15 March 2018 Using water-soluble additive manufacturing for cheap and soft silicon organ models
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Abstract
The evaluation and trial of computer-assisted surgery systems is an important part of the development process. Since human and animal trials are difficult to perform and have a high ethical value artificial organs and phantoms have become a key component for testing clinical systems. For soft-tissue phantoms like the liver it is important to match its biomechanical properties as close as possible. Organ phantoms are often created from silicone that is shaped in casting molds. Silicone is relatively cheap and the method doesn’t rely on expensive equipment. One big disadvantage of silicone phantoms is their high rigidity. To this end, we propose a new method for the generation of silicon phantoms with a softer and mechanically more accurate structure. Since we can’t change the rigidity of silicone we developed a new and easy method to weaken the structure of the silicone phantom. The key component is the misappropriation of water-soluble support material from 3D FDM-printing. We designed casting molds with an internal grid structure to reduce the rigidity of the structure. The molds are printed with an FDM (Fused Deposition Modeling) printer and entirely from water-soluble PVA (Polyvinyl Alcohol) material. After the silicone is hardened, the mold with the internal structure can be dissolved in water. The silicone phantom is then pervaded with a grid of cavities. Our experiments have shown that we can control the rigidity of the model up to a 70% reduction of its original value. The rigidity of our silicon models is simply controlled with the size of the internal grid structure.
Conference Presentation
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Daniel Reichard, Markus Gern, Isabel Funke, Sebastian Bodenstedt, Hannes Kenngott, Beat Peter Müller-Stich, Christian Pylatiuk, Rüdiger Dillmann, Stefanie Speidel, "Using water-soluble additive manufacturing for cheap and soft silicon organ models", Proc. SPIE 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, 105761Q (15 March 2018); doi: 10.1117/12.2293339; https://doi.org/10.1117/12.2293339
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