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Journal articles on the topic 'Soft tissue simulation'

Author: Grafiati

Published: 11 March 2023

Last updated: 31 July 2025

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1

Guo, Lei, Xin Guo, and Feiya Lv. "A Study on Dual-Mode Hybrid Dynamics Finite Element Algorithm for Human Soft Tissue Deformation Simulation." Symmetry 17, no. 5 (2025): 765. https://doi.org/10.3390/sym17050765.

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The simulation of human soft tissue deformation is a key issue in the research of surgical simulators. The most mathematically accurate model for soft tissue behavior is the finite element model (FEM), being the most widely adopted numerical approach for nonlinear continuum mechanics equations. The total Lagrangian explicit dynamics (TLED) model is a nonlinear FEM that could simulate the nonlinear deformation of soft tissues accurately and in real time. However, the main problems faced by this method are the high computational cost and the real-time performance of the simulation. Therefore, th

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ZHANG, JINAO, JEREMY HILLS, YONGMIN ZHONG, BIJAN SHIRINZADEH, JULIAN SMITH, and CHENGFAN GU. "TEMPERATURE-DEPENDENT THERMOMECHANICAL MODELING OF SOFT TISSUE DEFORMATION." Journal of Mechanics in Medicine and Biology 18, no. 08 (2018): 1840021. http://dx.doi.org/10.1142/s0219519418400213.

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Modeling of thermomechanical behavior of soft tissues is vitally important for the development of surgical simulation of hyperthermia procedures. Currently, most literature considers only temperature-independent thermal parameters, such as the temperature-independent tissue specific heat capacity, thermal conductivity and stress–strain relationships for soft tissue thermomechanical modeling; however, these thermal parameters vary with temperatures as shown in the literature. This paper investigates the effect of temperature-dependent thermal parameters for soft tissue thermomechanical modeling

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Park, Dae Woo. "Ultrasound Shear Wave Simulation of Breast Tumor Using Nonlinear Tissue Elasticity." Computational and Mathematical Methods in Medicine 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/2541325.

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Shear wave elasticity imaging (SWEI) can assess the elasticity of tissues, but the shear modulus estimated in SWEI is often less sensitive to a subtle change of the stiffness that produces only small mechanical contrast to the background tissues. Because most soft tissues exhibit mechanical nonlinearity that differs in tissue types, mechanical contrast can be enhanced if the tissues are compressed. In this study, a finite element- (FE-) based simulation was performed for a breast tissue model, which consists of a circular (D: 10 mm, hard) tumor and surrounding tissue (soft). The SWEI was perfo

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Stewart, Lygia, and Elizabeth De La Rosa. "Creation of a High Fidelity, Cost Effective, Real World Surgical Simulation for Surgical Education." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 10, no. 1 (2021): 147. http://dx.doi.org/10.1177/2327857921101081.

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Background How do surgical residents learn to operate? What is a surgical plane? How does one learn to see and dissect the plane? How do surgical residents learn tissue handling and suturing (sewing)? One method to learn and practice performing surgery is through the use of simulation training. Surgical training models include laparoscopic box trainers (a plastic box with holes for instruments) with synthetic materials inside to simulate tissues, or computer-based virtual reality simulation for laparoscopic, endoscopic, and robotic techniques. These methods, however, do not use real tissues. T

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Omar, Nadzeri, Yongmin Zhong, Julian Smith, and Chengfan Gu. "Local deformation for soft tissue simulation." Bioengineered 7, no. 5 (2016): 291–97. http://dx.doi.org/10.1080/21655979.2016.1197712.

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6

Fischle, Andreas, Axel Klawonn, Oliver Rheinbach, and Jörg Schröder. "Parallel Simulation of Biological Soft Tissue." PAMM 12, no. 1 (2012): 767–68. http://dx.doi.org/10.1002/pamm.201210372.

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7

Olejnik, Anna, Laurence Verstraete, Tomas-Marijn Croonenborghs, Constantinus Politis, and Gwen R. J. Swennen. "The Accuracy of Three-Dimensional Soft Tissue Simulation in Orthognathic Surgery—A Systematic Review." Journal of Imaging 10, no. 5 (2024): 119. http://dx.doi.org/10.3390/jimaging10050119.

