Relevant bibliographies by topics / Soft tissue simulation

Academic literature on the topic 'Soft tissue simulation'

Author: Grafiati
Published: 11 March 2023

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Journal articles on the topic "Soft tissue simulation"

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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 (December 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. It establishes formulations for specific heat capacity, thermal conductivity and stress–strain relationships of soft tissues, all of which are temperature-dependent parameters. Simulations and comparison analyses are conducted, showing a different thermal-induced stress distribution of lower magnitudes when considering temperature-dependent thermal parameters of soft tissues.
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Omar, Nadzeri, Yongmin Zhong, Julian Smith, and Chengfan Gu. "Local deformation for soft tissue simulation." Bioengineered 7, no. 5 (June 10, 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 (December 2012): 767–68. http://dx.doi.org/10.1002/pamm.201210372.

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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 performed with 0% to 30% compression of the breast tissue model. The shear modulus of the tumor exhibited noticeably high nonlinearity compared to soft background tissue above 10% overall applied compression. As a result, the elastic modulus contrast of the tumor to the surrounding tissue was increased from 0.46 at 0% compression to 1.45 at 30% compression.
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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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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 (June 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. They lack the haptic and kinesthetic feedback of real tissue. These simulations fail to recreate the fidelity of soft tissues, do not foster the ability to accurately see surgical planes, do not accurately mimic the act of dissecting surgical planes, do not allow for complex surgical procedures, and do not provide accurate experience to learn tissue handling and suturing. Despite their poor performance, these plastic and virtual trainers are extremely costly to purchase, maintain, and keep up to date - with prices starting at $700 for basic plastic training boxes to thousands of dollars for virtual simulation. Also, there are additional costs of maintenance and software curriculum. Despite the cost of software, virtual simulators do not include a simulation for every surgery. Our aim was to create a life-like surgical simulation as close to real world as possible that allows trainees to learn how to see and dissect surgical planes, learn how soft tissues move, and learn the dynamics of soft tissue manipulation. We created a laparoscopic simulator using porcine tissues for gallbladder removal, acid reflux surgery, and surgery to treat swallowing difficulties (cholecystectomy, Nissen fundoplication, and Heller myotomy, respectively). Second year general surgery residents were able to practice these procedures on real tissues, enabling them to learn the steps of each procedure, increase manual dexterity, improve use of laparoscopic equipment, all while maintaining life-like haptic, soft-tissue feedback and enabling them to develop the ability to see real surgical planes. Methods The abdomen was recreated by purchasing intact porcine liver, gallbladder, (Cholecystectomy simulation) and intact esophagus, stomach, and diaphragm (Nissen and Heller simulation) from a packing supplier. Each organ system was placed into a laparoscopic trainer box with the ability to re-create laparoscopic ports. Surgical residents were then able to perform the procedures using real laparoscopic instruments, laparoscopic camera/video imaging, and real-time electrocautery. The simulation included all critical steps of each procedure such as obtaining the critical view of safety and removing the gallbladder from the liver bed (cholecystectomy), wrapping the stomach around the esophagus and laparoscopic suturing (Nissen fundoplication), and dissecting the muscular portion of the esophageal wall (Heller myotomy). Because these porcine tissues were readily available, several stations were set-up to teach multiple residents during each session (10-12 residents / session). Discussion Surgeons develop haptic perception of soft tissues by cutaneous or tactile feedback and kinesthetic feedback (Okamura, 2009). Kinesthetic feedback is the force and pressure transmitted by the soft tissues along the shaft of the laparoscopic instruments (Okamura, 2009). This soft tissue simulation re-creates the ability to experience what soft tissue feedback feels like, outside a normal operative environment. Real tissue learning allows trainees to learn how to see surgical planes, learn how soft tissues feel and move, develop proficiency in surgical dissection, and learn how to suture laparoscopically. This is the only model that recreates the movement of soft tissues and visualization of dissection planes outside the operative environment. Because this model utilizes the laparoscopic instruments used in the operating room, residents also develop familiarity with laparoscopic instruments, thus, flattening another learning curve. A literature review found that this is the only real tissue simulation being performed for foregut procedures used specifically for resident training. By building a realistic, anatomical model with inherent accurate soft tissue surgical planes, surgical trainees can have a more realistic surgical experience and develop skills in a safe, low pressure environment without sacrificing the hepatic learning and surgical visualization that is critical to performing safe laparoscopic surgery. All residents that participated in the stimulation reported positive feedback and felt that is contributed to their surgical education.
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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 (May 2016): 280–89. http://dx.doi.org/10.1002/cav.1691.

