Relevant bibliographies by topics / Soft tissue simulation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Soft tissue simulation'

Author: Grafiati
Published: 11 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Soft tissue simulation"

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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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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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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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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Dissertations / Theses on the topic "Soft tissue simulation"

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Golec, Karolina. "Hybrid 3D Mass Spring System for Soft Tissue Simulation." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSE1004/document.

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La nécessité de simulations de tissus mous, tels que les organes internes, se pose avec le progrès des domaines scientifiques et médicaux. Le but de ma thèse est de développer un nouveau modèle générique, topologique et physique, pour simuler les organes humains. Un tel modèle doit être facile à utiliser, doit pouvoir effectuer des simulations en temps réel avec un niveau de précision permettant l'utilisation à des fins médicales. Cette thèse explore de nouvelles méthodes de simulation et propose des améliorations pour la modélisation de corps déformables. Les méthodes proposées visent à pouvo
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Duysak, Alpaslan. "Efficient techniques for soft tissue modeling and simulation." Thesis, Bournemouth University, 2004. http://eprints.bournemouth.ac.uk/446/.

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Performing realistic deformation simulations in real time is a challenging problem in computer graphics. Among numerous proposed methods including Finite Element Modeling and ChainMail, we have implemented a mass spring system because of its acceptable accuracy and speed. Mass spring systems have, however, some drawbacks such as, the determination of simulation coefficients with their iterative nature. Given the correct parameters, mass spring systems can accurately simulate tissue deformations but choosing parameters that capture nonlinear deformation behavior is extremely difficult. Since mo
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Schill, Markus A. "Biomechanical soft tissue modeling techniques, implementation and application /." [S.l. : s.n.], 2002. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB10605020.

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Comas, Olivier. "Real-time Soft Tissue Modelling on GPU for Medical Simulation." Phd thesis, Université des Sciences et Technologie de Lille - Lille I, 2010. http://tel.archives-ouvertes.fr/tel-00561299.

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Modéliser la déformation de structures anatomiques en temps réel est un problème crucial en simulation médicale. En raison des grandes différences existantes dans leur forme et leur constitution, un modèle unique est insuffisant face à la variété des comportements mécaniques. Par conséquent, nous avons identifié deux principaux types de structures: les organes pleins (cerveau, foie, prostate etc.) et les organes creux (colon, vaisseaux sanguins, estomac etc.). Notre réponse à cette problématique est double. Notre première contribution est une implémentation GPU d'un modèle éléments finis qui e
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Teschner, Matthias. "Direct computation of soft tissue deformation in craniofacial surgery simulation /." Aachen : Shaker, 2001. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=009236357&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Chen, Zhuo-Wei. "Simulation numérique du comportement dynamique des organes pelviens." Thesis, Evry-Val d'Essonne, 2013. http://www.theses.fr/2013EVRY0009/document.

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Le prolapsus des organes pelviens (vessie, rectum, utérus, vagin) est un problème de santé qui touche de plus en plus de femmes. Ce trouble, dont la fréquence augmente avec le vieillissement de la population, altère inévitablement la qualité de vie des malades. Pour autant, les causes de cette pathologie sont mal connues et les pratiques chirurgicales demeurent mal évaluées. La réalisation d’un simulateur du comportement dynamique des organes pelviens permettant au chirurgien d’estimer l’impact fonctionnel de son geste avant sa réalisation est donc un besoin identifié. Ce travail concerne ains
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Lu, Yongtao. "Soft tissue modelling and facial movement simulation using the finite element method." Thesis, Cardiff University, 2010. http://orca.cf.ac.uk/54369/.

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This thesis presents a framework for soft tissue modelling, facial surgery simulation, and facial movement synthesis based on the volumetric finite element method. Assessment of facial appearance pre- and post-surgery is of major concern for both patients and clinicians. Pre-surgical planning is a prerequisite for successful surgical procedures and outcomes. Early computer-assisted facial models have been geometrically based. They are computationally efficient, but cannot give an accurate prediction for facial surgery simulation. Therefore, in this thesis, the emphasis is placed on physically-
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Faraci, Alessandro. "A multiresolution nonlinear finite element approach to real-time simulation of soft tissue deformation with haptic feedback." Thesis, Imperial College London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.430145.

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Nilsson, Linus. "Real-time simulation of diaphragm displacement during physiological and mechanical ventilation." Thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-202329.