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Three-dimensional soft tissue simulation has become a popular tool in the process of virtual orthognathic surgery planning and patient–surgeon communication. To apply 3D soft tissue simulation software in routine clinical practice, both qualitative and quantitative validation of its accuracy are required. The objective of this study was to systematically review the literature on the accuracy of 3D soft tissue simulation in orthognathic surgery. The Web of Science, PubMed, Cochrane, and Embase databases were consulted for the literature search. The systematic review (SR) was conducted according

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Dosaev, Marat, Vitaly Samsonov, and Vladislav Bekmemetev. "Comparison between 2D and 3D Simulation of Contact of Two Deformable Axisymmetric Bodies." International Journal of Nonlinear Sciences and Numerical Simulation 21, no. 2 (2020): 123–33. http://dx.doi.org/10.1515/ijnsns-2018-0157.

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AbstractA portable pneumatic video-tactile sensor for determining the local stiffness of soft tissue and the methodology for its application are considered. The expected range of local elastic modulus that can be estimated by the sensor is 100 kPa–1 MPa. The current version of the device is designed to determine the characteristics of tissues that are close in mechanical properties to the skin with subcutis and muscles. A numerical simulation of the contact between the sensor head and the soft tissue was performed using the finite-element method. Both 2D and 3D models were developed. Results o

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Qian, Kun, Tao Jiang, Meili Wang, Xiaosong Yang, and Jianjun Zhang. "Energized soft tissue dissection in surgery simulation." Computer Animation and Virtual Worlds 27, no. 3-4 (2016): 280–89. http://dx.doi.org/10.1002/cav.1691.

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10

Liao, Xiangyun, Zhiyong Yuan, Pengfei Hu, and Qianfeng Lai. "GPU-assisted energy asynchronous diffusion parallel computing model for soft tissue deformation simulation." SIMULATION 90, no. 11 (2014): 1199–208. http://dx.doi.org/10.1177/0037549714552708.

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Soft tissue deformation simulation is a key technology of virtual surgical simulation. In this work, we present a graphics processing unit (GPU)-assisted energy asynchronous diffusion parallel computing model which is stable and fast in processing complex models, especially concave surface models. We adopt hexahedral voxels to represent the physical model of soft tissue to improve the visual realistic quality and computing efficiency of deformation simulation. We also adopt the concept of free boundary to simulate soft tissue geometric characteristics more precisely during the deformation proc

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11

Little, J. Paige, Clayton Adam, John H. Evans, Graeme Pettet, and Mark J. Pearcy. "Finite Element Simulation of an L4/5 Lumbar Intervertebral Disc(Soft Tissue Mechanics)." Proceedings of the Asian Pacific Conference on Biomechanics : emerging science and technology in biomechanics 2004.1 (2004): 181–82. http://dx.doi.org/10.1299/jsmeapbio.2004.1.181.

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12

Wittek, Adam, George Bourantas, Benjamin F. Zwick, Grand Joldes, Lionel Esteban, and Karol Miller. "Mathematical modeling and computer simulation of needle insertion into soft tissue." PLOS ONE 15, no. 12 (2020): e0242704. http://dx.doi.org/10.1371/journal.pone.0242704.

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In this study we present a kinematic approach for modeling needle insertion into soft tissues. The kinematic approach allows the presentation of the problem as Dirichlet-type (i.e. driven by enforced motion of boundaries) and therefore weakly sensitive to unknown properties of the tissues and needle-tissue interaction. The parameters used in the kinematic approach are straightforward to determine from images. Our method uses Meshless Total Lagrangian Explicit Dynamics (MTLED) method to compute soft tissue deformations. The proposed scheme was validated against experiments of needle insertion i

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13

Liu, Xuemei, Ruiyi Wang, Yunhua Li, and Dongdong Song. "Deformation of Soft Tissue and Force Feedback Using the Smoothed Particle Hydrodynamics." Computational and Mathematical Methods in Medicine 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/598415.

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We study the deformation and haptic feedback of soft tissue in virtual surgery based on a liver model by using a force feedback device named PHANTOM OMNI developed by SensAble Company in USA. Although a significant amount of research efforts have been dedicated to simulating the behaviors of soft tissue and implementing force feedback, it is still a challenging problem. This paper introduces a kind of meshfree method for deformation simulation of soft tissue and force computation based on viscoelastic mechanical model and smoothed particle hydrodynamics (SPH). Firstly, viscoelastic model can p

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Brake, Elena Alida, Yordan Kyosev, and Katerina Rose. "Investigation of the tissue displacement through textile pressure on soft avatar in Browzwear’s VStitcher software." Communications in Development and Assembling of Textile Products 5, no. 2 (2024): 151–60. http://dx.doi.org/10.25367/cdatp.2024.5.p151-160.

