive Simulation of Diaphragm Motion Through Muscle and Rib Kinematics

ISBN-10: 1848825641/ ISBN-13: 978-1848825642 / The original publication is available at www.springerlink.comModelling of diaphragm behaviour is of relevance to a number of clini cal procedures such as lung cancer radiotherapy and liver access interventions. The heterogeneity in tissue composition of the diaphragm, as well as the various physiological phenomena influencing its behaviour, requires a complex model in order to accurately capture its motion. In this paper we present a novel methodology based on a heterogeneous model composed of mass-spring and tensegrity elements. The physiological boundary conditions have been carefully taken into account and applied to our model. Thus, it incorporates the influence of the rib kinematics, the muscle natural contraction/relaxation and the motion of the sternum. Initial validation results show that the behaviour of the model closely follows that of a real diaphragm

Virtual Reality Simulation of Liver Biopsy with a Respiratory Component

International audienceThe field of computer-based simulators has grown exponentially in the last few decades, especially in Medicine. Advantages of medical simulators include: (1) provision of a platform where trainees can practice procedures without risk of harm to patients; (2) anatomical fidelity; (3) the ability to train in an environment wherein physiological behaviour is observed, something that is not permitted where in-vitro phantoms are used; (4) flexibility regarding anatomical and pathological variation of test cases that is valuable in the acquisition of experience; (5) quantification of metrics relating to task performance that can be used to monitor trainee performance throughout the learning curve; and (6) cost effectiveness. In this chapter, we will focus on the current state of the art of medical simulators, the relevant parameters required to design a medical simulator, the basic framework of the simulator, methods to produce a computer-based model of patient respiration and finally a description of a simulator for ultrasound guided for liver biopsy. The model that is discussed presents a framework that accurately simulates respiratory motion, allowing for the fine tuning of relevant parameters in order to produce a patient-specific breathing pattern that can then be incorporated into a simulation with real-rime haptic interaction. Thus work was conducted as part CRaIVE collaboration [1], whose aim is to develop simulators specific to interventional radiology

Models of Mechanics and Growth in Developmental Biology: A Computational Morphodinamics approach

Recent evidence has revealed the role of mechanical cues in the development of shapes in organisms. This thesis is an effort to test some of the fundamental hypotheses about the relation between mechanics and patterning in plants. To do this, we develop mechanical models designed to include specific features of plant cell walls. These are heterogeneous stiffness and material anisotropy as well as rates and directions of growth, which we then relate to different domains of the plant tissue.In plant cell walls, anisotropic fiber deposition is the main controller of longitudinal growth. In our model, this is achieved spontaneously, by applying feedback from the maximal stress direction to the fiber orientation. We show that a stress feedback model is in fact an energy minimization process. This can be considered as an evolutionary motivation for the emergence of a stress feedback mechanism. Then we add continuous growth and cell division to the model and employ the strain signal directing large growth deformations. We show the advantages of strain-based growth model for emergence of plant-like organ shapes as well as for reproducing microtubular dynamics in hypocotyls and roots. We also investigate possibilities for describing microtubular patterns, at root hair outgrowth sites according to stress patterns. Altogether, the work described in this thesis, provides a new improved growth model for plant tissue, where mechanical properties are handled with appropriate care in the event of growth driven by either molecular or mechanical signals. The model unifies the patterning process for several different plant tissues, from shoot to single root hair cells, where it correctly predict microtubular dynamics and growth patterns. In a long-term perspective, this understanding can propagate to novel technologies for improvement of yield in agriculture and the forest industry

Variational methods for modeling and simulation of tool-tissue interaction

Ph.DDOCTOR OF PHILOSOPH

A continuous growth model for plant tissue

Morphogenesis in plants and animals involves large irreversible deformations. In plants, the response of the cell wall material to internal and external forces is determined by its mechanical properties. An appropriate model for plant tissue growth must include key features such as anisotropic and heterogeneous elasticity and cell dependent evaluation of mechanical variables such as turgor pressure, stress and strain. In addition, a growth model needs to cope with cell divisions as a necessary part of the growth process. Here we develop such a growth model, which is capable of employing not only mechanical signals but also morphogen signals for regulating growth. The model is based on a continuous equation for updating the resting configuration of the tissue. Simultaneously, material properties can be updated at a different time scale. We test the stability of our model by measuring convergence of growth results for a tissue under the same mechanical and material conditions but with different spatial discretization. The model is able to maintain a strain field in the tissue during re-meshing, which is of particular importance for modeling cell division. We confirm the accuracy of our estimations in two and three-dimensional simulations, and show that residual stresses are less prominent if strain or stress is included as input signal to growth. The approach results in a model implementation that can be used to compare different growth hypotheses, while keeping residual stresses and other mechanical variables updated and available for feeding back to the growth and material properties.This work was supported by the Swedish Research Council (VR2013:4632), the Gatsby Charitable Foundation (GAT3395/PR4), and the Knut and Alice Wallenberg Foundation via project ShapeSystems (KAW2012.0050)