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This thesis presents a tunable 3D real-time interactive simulator of the geometrical displacement of the thoracic diaphragm during physiological and mechanical ventilation. Particular attention is placed on capturing the heterogeneous tissue composition while maintaining computational efficiency and accuracy. The long term goal is to establish an accurate theoretical model to complement the experimental and clinical studies of the side effects associated with mechanical ventilation and to overcome the ethical difficulties of performing time resolved studies on human patients. The deformations
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Visconti, Maria Augusta Portella Guedes 1985. "Validity of water and acrylic as soft tissue simulation materials in an in vitro study using cone beam computed tomography." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/290177.

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Orientador: Francisco Haiter Neto<br>Texto em português e inglês<br>Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Odontologia de Piracicaba<br>Made available in DSpace on 2018-08-24T17:16:38Z (GMT). No. of bitstreams: 1 Visconti_MariaAugustaPortellaGuedes_D.pdf: 2357684 bytes, checksum: ed042e1f5da51d27eae5691a052e4cc3 (MD5) Previous issue date: 2014<br>Resumo: O presente estudo propôs-se validar os materiais água e acrílico como simuladores de tecidos moles em um estudo in vitro realizado com tomografia computadorizada de feixe cônico (TCFC). Para isso foram utilizadas
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Books on the topic "Soft tissue simulation"

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Ayache, Nicholas, and Hervé Delingette, eds. Surgery Simulation and Soft Tissue Modeling. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45015-7.

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Maurel, Walter, Daniel Thalmann, Yin Wu, and Nadia Magnenat Thalmann. Biomechanical Models for Soft Tissue Simulation. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03589-4.

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Nicholas, Ayache, and Delingette Hervé, eds. Surgery simulation and soft tissue modeling. Springer, 2003.

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1969-, Maurel Walter, ed. Biomechanical models for soft tissue simulation. Springer-Verlag, 1998.

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Duysak, Alpaslan. Efficient techniques for soft tissue modeling and simulation. Bournemouth University, 2004.

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Payan, Yohan. Soft tissue biomechanical modeling for computer assisted surgery. Springer, 2012.

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Maurel, Walter. Biomechanical Models for Soft Tissue Simulation. Springer, 2014.

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Thalmann, Daniel, Walter Maurel, Yin Wu, and Nadia Magnenat Thalmann. Biomechanical Models for Soft Tissue Simulation. Springer London, Limited, 2013.

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Thalmann, Daniel, Walter Maurel, Yin Wu, and Nadia Magnenat Thalmann. Biomechanical Models for Soft Tissue Simulation (ESPRIT Basic Research Series). Springer, 2003.

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Payan, Yohan. Soft Tissue Biomechanical Modeling for Computer Assisted Surgery. Springer, 2014.

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Book chapters on the topic "Soft tissue simulation"

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Maurel, Walter, Daniel Thalmann, Yin Wu, and Nadia Magnenat Thalmann. "Soft Tissue Physiology." In Biomechanical Models for Soft Tissue Simulation. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03589-4_1.

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Paloc, Céline, Alessandro Faraci, and Fernando Bello. "Local Mesh Adaptation for Soft Tissue Simulation." In Biomedical Simulation. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11790273_23.

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Maciel, Anderson, Ronan Boulic, and Daniel Thalmann. "Deformable Tissue Parameterized by Properties of Real Biological Tissue." In Surgery Simulation and Soft Tissue Modeling. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45015-7_8.

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Roose, Liesbet, Wim De Maerteleire, Wouter Mollemans, Frederik Maes, and Paul Suetens. "Simulation of Soft-Tissue Deformations for Breast Augmentation Planning." In Biomedical Simulation. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11790273_22.

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Hu, Tie, and Jaydev P. Desai. "Characterization of Soft-Tissue Material Properties: Large Deformation Analysis." In Medical Simulation. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25968-8_4.

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Liu, Yi, Amy E. Kerdok, and Robert D. Howe. "A Nonlinear Finite Element Model of Soft Tissue Indentation." In Medical Simulation. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25968-8_8.

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Castañeda, Miguel A. Padilla, and Fernando Arámbula Cosío. "Soft Tissue Resection for Prostatectomy Simulation." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30136-3_70.

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Kim, Sang-Youn, Jinah Park, and Dong-Soo Kwon. "Area-Contact Haptic Simulation." In Surgery Simulation and Soft Tissue Modeling. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45015-7_11.