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Nowadays, soft avatars are used in various fields to simulate the behavior of human soft tissues in different applications. Likewise, they are also utilized in the garment industry in order to achieve a realistic testing of the fit and functionality of tight-fitting clothing. Therefore it is important that avatars in CAD programs for clothing conform to the mechanical properties of human soft tissue. The accuracy of the avatars' properties in simulating the change in shape of human tissue is crucial here, which is caused by the contact pressure that compressive or tight-fitting garments exert

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15

Heikkilä, Janne, and Kullervo Hynynen. "Investigation of Optimal Method for Inducing Harmonic Motion in Tissue Using a Linear Ultrasound Phased Array — A Simulation Study." Ultrasonic Imaging 28, no. 2 (2006): 97–113. http://dx.doi.org/10.1177/016173460602800203.

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Many noninvasive ultrasound techniques have been developed to explore mechanical properties of soft tissues. One of these methods, Localized Harmonic Motion Imaging (LHMI), has been proposed to be used for ultrasound surgery monitoring. In LHMI, dynamic ultrasound radiation-force stimulation induces displacements in a target that can be measured using pulse-echo imaging and used to estimate the elastic properties of the target. In this initial, simulation study, the use of a one-dimensional phased array is explored for the induction of the tissue motion. The study compares three different dual

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Farrell, Joyce, Zheng Lyu, Zhenyi Liu, et al. "Soft-prototyping imaging systems for oral cancer screening." Electronic Imaging 2020, no. 7 (2020): 212–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.7.iss-212.

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We are using image systems simulation technology to design a digital camera for measuring fluorescent signals; a first application is oral cancer screening. We validate the simulations by creating a camera model that accurately predicts measured RGB values for any spectral radiance. Then we use the excitationemission spectra for different biological fluorophores to predict measurements of fluorescence of oral mucosal tissue under several different illuminations. The simulations and measurements are useful for (a) designing cameras that measure tissue fluorescence and (b) clarifying which fluor

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17

Awad, Daniel, Siegmar Reinert, and Susanne Kluba. "Accuracy of Three-Dimensional Soft-Tissue Prediction Considering the Facial Aesthetic Units Using a Virtual Planning System in Orthognathic Surgery." Journal of Personalized Medicine 12, no. 9 (2022): 1379. http://dx.doi.org/10.3390/jpm12091379.

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Virtual surgical planning (VSP) is commonly used in orthognathic surgery. A precise soft-tissue predictability would be a helpful tool, for determining the correct displacement distances of the maxilla and mandible. This study aims to evaluate the soft-tissue predictability of the VSP software IPS CaseDesigner® (KLS Martin Group, Tuttlingen, Germany). Twenty patients were treated with bimaxillary surgery and were included in the study. The soft-tissue simulation, done by the VSP was exported as STL files in the engineering software Geomagic Control XTM (3D systems, RockHill, SC, USA). Four mon

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Alcañiz, Patricia, Jesús Pérez, Alessandro Gutiérrez, et al. "Soft-Tissue Simulation for Computational Planning of Orthognathic Surgery." Journal of Personalized Medicine 11, no. 10 (2021): 982. http://dx.doi.org/10.3390/jpm11100982.

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Simulation technologies offer interesting opportunities for computer planning of orthognathic surgery. However, the methods used to date require tedious set up of simulation meshes based on patient imaging data, and they rely on complex simulation models that require long computations. In this work, we propose a modeling and simulation methodology that addresses model set up and runtime simulation in a holistic manner. We pay special attention to modeling the coupling of rigid-bone and soft-tissue components of the facial model, such that the resulting model is computationally simple yet accur

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19

Oz, Aslihan Zeynep, Cenk Ahmet Akcan, Hakan El, and Semra Ciger. "Evaluation of the soft tissue treatment simulation module of a computerized cephalometric program." European Journal of Dentistry 08, no. 02 (2014): 229–33. http://dx.doi.org/10.4103/1305-7456.130614.