Modeling growth in biological materials

The biomechanical modeling of growing tissues has recently become an area of intense interest. In particular, the interplay between growth patterns and mechanical stress is of great importance, with possible applications to arterial mechanics, embryo morphogenesis, tumor development, and bone remodeling. This review aims to give an overview of the theories that have been used to model these phenomena, categorized according to whether the tissue is considered as a continuum object or a collection of cells. Among the continuum models discussed is the deformation gradient decomposition method, which allows a residual stress field to develop from an incompatible growth field. The cell-based models are further subdivided into cellular automata, center-dynamics, and vertex-dynamics models. Of these the second two are considered in more detail, especially with regard to their treatment of cell-cell interactions and cell division. The review concludes by assessing the prospects for reconciliation between these two fundamentally different approaches to tissue growth, and by identifying possible avenues for further research. © 2012 Society for Industrial and Applied Mathematics

Simultaneous real-time viscoelasticity, mass and cell cycle monitoring for single adherent cancer cells

Cancer is a complex disease caused by the combined effects of genetic and environmental factors. Evidently, there exists a correlation between the surrounding environment of a cell, its biophysical properties and health. Information gained from biomechanics has led to an improved understanding of the way diseases evolve and their progression cycle, providing methods targeted towards curing these diseases. Countless studies have been carried out on the mechanisms underlying cell cycle progression. More particularly, these studies on the mechanics of individual cells have pointed to their coordination, which helps us understand cellular metabolic and physiological process better. Development of more precise, versatile and reliable measurement tools and techniques will provide a greater understanding of cellular behavior and biophysical properties. Micromechanical systems (MEMS) technology can provide these tools – for analyzing single cells and providing important and useful information of their biophysical properties. In modern research, the ability to reliably investigate and understand these cellular properties requires measurement devices that provide high sensitivity, high throughput, and adaptability to include multiple on-chip functionalities. Many MEMS-based resonant sensors have been extensively studied and used as biological and chemical sensors. However, previous works have shown that there are several technology limitations that inhibit application of various MEMS-sensors to mechanical measurement and analysis, including insufficient cell capture efficiency, media perfusion for long term growth, cell adhesion and cell movement/spreading and cell-sensor modelling. Cellular mechanics and viscoelastic properties are known to play a role in biological processes such as cell growth, stem cell differentiation, cell crawling, wound healing, protein regulation, cell malignancy and even apoptosis (programmed cell death). Thus, an accurate measurement of stiffness and growth is fundamental to understanding cellular proliferation in cancer. Capturing these biophysical properties of cancer cells over the duration of their growth cycle through MEMS devices can help provide a better insight into the mechanics of the metastasis of cancer cells. Meanwhile, many MEMS sensing devices still require further development and characterization to reliably investigate long-term cell behaviors. This dissertation focuses on characterization of our MEMS resonant sensors to address current challenges in the measurement of long-term biophysical behaviors of cells across its cell cycle. The amplitude and frequency of MEMS resonant pedestal sensors were used in conjunction with a vibration induced and optically-sensed phase shift of target light incident on an adhering sample to extract the loss tangent - a measure of the relative viscoelasticity of soft materials. This observed phase shift, combined with a representative two-degree-of-freedom Kelvin-Voigt model, is used to simultaneously obtain the elasticity (stiffness), viscosity and mass associated with individual adherent cancer cells. The research is unique as it decouples the heterogeneity of individual cells in our population and further refines our viscoelastic solution space. This novel development enables long-term simultaneous measurement of changes in stiffness and mass of normal and cancerous cells over time. This is the first investigation of the time-varying simultaneous measurement of viscoelasticity and mass for individual adherent cells using our MEMS resonant sensors

Computational models of melanoma.

Genes, proteins, or cells influence each other and consequently create patterns, which can be increasingly better observed by experimental biology and medicine. Thereby, descriptive methods of statistics and bioinformatics sharpen and structure our perception. However, additionally considering the interconnectivity between biological elements promises a deeper and more coherent understanding of melanoma. For instance, integrative network-based tools and well-grounded inductive in silico research reveal disease mechanisms, stratify patients, and support treatment individualization. This review gives an overview of different modeling techniques beyond statistics, shows how different strategies align with the respective medical biology, and identifies possible areas of new computational melanoma research