Research on real-time physics-based deformation for haptic-enabled medical simulation

This study developed a multiple effective visuo-haptic surgical engine to handle a variety of surgical manipulations in real-time. Soft tissue models are based on biomechanical experiment and continuum mechanics for greater accuracy. Such models will increase the realism of future training systems and the VR/AR/MR implementations for the operating room

Conception et évaluation d’un simulateur à réalité virtuelle d’intervention laparoscopique actionné par des embrayages magnétorhéologiques

La laparoscopie est une technique chirurgicale qui offre une alternative moins invasive à la chirurgie abdominale traditionnelle, en permettant aux patients de récupérer plus rapidement et avec moins de douleur. Dès son arrivée, cette nouvelle technique a su révolutionner le monde de la chirurgie, mais cette révolution est d'ailleurs venue avec un cout, une formation longue et difficile. Des simulateurs haptiques ont tenté de rendre cet apprentissage plus facile, mais leur cout élevé et leurs grosses dimensions les rendent difficiles d'accès pour les étudiants moyens. Afin de résoudre ce problème, des concepts qui utilisent des dispositifs haptiques sont offerts sur le marché pour concevoir des plateformes de simulation d'interventions laparoscopiques. Ces plateformes sont toutefois peu fidèles à la réalité et n'atteignent pas simultanément les performances dynamiques et cinétiques nécessaires à un apprentissage adéquat. En effet, les moteurs électriques utilisés obligent les concepteurs de dispositifs haptiques à faire un compromis entre la force produite et la réponse dynamique du système. Cette approche pourrait par contre être utilisée avec un dispositif haptique nouvelle-génération, le T-Rex. Ce dernier a été développé récemment par Exonetik, une compagnie issue de recherches de l'Université de Sherbrooke. Contrairement aux dispositifs haptiques offerts sur le marché, le T-Rex utilise la technologie d'actionneurs magnéto-rhéologiques développée par Exonetik. Cette technologie pourrait permettre d'atteindre les performances dynamiques et cinétiques nécessaires à la formation de chirurgiens. Ce projet de recherche présente l'analyse préliminaire du T-Rex d'Exonetik en tant que simulateur à réalité virtuelle d'interventions laparoscopiques. Un simulateur à réalité virtuelle d'interventions laparoscopiques utilisant le T-Rex d'Exonetik en tant qu'interface haptique a été conçu. Des critères de performances ont été établis à l'aide de la littérature pour faire une évaluation quantitative du système. Des simulations utilisant la méthode des éléments finis ont aussi été développées pour faire une évaluation qualitative du système auprès de résidents et de chirurgiens. L'évaluation quantitative du système démontre qu'il répond aux quatre critères cinématiques ainsi qu'à trois des quatre critères cinétiques. Les résultats démontrent donc que l'utilisation d'actionneurs magnéto-rhéologiques dans les simulateurs à réalité virtuelle d'interventions laparoscopiques a beaucoup de potentiel. Par contre, la friction dans le système se doit d'être adressée dans les itérations futures du système

GPU-based Real-Time Soft Tissue Deformation with Cutting and Haptic Feedback

Special Issue on Biomechanical Modelling of Soft Tissue MotionInternational audienceThis article describes a series of contributions in the field of real-time simulation of soft tissue biomechanics. These contributions address various requirements for interactive simulation of complex surgical procedures. In particular, this article presents results in the areas of soft tissue deformation, contact modelling, simulation of cutting, and haptic rendering, which are all relevant to a variety of medical interventions. The contributions described in this article share a common underlying model of deformation and rely on GPU implementations to significantly improve computation times. This consistency in the modelling technique and computational approach ensures coherent results as well as efficient, robust and flexible solutions

Nonlinear effects in finite elements analysis of colorectal surgical clamping

Minimal Invasive Surgery (MIS) is a procedure that has increased its applications in past few years in different types of surgeries. As number of application fields are increasing day by day, new issues have been arising. In particular, instruments must be inserted through a trocar to access the abdominal cavity without capability of direct manipulation of tissues, so a loss of sensitivity occurs. Generally speaking, the student of medicine or junior surgeons need a lot of practice hours before starting any surgical procedure, since they have to difficulty in acquiring specific skills (hand–eye coordination among others) for this type of surgery. Here is what the surgical simulator present a promising training method using an approach based on Finite Element Method (FEM). The use of continuum mechanics, especially Finite Element Analysis (FEA) has gained an extensive application in medical field in order to simulate soft tissues. In particular, colorectal simulations can be used to understand the interaction between colon and the surrounding tissues and also between colon and instruments. Although several works have been introduced considering small displacements, FEA applied to colorectal surgical procedures with large displacements is a topic that asks for more investigations. This work aims to investigate how FEA can describe non-linear effects induced by material properties and different approximating geometries, focusing as test-case application colorectal surgery. More in detail, it shows a comparison between simulations that are performed using both linear and hyperelastic models. These different mechanical behaviours are applied on different geometrical models (planar, cylindrical, 3D-SS and a real model from digital acquisitions 3D-S) with the aim of evaluating the effects of geometric non-linearity. Final aim of the research is to provide a preliminary contribution to the simulation of the interaction between surgical instrument and colon tissues with multi-purpose FEA in order to help the preliminary set-up of different bioengineering tasks like force-contact evaluation or approximated modelling for virtual reality (surgical simulations). In particular, the contribution of this work is focused on the sensitivity analysis of the nonlinearities by FEA in the tissue-tool interaction through an explicit FEA solver. By doing in this way, we aim to demonstrate that the set-up of FEA computational surgical tools may be simplified in order to provide assistance to non-expert FEA engineers or medicians in more precise way of using FEA tools

Patient-specific simulation for autonomous surgery

An Autonomous Robotic Surgical System (ARSS) has to interact with the complex anatomical environment, which is deforming and whose properties are often uncertain. Within this context, an ARSS can benefit from the availability of patient-specific simulation of the anatomy. For example, simulation can provide a safe and controlled environment for the design, test and validation of the autonomous capabilities. Moreover, it can be used to generate large amounts of patient-specific data that can be exploited to learn models and/or tasks. The aim of this Thesis is to investigate the different ways in which simulation can support an ARSS and to propose solutions to favor its employability in robotic surgery. We first address all the phases needed to create such a simulation, from model choice in the pre-operative phase based on the available knowledge to its intra-operative update to compensate for inaccurate parametrization. We propose to rely on deep neural networks trained with synthetic data both to generate a patient-specific model and to design a strategy to update model parametrization starting directly from intra-operative sensor data. Afterwards, we test how simulation can assist the ARSS, both for task learning and during task execution. We show that simulation can be used to efficiently train approaches that require multiple interactions with the environment, compensating for the riskiness to acquire data from real surgical robotic systems. Finally, we propose a modular framework for autonomous surgery that includes deliberative functions to handle real anatomical environments with uncertain parameters. The integration of a personalized simulation proves fundamental both for optimal task planning and to enhance and monitor real execution. The contributions presented in this Thesis have the potential to introduce significant step changes in the development and actual performance of autonomous robotic surgical systems, making them closer to applicability to real clinical conditions