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ABSTRACT Objective: The purpose of this study is to compare the accuracy of the treatment simulation module of Quick Ceph Studio (QCS) program to the actual treatment results in Class II Division 1 patients. Design: Retrospective study. Materials and Methods: Twenty-six skeletal Class II patients treated with functional appliances were included. T0 and T1 lateral cephalograms were digitized using QCS. Before applying treatment simulation to the digitized cephalograms, the actual T0-T1 difference was calculated for the SNA, SNB, ANB angles, maxillary incisor inclination, and protrusion and mand

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20

Aootaphao, Sorapong, Saowapak S. Thongvigitmanee, Jartuwat Rajruangrabin, Chalinee Thanasupsombat, Tanapon Srivongsa, and Pairash Thajchayapong. "X-Ray Scatter Correction on Soft Tissue Images for Portable Cone Beam CT." BioMed Research International 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/3262795.

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Soft tissue images from portable cone beam computed tomography (CBCT) scanners can be used for diagnosis and detection of tumor, cancer, intracerebral hemorrhage, and so forth. Due to large field of view, X-ray scattering which is the main cause of artifacts degrades image quality, such as cupping artifacts, CT number inaccuracy, and low contrast, especially on soft tissue images. In this work, we propose the X-ray scatter correction method for improving soft tissue images. The X-ray scatter correction scheme to estimate X-ray scatter signals is based on the deconvolution technique using the m

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21

Ionescu, Irina, James E. Guilkey, Martin Berzins, Robert M. Kirby, and Jeffrey A. Weiss. "Simulation of Soft Tissue Failure Using the Material Point Method." Journal of Biomechanical Engineering 128, no. 6 (2006): 917–24. http://dx.doi.org/10.1115/1.2372490.

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Understanding the factors that control the extent of tissue damage as a result of material failure in soft tissues may provide means to improve diagnosis and treatment of soft tissue injuries. The objective of this research was to develop and test a computational framework for the study of the failure of anisotropic soft tissues subjected to finite deformation. An anisotropic constitutive model incorporating strain-based failure criteria was implemented in an existing computational solid mechanics software based on the material point method (MPM), a quasi-meshless particle method for simulatio

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22

Nakayama, Masano, Satoko Abiko, Xin Jiang, Atsushi Konno, and Masaru Uchiyama. "Stable Soft-Tissue Fracture Simulation for Surgery Simulator." Journal of Robotics and Mechatronics 23, no. 4 (2011): 589–97. http://dx.doi.org/10.20965/jrm.2011.p0589.

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Soft-tissue fracture simulation is a key to surgery simulation virtually reproducing cutting, dissection, and removal. Soft-tissue fracture is modeled by finite element fracture in which elements are removed if their stress exceeds a specified fracture stress. Removing elements without considering connection to adjacent elements may produce structurally unstable elements, that cause computational instability. We propose geometric limitation and element fracture method to avoid this instability. We confirmed the feasibility of our proposals by comparing blunt dissection simulation results to bl

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Sheen, Seung Heon, Egor Larionov, and Dinesh K. Pai. "Volume Preserving Simulation of Soft Tissue with Skin." Proceedings of the ACM on Computer Graphics and Interactive Techniques 4, no. 3 (2021): 1–23. http://dx.doi.org/10.1145/3480143.

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Simulation of human soft tissues in contact with their environment is essential in many fields, including visual effects and apparel design. Biological tissues are nearly incompressible. However, standard methods employ compressible elasticity models and achieve incompressibility indirectly by setting Poisson's ratio to be close to 0.5. This approach can produce results that are plausible qualitatively but inaccurate quantatively. This approach also causes numerical instabilities and locking in coarse discretizations or otherwise poses a prohibitive restriction on the size of the time step. We

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Delingette, H. "Toward realistic soft-tissue modeling in medical simulation." Proceedings of the IEEE 86, no. 3 (1998): 512–23. http://dx.doi.org/10.1109/5.662876.

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25

Caldwell, Julia, and James J. Mooney. "Analysis of Soft Tissue Materials for Simulation Development." Simulation in Healthcare: The Journal of the Society for Simulation in Healthcare 14, no. 5 (2019): 312–17. http://dx.doi.org/10.1097/sih.0000000000000382.

