Tissue identification using inverse Finite Element analysis of rolling indentation

Sangpradit, Kiattisak; Liu, Hongbin; Seneviratne, Lakmal; Althoefer, Kaspar

doi:10.1109/robot.2009.5152644

Cited by 31 publications

(26 citation statements)

References 14 publications

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“…The silicone phantom was modeled based on the studies in [24] -a non-linear, isotropic, incompressible, and hyperelastic Arruda-Boyce model was used. The size of the model of the silicone block was set 50 × 50 × 30 mm 3 .…”

Section: A Design Of Fe Simulationsmentioning

confidence: 99%

Behavioral Characteristics of Manual Palpation to Localize Hard Nodules in Soft Tissues

Konstantinova

Mehra

et al. 2014

IEEE Trans. Biomed. Eng.

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Abstract-Improving the effectiveness of artificial tactile sensors for soft tissue examination and tumor localization is a pressing need in robot-assisted minimally invasive surgery. Despite the availability of tactile probes, guidelines for optimal examination behaviors that best exploit soft tissue properties are not available as yet. Simulations on soft-tissue palpation show that particular stress-velocity patterns of probing lead to constructive dynamic interactions between the probe and the tissue to localize hard nodules. However, there has been no methodical validation of such probing behavioral hypotheses using human participants so far. In this study, we use simulation studies to establish open hypotheses on the interaction and influence of relevant behavioral palpation variables, such as finger velocity and trajectory, force exerted by fingers on the accuracy of detection of embedded nodules. We test them by analyzing palpation strategies used by humans to detect hard nodules inside silicone phantoms and ex-vivo porcine organs. Our findings allow us, for the first time, to derive palpation behavior guidelines suitable for the design of controllers of palpation robots.

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Section: A Design Of Fe Simulationsmentioning

confidence: 99%

Behavioral Characteristics of Manual Palpation to Localize Hard Nodules in Soft Tissues

Konstantinova

Mehra

et al. 2014

IEEE Trans. Biomed. Eng.

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“…Results have been reported for different organs of animals and humans, such as the liver [10], the brain [11], [12], and the kidney [7]. Some researchers have used ex-vivo experiments and phantom tissues, as they allow precise control of the sample and experimental conditions for modeling [6], [13].…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2012

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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.

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“…Wang et al [13], [14] researched an identification method for properties of rheological objects based on a FE formulation. Sangpradit et al [15] introduced the concept of a robotic indentation tool using FEA, and showed preliminary experiments for it. We researched the problem of determining the values of human material parameters for surgical use [16], [17].…”

Section: Related Workmentioning

confidence: 99%

Development and evaluation of an identification method for the biomechanical parameters using robotic force measurements, medical images, and FEA

Hoshi

Tsukune

Kobayashi

et al. 2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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This paper presents a new identification method for the biomechanical parameters of human tissues for the purpose of improving the accuracy of dynamic organ simulation. We describe the formulation of the method, and also design a robotic system to implement the method using a robotic probe, a medical imaging device, and a numerical simulator for the finite element analysis (FEA). We carried out an experiment using an experimental system and a tissue phantom to verify the effectiveness of the method. The results of this experiment show that the Young's modulus of the tissue phantom can be estimated with the experimental system. We also compared the estimated values of the Young's moduli with the measured values from a rheometer. These results confirm that the identification method and the system design, proposed and developed in this work, are effective for accurately simulating organ behavior.

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Tissue identification using inverse Finite Element analysis of rolling indentation

Cited by 31 publications

References 14 publications

Behavioral Characteristics of Manual Palpation to Localize Hard Nodules in Soft Tissues

Behavioral Characteristics of Manual Palpation to Localize Hard Nodules in Soft Tissues

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

Development and evaluation of an identification method for the biomechanical parameters using robotic force measurements, medical images, and FEA

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