Interactive simulations of deformable bodies are a growing research area with possible applications in several fields, i.e. computer aided surgery. The main implementation issue is to mimic the real behavior of the body at the extremely high rates required by haptic devices. Since even high-end computers have inadequate performance, one possible solution is to exploit the parallelism of modern Graphics Processing Units.In this paper we present our research aiming at moving the whole computational process from the CPU to the GPU taking advantage of the computational power of the graphics hardware. We use a mass-spring model, augmented with local damping coefficients and volume preservation forces. Collision detection is performed against external rigid bodies with high complexity mesh, such as the skeleton's one. The user interacts with the model by controlling virtual tools, i.e. probes or tweezers. Haptic forces are computed on GPU and the results are asyncronously transferred to the CPU. Our approach can simulate the deformation of complex models with gravity and interaction with environment and tools at a frame rate higher than 1 KHz, making it suitable for visual rendering and haptic feedback.

The two main categories of deformable models used in surgical simulators are Mass Spring Models (MSM) and Finite Element Models (FEM). Mass spring models are often preferred due to their simplicity and low computational cost and because they allow to perform topology changes on the modeled body without significant computational overhead. The principal drawback of the mass spring model is the need of complex calibration procedure since they don't have a clear physical meaning. In this paper we propose a new method to calibrate mass spring models. Our method uses CAT data to identify mass values and deformation measures to define elastic coefficient and damping ratio for the springs of the model. Spring parameters are obtained through a genetic algorithm that minimizes the difference between the model and the measured behavior. The algorithm we developed to compute the masses was tested with medical CAT data whereas the spring algorithm correctness was tested with synthetic models. Simulation verifications are presented.

The comparison of the developments obtained by training for aviation with the ones obtained by training for surgery highlights the efforts that are still required to define shared and validated training curricula for surgeons. This work focuses on robotic assisted surgery and the related training systems to analyze the current approaches to surgery training based on virtual environments. Limits of current simulation technology are highlighted and the systems currently on the market are compared in terms of their mechanical design and characteristics of the virtual environments offered. In particular the analysis focuses on the level of realism, both graphical and physical, and on the set of training tasks proposed.Some multimedia material is proposed to support the analysis and to highlight the differences between the simulations and the approach to training. From this analysis it is clear that, although there are several training systems on the market, some of them with a lot of scientific literature proving their validity, there is no consensus about the tasks to include in a training curriculum or the level of realism required to virtual environments to be useful.Keywords: Surgical training; simulation; virtual environment; training curriculum This simplification of the interaction between the virtual environment and the surgeon simplifies the analysis of the effectiveness of virtual environments in surgical training by reducing the number of variables to consider.For this reason, this work focuses on robotic surgery training systems; however, the considerations related to virtual environments used for training in robotic assisted surgery can be easily extended to other surgical training systems based on virtual environments. Robotic assisted surgery training systemsThe market proposes five systems dedicated to the training of robotic assisted surgery. In alphabetical order they are: Actaeon, by BBZ srl; dV Trainer, by Mimic Technologies Inc.; RobotiX Mentor by 3D Systems USA Corp; ROSS, by Simulated Surgical Systems LLC; and SEP robot by SimSurgery AS. In addition to these systems it is possible to use the actual robot console to train in virtual environments thanks to the da Vinci Skills Simulator: an hardware module developed by the robot manufacturer Intuitive Surgical which can be attached to the robot console and that simulates the robotic tools and the environment.These systems follow completely different approaches in the way they recreate the look and feel of the robotic console. A visual comparison of the systems is provided in Figure 2. Actaeon, ROSS and da Vinci Skills Simulator use robotics arms to reproduce the input devices, whereas dV Trainer uses a pair of cable driven input devices to get user input. RobotiX Mentor and SEP robot use magnetically tracked devices to get user's hands pose and orientation.Actaeon is the only training system which uses hardware specifically designed to reproduce the da Vinci control console whereas other systems integrates existing technologies.During e...

The integration of physics-based deformable models in simulations greatly increases the realism of the virtual environment, taking into account real tissue properties and allowing the user to feel the actual forces exerted by organs on virtual tools. Our method proves the feasibility of exploiting GPU to simulate deformable models in interactive virtual environments.

Abstract. Minimally invasive robotic surgery has gained wide acceptance recently. Computer-aided features to assist the surgeon during these interventions may help to develop safer, faster, and more accurate procedures. Especially physiological motion compensation of the beating heart and online soft tissue modelling are promising features that were developed recently. This paper presents the integration of these new features into the minimally invasive robotic surgery platform MiroSurge. A central aim of this research platform is to enable evaluation and comparison of new functionalities for minimally invasive robotic surgery. The system structure of MiroSurge is presented as well as the interfaces for the new functionalities. Some details about the modules for motion tracking and for soft tissue simulation are given. Results are shown with an experimental setup that includes a heart motion simulator and dedicated silicone organ models. Both features are integrated seamlessly and work reliably in the chosen setup. The MiroSurge platform thus shows the potential to provide valuable results in evaluating new functionalities for minimally invasive robotic surgery.