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26

Thomas, Paul M. "Three-Dimensional Soft Tissue Simulation in Orthognathic Surgery." Atlas of the Oral and Maxillofacial Surgery Clinics 28, no. 2 (2020): 73–82. http://dx.doi.org/10.1016/j.cxom.2020.05.003.

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27

Zhong, Yongmin, Bijan Shirinzadeh, Gursel Alici, and Julian Smith. "Soft tissue modelling through autowaves for surgery simulation." Medical & Biological Engineering & Computing 44, no. 9 (2006): 805–21. http://dx.doi.org/10.1007/s11517-006-0084-7.

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Zyganitidis, Christos, Kristina Bliznakova, and Nicolas Pallikarakis. "A novel simulation algorithm for soft tissue compression." Medical & Biological Engineering & Computing 45, no. 7 (2007): 661–69. http://dx.doi.org/10.1007/s11517-007-0205-y.

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29

Wissel, Tobias, Ralf Bruder, Achim Schweikard, and Floris Ernst. "Estimating soft tissue thickness from light-tissue interactions––a simulation study." Biomedical Optics Express 4, no. 7 (2013): 1176. http://dx.doi.org/10.1364/boe.4.001176.

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30

Almigdad, Ahmad. "Bony and Soft Tissue Hand Tumors." Clinical Orthopaedics and Trauma Care 5, no. 2 (2023): 01–09. http://dx.doi.org/10.31579/2694-0248/057.

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Background: The hand is a common site of tumors and tumor-like pathology. This study aimed to evaluate hand tumors and their distribution regarding age, gender, and histopathological characteristics to promote better understanding and aid in diagnosis. Material and Methods: A total of 261 incisional or excisional biopsies for hand tumors were reviewed retrospectively from January 2017 to December 2022 at Princess Iman Research Center. The tumor was assessed according to the tumor origin, and histopathological diagnosis was analyzed regarding age and gender to find the correlation. Biopsies wit

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Nicolas, Jan-David, Sebastian Aeffner, and Tim Salditt. "Radiation damage studies in cardiac muscle cells and tissue using microfocused X-ray beams: experiment and simulation." Journal of Synchrotron Radiation 26, no. 4 (2019): 980–90. http://dx.doi.org/10.1107/s1600577519006817.

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Soft materials are easily affected by radiation damage from intense, focused synchrotron beams, often limiting the use of scanning diffraction experiments to radiation-resistant samples. To minimize radiation damage in experiments on soft tissue and thus to improve data quality, radiation damage needs to be studied as a function of the experimental parameters. Here, the impact of radiation damage in scanning X-ray diffraction experiments on hydrated cardiac muscle cells and tissue is investigated. It is shown how the small-angle diffraction signal is affected by radiation damage upon variation

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32

Gao, De Dong, and Hao Jun Zheng. "Simulation for Needle Deflection and Soft Tissue Deformation in Needle Insertion." Advanced Materials Research 139-141 (October 2010): 889–92. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.889.

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Needle deflection and soft tissue deformation are the most important factors that affect accuracy in needle insertion. Based on the quasi-static thinking and needle forces, an improved virtual spring model and a finite element method are presented to analyze needle deflection and soft tissue deformation when a needle is inserted into soft tissue. According to the spring model, the trajectory of the needle tip is calculated with MATLAB using different parameters. With the superposed element method, the two and three dimensional quasi-static finite element models are created to simulate the dyna

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Moreno-Guerra, Mario R., Oscar Martínez-Romero, Luis Manuel Palacios-Pineda, et al. "Soft Tissue Hybrid Model for Real-Time Simulations." Polymers 14, no. 7 (2022): 1407. http://dx.doi.org/10.3390/polym14071407.

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In this article, a recent formulation for real-time simulation is developed combining the strain energy density of the Spring Mass Model (SMM) with the equivalent representation of the Strain Energy Density Function (SEDF). The resulting Equivalent Energy Spring Model (EESM) is expected to provide information in real-time about the mechanical response of soft tissue when subjected to uniaxial deformations. The proposed model represents a variation of the SMM and can be used to predict the mechanical behavior of biological tissues not only during loading but also during unloading deformation st

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Mazza, E., O. Papes, M. B. Rubin, S. R. Bodner, and N. S. Binur. "Simulation of the Aging Face." Journal of Biomechanical Engineering 129, no. 4 (2006): 619–23. http://dx.doi.org/10.1115/1.2746388.

