Inverse-FEM Characterization of a Brain Tissue Phantom to Simulate Compression and Indentation

Abstract

The realistic simulation of tool-tissue interactions is necessary for the development of surgical simulators and one of the key element for it realism is accurate bio-mechanical tissue models. In this paper, we determined the mechanical properties of soft tissue by minimizing the difference between experimental measurements and the analytical or simulated solution of the deformation. Then, we selected the best model parameters that fit the experimental data to simulate a bonded compression and a needle indentation with a flat-tip. We show that the inverse FEM allows accurate material property estimation. We also validated our results using multiple tool-tissue interactions over the same specimen. Caracterización de tejido cerebral artificial utilizando Inverse-FEM para simular indentación y comprensión ResumenUna simulación realista de la interacción tejido-herramienta es necesaria para desarrollar simuldores quirúrgicos, y la presición en modelos biomecánicos de tejidos es determinante para cumplir tal fin. Los trabajos previos han caracterizado las propiedades de tejidos blandos; sin embargo, ha faltado una validación apropiada de los resultados. En este trabajo se determinaron las propiedades mecánicas de un tejido blando minimizando la diferencia entre las mediciones experimentales y la solución analítica o simulada del problema. Luego, fueron seleccionados los parámetros que mejor se ajustaron a los datos experimentales para simular una compresión con fricción y la indentación de una aguja con punta plana. Se concluye que el inverse-FEM permite la precisa estimación de las propiedades del material. Además, estos resultados fueron validados con varias interacciones tejido-herramienta sobre el mismo espécimen.

Experimental evaluation of neural probe's insertion induced injury based on digital image correlation method

“…The initial Young's modulus of the tissue phantom was 8429.6 Pa, 18 which is very close to the value of 7 kPa (porcine brain) used in the study conducted by Oldfield et al 20 In Ref. 20, the researchers utilized a gelatine phantom, which was a linear model, to simulate the needle's insertion into soft tissue.…”

“…As shown by Mesa-Múnera et al, 18 silicon rubber (Ecoflex-0010, Smooth-on, Inc.) mixed with a proportion (20%) of the softener (Slacker, Smooth-on, Inc.) can successfully simulate human brain's hyperelastic mechanical behavior. Hence, in this study we use this method to prepare biological brain tissue phantom.…”

Uniaxial compression testing and Cauchy stress modeling to design anatomical silicone replicas

“…Sixth, friction plays a role in uniaxial compression test experiments 16,17 . Related work found no significant differences for the chosen silicone 18 . Indeed, finite element (FE) simulation experiments revealed no significant effects in the ranges of a friction coefficient for silicones between 0.6 to 0.9, which corresponds to the range of the tested silicone 18 .…”

“…Related work found no significant differences for the chosen silicone 18 . Indeed, finite element (FE) simulation experiments revealed no significant effects in the ranges of a friction coefficient for silicones between 0.6 to 0.9, which corresponds to the range of the tested silicone 18 . Since this neither affects the reaction forces, nor the geometrical changes, our measurements and results are comparable.…”

Dynamics and trajectory optimization for a soft spatial fluidic elastomer manipulator

“…During normal-force bracing, a rigid-body robot is coated with a wear-resistant surface (Book et al, 1985), which has a low coefficient of static friction when in contact with solids (ToolBox, nd). For example, teflon in contact with steel has a μ s of between 0.05 and 0.20 (ToolBox, nd) whereas soft silicone rubber in contact with steel has a μ s of between 0.6 and 0.9 (Mesa Múnera et al, 2011). It follows that for a given tangential force F t , a hard robot will have to exert a force on the object of between three and 18 times greater than a soft robot to maintain a static brace.…”