Francis Thibault

The Development of a Virtual Simulator for Training Neurosurgeons to Perform and Perfect Endoscopic Endonasal Transsphenoidal Surgery

BACKGROUND A virtual reality (VR) neurosurgical simulator with haptic feedback may provide the best model for training and perfecting surgical techniques for transsphenoidal approaches to the sella turcica and cranial base. Currently there are 2 commercially available simulators: NeuroTouch (Cranio and Endo) developed by the National Research Council of Canada in collaboration with surgeons at teaching hospitals in Canada, and the Immersive Touch. Work in progress on other simulators at additional institutions is currently unpublished. OBJECTIVE This article describes a newly developed application of the NeuroTouch simulator that facilitates the performance and assessment of technical skills for endoscopic endonasal transsphenoidal surgical procedures as well as plans for collecting metrics during its early use. METHODS The main components of the NeuroTouch-Endo VR neurosurgical simulator are a stereovision system, bimanual haptic tool manipulators, and high-end computers. The software engine continues to evolve, allowing additional surgical tasks to be performed in the VR environment. Device utility for efficient practice and performance metrics continue to be developed by its originators in collaboration with neurosurgeons at several teaching hospitals in the United States. Training tasks are being developed for teaching 1- and 2-nostril endonasal transsphenoidal approaches. Practice sessions benefit from anatomic labeling of normal structures along the surgical approach and inclusion (for avoidance) of critical structures, such as the internal carotid arteries and optic nerves. CONCLUSION The simulation software for NeuroTouch-Endo VR simulation of transsphenoidal surgery provides an opportunity for beta testing, validation, and evaluation of performance metrics for use in neurosurgical residency training. ABBREVIATIONS CTA, cognitive task analysisVR, virtual reality.BACKGROUND: A virtual reality (VR) neurosurgical simulator with haptic feedback may provide the best model for training and perfecting surgical techniques for transsphenoidal approaches to the sella turcica and cranial base. Currently there are 2 commercially available simulators: NeuroTouch (Cranio and Endo) developed by the National Research Council of Canada in collaboration with surgeons at teaching hospitals in Canada, and the Immersive Touch. Work in progress on other simulators at additional institutions is currently unpublished. OBJECTIVE: This article describes a newly developed application of the NeuroTouch simulator that facilitates the performance and assessment of technical skills for endoscopic endonasal transsphenoidal surgical procedures as well as plans for collecting metrics during its early use. METHODS: The main components of the NeuroTouch-Endo VR neurosurgical simulator are a stereovision system, bimanual haptic tool manipulators, and high-end computers. The software engine continues to evolve, allowing additional surgical tasks to be performed in the VR environment. Device utility for efficient practice and performance metrics continue to be developed by its originators in collaboration with neurosurgeons at several teaching hospitals in the United States. Training tasks are being developed for teaching 1- and 2-nostril endonasal transsphenoidal approaches. Practice sessions benefit from anatomic labeling of normal structures along the surgical approach and inclusion (for avoidance) of critical structures, such as the internal carotid arteries and optic nerves. CONCLUSION: The simulation software for NeuroTouch-Endo VR simulation of transsphenoidal surgery provides an opportunity for beta testing, validation, and evaluation of performance metrics for use in neurosurgical residency training.

Optimization of extrusion blow molding processes using soft computing and Taguchi's method

The objective of this study is to present a new numerical strategy using soft-computing techniques to determine the optimal die gap programming of extrusion blow molding processes. In this study, the design objective is to target a uniform part thickness after parison inflation by manipulating the parison die gap openings over time. To model the whole process, that is, the parison extrusion, the mould clamping and the parison inflation, commercial finite element software (BlowSim) from the National Research Council (NRC) of Canada is used. However, the use of such software is time-consuming and one important issue in a design environment is to minimize the number of simulations to get the optimal operating conditions. To do so, we proposed a new strategy called fuzzy neural–Taguchi network with genetic algorithm (FUNTGA) that establishes a back propagation network using a Taguchi’s experimental array to predict the relationship between design variables and responses. Genetic algorithm (GA) is then applied to search for the optimum design of die gap parison programming. As the number of training samples is greatly reduced due to the use of orthogonal arrays, the prediction accuracy of the neural network model is closely related to the distance between sampling points and the evolved designs. The extrapolation distance concept is proposed and introduced to GA using fuzzy rules to modify the fitness function and thus improving search efficiency. The comparison of the results with commercial optimization software from NRC demonstrates the effectiveness of the proposed approach.

Design optimization of the blow moulding process using a fuzzy optimization algorithm

Abstract Blow moulding is the forming of a hollow part by ‘blowing’ a mould cavity shaped parison made by a thermoplastic molten tube. Blow-moulded parts often require strict control of the thickness distribution in order to achieve the required mechanical performance and final weight. A fuzzy optimization algorithm for determining the optimal die gap openings and die geometry for the required thickness distribution in the blow moulding process is presented. The idea of the fuzzy optimization algorithm is that, instead of using purely numerical information to obtain the new design point in the next iteration, engineering knowledge and the human supervision process can be modelled in the optimization algorithm using fuzzy rules. The structure of an optimization algorithm is still maintained to guide the engineering decision process and to ensure that an optimal solution rather than a trial and error solution can be obtained. It is shown how a single fuzzy engine can be used in various cases and types of optimization of the blow moulding process.

The Use of Elasto‐Visco‐Plastic Material Model Coupled with Pressure‐Volume Thermodynamic Relationship to Simulate the Stretch Blow Molding of Polyethylene Terephthalate

The use of polyethylene terephthalate (PET) in the stretch blow molding process presents several challenging issues due to various processing parameters and complex behavior of the material, which is both temperature and strain‐rate dependent. In this paper, we generalize the G’Sell‐Jonas law in 3D to model and simulate the elasto‐visco‐plastic (EVP) behavior of PET, taking into account strain‐hardening and strain‐softening. It is observed that the internal pressure (inside the preform) is significantly different from the nominal pressure (imposed in the blowing device upstream) since the internal pressure and the enclosed volume of the preform are fully coupled. In order to accurately simulate this phenomenon, a thermodynamic model was used to characterize the pressure‐volume relationship (PVR). The predicted pressure evolution is thus more realistic when imposing only the machine power of the blowing device (air compressor or vacuum pump). Mechanical and temperature equilibrium equations are fully nonli...

A Lagrangian level set-like method for modelling and simulation in bioengineering

The level set method has been used for 20 years in a wide range of physical applications to track moving interfaces instead of an explicit description of the geometry. This paper studies in detail the shape of the level set function, delimiting a sub-domain in solid mechanics, with an innovative update method based on the computation of a displacement field obtained with the values of the level set function. A criterion based on the values of the level set function is proposed in order to assign the material properties. With the help of this criterion, an optimal approach is proposed, which predicts an accurate evolution of the sub-domain boundary. To validate this method, it was first applied in two dimensions to a through-thickness hole plate case, and then to the cases of brain tumour expansion and grasping to demonstrate the applicability of the method.

Modelling of Solidification Deformation in Automotive Formed Parts

The accurate prediction of part deformation due to solidification in automotive formed parts is important to help achieve an efficient production. Forming processes are those where a molten preform is deformed to take theshape of a mould cavity and subsequently solidified. Tolerance issues are critical in automotive applications and therefore part deformation due to solidification needs to be controlled and optimized accordingly. Formed parts can have a wide range of deformations according to the conditions of solidification. Both a small displacement and a large displacement formulation are developed for prediction of part deformation due to solidification. Experimental results obtained on a simple as well as complex automotive part are compared to determine whether the small displacement theory or the more complex approach is more appropriate.