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A three-dimensional finite element program is described which attempts to simulate the nonlinear mechanical behavior of an aging human face with specific reference to progressive gravimetric soft tissue descent. A cross section of the facial structure is considered to consist of a multilayered composite of tissues with differing mechanical behavior. Relatively short time (elastic-viscoplastic) behavior is governed by equations previously developed which are consistent with mechanical tests. The long time response is controlled by the aging elastic components of the tissues. An aging function i

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Chanda, Arnab, and Christian Callaway. "Tissue Anisotropy Modeling Using Soft Composite Materials." Applied Bionics and Biomechanics 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/4838157.

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Soft tissues in general exhibit anisotropic mechanical behavior, which varies in three dimensions based on the location of the tissue in the body. In the past, there have been few attempts to numerically model tissue anisotropy using composite-based formulations (involving fibers embedded within a matrix material). However, so far, tissue anisotropy has not been modeled experimentally. In the current work, novel elastomer-based soft composite materials were developed in the form of experimental test coupons, to model the macroscopic anisotropy in tissue mechanical properties. A soft elastomer

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Fouly, Ahmed, Ahmed M. R. FathEl-Bab, A. A. Abouelsoud, T. Tsuchiya, and O. Tabata. "Design and Simulation of Micro Tactile Sensor for Stiffness Detection of Soft Tissue with Irregular Surface." Sensor Letters 18, no. 3 (2020): 200–209. http://dx.doi.org/10.1166/sl.2020.4207.

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Tactile sensors become an essential part of many applications in our life. Integrating tactile sensors with surgical tools used in MIS is significant to compensate for the shortage of touch feeling of soft tissues and organs comparing with traditional surgeries. This paper presents a detailed design of a micro tactile sensor for measuring the stiffness of soft tissue with an irregular surface. The sensor consists of five cantilever springs with different stiffness. A spring in the middle has a relatively low stiffness surrounded by 4 springs have relatively equal high stiffness to compensate f

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Ruggiero, Federica, Alessandro Borghi, Mirko Bevini, et al. "Soft tissue prediction in orthognathic surgery: Improving accuracy by means of anatomical details." PLOS ONE 18, no. 11 (2023): e0294640. http://dx.doi.org/10.1371/journal.pone.0294640.

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Three-dimensional virtual simulation of orthognathic surgery is now a well-established method in maxillo-facial surgery. The commercial software packages are still burdened by a consistent imprecision on soft tissue predictions. In this study, the authors produced an anatomically detailed patient specific numerical model for simulation of soft tissue changes in orthognathic surgery. Eight patients were prospectively enrolled. Each patient underwent CBCT and planar x-rays prior to surgery and in addition received an MRI scan. Postoperative soft-tissue change was simulated using Finite Element M

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Cheng, Qiangqiang, Peter X. Liu, Pinhua Lai, Shaoping Xu, and Yanni Zou. "A Novel Haptic Interactive Approach to Simulation of Surgery Cutting Based on Mesh and Meshless Models." Journal of Healthcare Engineering 2018 (2018): 1–16. http://dx.doi.org/10.1155/2018/9204949.

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In the present work, the majority of implemented virtual surgery simulation systems have been based on either a mesh or meshless strategy with regard to soft tissue modelling. To take full advantage of the mesh and meshless models, a novel coupled soft tissue cutting model is proposed. Specifically, the reconstructed virtual soft tissue consists of two essential components. One is associated with surface mesh that is convenient for surface rendering and the other with internal meshless point elements that is used to calculate the force feedback during cutting. To combine two components in a se

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Wu, Longyan, Jun Zhu, Jun Zheng, et al. "A novel dynamic mechanical analysis device to measure the in-vivo material properties of plantar soft tissue and primary finite elementary analysis results." Journal of Physics: Conference Series 2313, no. 1 (2022): 012029. http://dx.doi.org/10.1088/1742-6596/2313/1/012029.

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Abstract We have designed a series of dynamic mechanical analysis (DMA)-like device to directly measure the material properties of living human plantar soft tissue. Various mechanical tests of plantar soft tissue such as vertical, horizontal shear and torsion can be carried out on the newly invented instruments, and periodic strain-stress outputs are obtained to analyse the viscoelasticity of the tissue. Pioneering finite element analysis has been done by coupling the machine and human foot FE model from different simulation environments, and the simulation tests show good engineering verifica

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Bao, YiDong, and DongMei Wu. "Real-time cutting simulation in virtual reality systems based on the measurement of porcine organs." SIMULATION 93, no. 12 (2017): 1073–85. http://dx.doi.org/10.1177/0037549717726144.

