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Soft tissue abnormalities are often correlated with a change in the mechanical properties of the soft tissue. New developing non-invasive techniques with the ability of early detection of cancerous tissue with high accuracy is a challenging state of art. In this paper, a new method is proposed to investigate the liver tissue cancers. Hyperelastic behavior of a porcine liver tissue has been extracted from the in vitro stress-strain experimental tests of the tissue. Hyperelastic coefficients have been used as the input of the Abaqus FEM software and the palpation of a physician has been simulated. The soft tissue contains a tumor with specified mechanical and geometrical properties. Artificial tactile sensing capability in tumor detection and localization has been investigated thoroughly. In mass localization we have focused on deeply located tumor which is a challenging area in the medical diagnosis. Moreover, tumor type differentiation which is commonly achieved through pathological investigations is studied by changing the stiffness ratio of the tumor and the tissue. Results show that the new proposed method has a high ability in mass detection, localization and type differentiation.

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Soft tissue’s cancers are related to major variations in the mechanical properties of the tissue. In recent years, a number of developing techniques have been introduced for early detection of soft tissue’s cancers. The major advantage of these methods over the common available techniques is while being noninvasive to the body, the accuracy of detection is noticeably increased. This article intends to analyze mechanical behavior of the breast tissue by considering a Mooney-Rivlin hyperelastic model. Coefficients of the model are defined by using a series of experimental mechanical datasets. For this purpose, a mechanical device is designed and fabricated base on a new noninvasive method named Artificial Tactile Sensing (ATS). The device is examined on 8 patients in 20 to 50 age range refer to “Jahad Daneshgahi Breast Diseases Clinic” while considering Helsinki agreement’s protocols. Due to wide anatomical variations of the breast tissue in individuals, 40 specified regions are examined on the tissues of all attended cases. Experimental stress versus strain datasets are collected for 40 test points. To achieve a reliable and optimized model, a genetic algorithm (GA) is used for calculating Mooney-Rivlin’s coefficients. Results confirmed that an accurate model can be afforded to estimate the soft tissue’s mechanical behavior with the least error. The model is suitable for disease diagnosis and follow-up procedure.

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This paper deals with studying and developing a proper constitutive model for liver tissue. For this purpose, deformation of liver in uniaxial compression, for two different strain rates, is analytically and numerically studied, based on both hyperelastic and hyperviscoelastic constitutive models. Both of the models are based on a polynomial-form energy function. The stress-strain curves, for uniaxial compression, obtained from these models, have been fitted to the existing experimental data to determine the model coefficients. Moreover the models are examined in uniaxial tension and pure shear loadings. ABAQUS commercial software, in which both of the models are available, has been used for numerical simulations. Then, to evaluate the computational analyses, analytical and numerical results have been compared with each other and also with the existing experimental data. The results show that the presented analytical solution and FE simulation are very close together and also both are accurate enough, compared with the experimental data and an acceptable stability is observed. Furthermore the effect of friction coefficient between the sample and the compressing plate in uniaxial compression test has been investigated. FE simulation results show that the stress will increase with increasing friction coefficient. This implies that friction coefficient must be carefully selected to accurately describe the tissue’s response. Compared with previously published researches on other tissues, the constitutive models adopted here to predict liver behavior is mathematically more complex due to non-zero material constants. Analytical solution of these constitutive models is, in fact, the main challenge and innovation of this paper.

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Nowadays, using of virtual reality in surgical training is taken consideration due to safety, reproducibility, lower cost and other benefits. The various presented method for virtual surgery have attempt to make it more real and also make it online. This paper presents a new methodology for the deformation of soft tissue by drawing an analogy between cellular neural network (CNN) and elastic and viscoelastic equations. Viscoelastic model has been resulted from collection between Navier-Cauchi equations and Kelvin-Voigt model. Furthermore, a haptic system for viscoelastic modeling of soft tissue deformation is presented. The displacement created at a point by external force is released throughout the tissue via the cellular neural network. Because this method needs to cubic meshing, a new meshing algorithm is designed that executed offline. Indeed a collision detection algorithm is used to detect collision between tool and cells that executed inside the main algorithm and force feedback using the force model provided by the neural network and the haptic interface. This algorithm is implemented on a 3d liver model and executed online.