For over 20 years, interventional methods have improved the outcomes of patients with cardiovascular disease. However, these procedures require an intricate combination of visual and tactile feedback and extensive training. In this paper, we describe a series of novel approaches that have led to the development of a high-fidelity simulation system for interventional neuroradiology. In particular, we focus on a new approach for real-time deformation of devices such as catheters and guidewires during navigation inside complex vascular networks. This approach combines a real-time incremental Finite Element Model (FEM), an optimization strategy based on substructure decomposition, and a new method for handling collision response in situations where the number of contact points is very large. We also briefly describe other aspects of the simulation system, from patient-specific segmentation to the simulation of contrast agent propagation and fast volume-rendering techniques for generating synthetic X-ray images in real time. Although currently targeted at stroke therapy, our results are applicable to the simulation of any interventional radiology procedure.
For over 20 years, interventional methods have improved the outcomes of patients with cardiovascular disease. However, these procedures require an intricate combination of visual and tactile feedback and extensive training periods. In this paper, we describe a series of novel approaches that have lead to the development of a high-fidelity simulation system for interventional neuroradiology. In particular we focus on a new approach for real-time deformation of devices such as catheters and guidewires during navigation inside complex vascular networks. This approach combines a real-time incremental Finite Element Model, an optimization strategy based on substructure decomposition, and a new method for handling collision response in situations where the number of contacts points is very large. We also briefly describe other aspects of the simulation system, from patient-specific segmentation to the simulation of contrast agent propagation and fast volume rendering techniques for generating synthetic X-ray images in real-time.
In this paper, we propose a new approach to simulate the small intestine in a context of laparoscopic surgery. The ultimate aim of this work is to simulate the training of a basic surgical gesture in real-time: moving aside the intestine to reach hidden areas of the abdomen. The main problem posed by this kind of simulation is animating the intestine. The problem comes from the nature of the intestine: a very long tube which is not isotropically elastic, and is contained in a volume that is small when compared to the intestine's length. It coils extensively and collides with itself in many places. To do this, we use a layered model to animate the intestine. The intestine's axis is animated as a linear mechanical component. A specific sphere-based model handles contacts and self-collisions. A skinning model is used to create the intestine's volume around the axis. This paper discusses and compares three different representations for skinning the intestine: a parametric surface model and two implicit surface models. The first implicit surface model uses point skeletons while the second uses local convolution surfaces. Using these models, we obtained good-looking results in real-time. Some videos of this work can be found in the online version at doi: 10.1016/j.media.2004.11.006 and at www-imagis.imag.fr/Publications/2004/FLAMCFC04.
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