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A virtual soft tissues cutting model consistent with the organ specificity of real soft tissues was established in this paper, which was applied to the virtual operation training system. A measurement platform of soft tissue organ was designed and built, and the stress–strain and stress–relaxation data of pig liver and kidney were experimentally measured. Then, using the viscoelasticity mathematical formula, an improved virtual cutting model of the meshless classified balls-filling was constructed through VC++ and OpenGL. The cutting performance of the virtual soft tissues was further increase

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Tang, Wen, and Tao Ruan Wan. "Constraint-Based Soft Tissue Simulation for Virtual Surgical Training." IEEE Transactions on Biomedical Engineering 61, no. 11 (2014): 2698–706. http://dx.doi.org/10.1109/tbme.2014.2326009.

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Jin, Xia, Grand Roman Joldes, Karol Miller, King H. Yang, and Adam Wittek. "Meshless algorithm for soft tissue cutting in surgical simulation." Computer Methods in Biomechanics and Biomedical Engineering 17, no. 7 (2012): 800–811. http://dx.doi.org/10.1080/10255842.2012.716829.

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43

Varslot, T., and G. Taraldsen. "Computer simulation of forward wave propagation in soft tissue." IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control 52, no. 9 (2005): 1473–82. http://dx.doi.org/10.1109/tuffc.2005.1516019.

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Székely, G., Ch Brechbühler, R. Hutter, A. Rhomberg, N. Ironmonger, and P. Schmid. "Modelling of soft tissue deformation for laparoscopic surgery simulation." Medical Image Analysis 4, no. 1 (2000): 57–66. http://dx.doi.org/10.1016/s1361-8415(00)00002-5.

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Kerdok, Amy E., Stephane M. Cotin, Mark P. Ottensmeyer, Anna M. Galea, Robert D. Howe, and Steven L. Dawson. "Truth cube: Establishing physical standards for soft tissue simulation." Medical Image Analysis 7, no. 3 (2003): 283–91. http://dx.doi.org/10.1016/s1361-8415(03)00008-2.

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Zhong, Yongmin, Bijan Shirinzadeh, Julian Smith, and Chengfan Gu. "Thermal–Mechanical-Based Soft Tissue Deformation for Surgery Simulation." Advanced Robotics 24, no. 12 (2010): 1719–39. http://dx.doi.org/10.1163/016918610x522531.

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Roth, S. H., Markus H. Gross, Silvio Turello, and Friedrich R. Carls. "A Bernstein-Bézier Based Approach to Soft Tissue Simulation." Computer Graphics Forum 17, no. 3 (1998): 285–94. http://dx.doi.org/10.1111/1467-8659.00275.

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Paloc, Celine, Alessandro Faraci, and Fernando Bello. "Online Remeshing for Soft Tissue Simulation in Surgical Training." IEEE Computer Graphics and Applications 26, no. 6 (2006): 24–34. http://dx.doi.org/10.1109/mcg.2006.134.

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Zhong, Yongmin, Bijan Shirinzadeh, Julian Smith, and Chengfan Gu. "An electromechanical based deformable model for soft tissue simulation." Artificial Intelligence in Medicine 47, no. 3 (2009): 275–88. http://dx.doi.org/10.1016/j.artmed.2009.08.003.

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Nguyen, Tan-Nhu, Marie-Christine Ho Ba Tho, and Tien-Tuan Dao. "A Systematic Review of Real-Time Medical Simulations with Soft-Tissue Deformation: Computational Approaches, Interaction Devices, System Architectures, and Clinical Validations." Applied Bionics and Biomechanics 2020 (February 20, 2020): 1–30. http://dx.doi.org/10.1155/2020/5039329.

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Abstract:

Simulating deformations of soft tissues is a complex engineering task, and it is even more difficult when facing the constraint between computation speed and system accuracy. However, literature lacks of a holistic review of all necessary aspects (computational approaches, interaction devices, system architectures, and clinical validations) for developing an effective system of soft-tissue simulations. This paper summarizes and analyses recent achievements of resolving these issues to estimate general trends and weakness for future developments. A systematic review process was conducted using

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