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Balaniuk, Remis, and Kenneth Salisbury. "Soft-Tissue Simulation Using the Radial Elements Method." In Surgery Simulation and Soft Tissue Modeling. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45015-7_5.

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Schiavone, Patrick, Emmanuel Promayon, and Yohan Payan. "LASTIC: A Light Aspiration Device for in vivo Soft TIssue Characterization." In Biomedical Simulation. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11615-5_1.

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Conference papers on the topic "Soft tissue simulation"

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Wu, Hao, Han Yang, and Jinwu Wang. "Advancing Virtual Surgery with Hybrid Soft Tissue Simulation Models." In 2024 World Rehabilitation Robot Convention (WRRC). IEEE, 2024. http://dx.doi.org/10.1109/wrrc62201.2024.10696799.

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Zhao, Xiaodong, Baoxiang Shan, and Assimina A. Pelegri. "Integrated System for Soft Tissue Dynamic Simulation." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-40680.

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An integrated system is built to model and simulate the dynamic response of soft tissues. The mathematical formulation employs finite element and model order reduction approaches to develop a state space model for soft tissues that allows for time-efficient numerical analysis. The stimulus device and signal processing routines are built in Matlab/Simulink and then integrated with the finite element state space model. This integrated system facilitates expeditious numerical evaluation of different soft tissue models subjected to dynamic excitation. It further elucidates the effect of different
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Xuemei Liu and Lei Mao. "Visual simulation of soft tissue deformation." In 2010 International Conference On Computer and Communication Technologies in Agriculture Engineering (CCTAE). IEEE, 2010. http://dx.doi.org/10.1109/cctae.2010.5544353.

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Hui, Zhao, and Wang Dang-xiao. "Soft tissue simulation with bimanual force feedback." In 2010 International Conference on Audio, Language and Image Processing (ICALIP). IEEE, 2010. http://dx.doi.org/10.1109/icalip.2010.5685189.

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Hariri, Alireza, and Jean W. Zu. "Design of a Tissue Resonator Indenter Device for Measurement of Soft Tissue Viscoelastic Properties Using Parametric Identification." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87786.

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The design of a new device called Tissue Resonator Indenter Device (TRID) for measuring soft tissue viscoelastic properties is presented. The two degrees-of-freedom device works based on mechanical vibration principles. When TRID comes into contact with a soft tissue, it can identify the tissue’s viscoelastic properties through the change of the device’s natural frequencies and damping ratios. In this paper, the deign of TRID is presented assuming Kelvin model for tissues. By working in the linear viscoelastic domain, TRID is designed to identify tissue properties in the range of 0–100 Hz. Ass
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Johansson, H. "Application-specific Inverse Identification for Soft Tissue Biomechanics." In 10th International Conference on Adaptative Modeling and Simulation. CIMNE, 2021. http://dx.doi.org/10.23967/admos.2021.021.

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Ahn, Bummo, and Jung Kim. "An Efficient Soft Tissue Characterization Method for Haptic Rendering of Soft Tissue Deformation in Medical Simulation." In 2007 Frontiers in the Convergence of Bioscience and Information Technologies. IEEE, 2007. http://dx.doi.org/10.1109/fbit.2007.97.

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Sadraei, Ehsan, Mohamad H. Moazzen, Majid M. Moghaddam, and Faeze Sayad Sijani. "Real-time haptic simulation of soft tissue deformation." In 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM). IEEE, 2014. http://dx.doi.org/10.1109/icrom.2014.6990876.

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Oliveira, Ana C. M. T. G., Romero Tori, Wyllian Brito, Jessica dos Santos, Helton H. Biscaro, and Fatima L. S. Nunes. "Simulation of soft tissue deformation: A new approach." In 2013 IEEE 26th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2013. http://dx.doi.org/10.1109/cbms.2013.6627758.

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Yu, Tian, and Minhua Zheng. "Soft Tissue Cutting Simulation Based on Meshless Method." In 2018 IEEE International Conference on Information and Automation (ICIA). IEEE, 2018. http://dx.doi.org/10.1109/icinfa.2018.8812408.

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Reports on the topic "Soft tissue simulation"

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Vesely, Ivan. Advanced Soft Tissue Modeling for Surgical Simulation and Telemedicine. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada455112.

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