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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 (April 26, 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 of experiments with device prototype are used for approval of adequacy of mathematical modelling in case of large deformations. Simulation results can be used to create soft tissue databases, which will be required to determine the local stiffness of soft tissues by the sensor. 2D model proved to be more efficient for the chosen range of values of local stiffness of soft tissues.
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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 (December 22, 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 into silicone gel samples. We also present a simulation of needle insertion into the brain demonstrating the method’s insensitivity to assumed mechanical properties of tissue.
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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 (September 22, 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 propose a novel approach to alleviate these issues by replacing indirect volume preservation using Poisson's ratios with direct enforcement of zonal volume constraints, while controlling fine-scale volumetric deformation through a cell-wise compression penalty. To increase realism, we propose an epidermis model to mimic the dramatically higher surface stiffness on real skinned bodies. We demonstrate that our method produces stable realistic deformations with precise volume preservation but without locking artifacts. Due to the volume preservation not being tied to mesh discretization, our method also allows a resolution consistent simulation of incompressible materials. Our method improves the stability of the standard neo-Hookean model and the general compression recovery in the Stable neo-Hookean model.
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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 pouvoir effectuer des simulations rapides, robustes et fournissant des résultats physiquement précis. L'intérêt principal de nos solutions réside dans la simulation de tissus mous élastiques a petites et grandes déformations à des fins médicales. Nous montrons que pour les méthodes existantes, la précision pour simuler librement des corps déformables ne va pas de pair avec la performance en temps de calcul. De plus, pour atteindre l'objectif de simulation rapide, de nombreuses approches déplacent certains calculs dans une étape de pré-traitement, ce qui entraîne l'impossibilité d'effectuer des opérations de modification topologiques au cours de la simulation comme la découpe ou le raffinement. Dans cette thèse, le cadre utilisé pour les simulations s'appelle TopoSim. Il est conçu pour simuler des matériaux à l'aide de systèmes masses-ressorts (MSS) avec des paramètres d'entrée spécifiques. En utilisant un MSS, qui est connu pour sa simplicité et sa capacité à effectuer des simulations temps réel, nous présentons plusieurs améliorations basé physiques pour contrôler les fonctionnalités globales du MSS qui jouent un rôle clé dans la simulation de tissus réels. La première partie de ce travail de thèse vise à reproduire une expérience réelle de simulation physique qui a étudié le comportement du tissu porcin à l'aide d'un rhéomètre rotatif. Son objectif était de modéliser un corps viscoélastique non linéaire. A partir de l'ensemble des données acquises, les auteurs de l'expérience ont dérivé une loi de comportement visco-élastique qui a ensuite été utilisée afin de la comparer avec nos résultats de simulation. Nous définissons une formulation des forces viscoélastiques non linéaires inspirée de la loi de comportement physique. La force elle-même introduit une non linéarité dans le système car elle dépend fortement de l'amplitude de l'allongement du ressort et de trois paramètres spécifiques à chaque type de tissu. La seconde partie de la thèse présente notre travail sur les forces de correction de volume permettant de modéliser correctement les changements volumétriques dans un MSS. Ces forces assurent un comportement isotrope des solides élastiques et un comportement correct du volume quel que soit la valeur du coefficient de Poisson utilisé. La méthode nécessite de résoudre deux problèmes: l'instabilité provoquant des plis et les contraintes de Cauchy. Nos solutions à ces limitations impliquent deux étapes. La première consiste à utiliser trois types de ressorts dans un maillage entièrement hexaédrique: les arêtes, les faces diagonales et les diagonales internes. Les raideurs des ressorts dans le système ont été formulées pour obéir aux lois mécaniques de base. La deuxième étape consiste à ajouter des forces de correction linéaires calculées en fonction du changement de volume et des paramètres mécaniques du tissu simulé, à savoir le coefficient de Poisson et le module de Young [etc…]
The need for simulations of soft tissues, like internal organs, arises with the progress of the scientific and medical environments. The goal of my PhD is to develop a novel generic topological and physical model to simulate human organs. Such a model shall be easy to use, perform the simulations in the real time and which accuracy will allow usage for the medical purposes.This thesis explores novel simulation methods and improvement approaches for modeling deformable bodies. The methods aim at fast and robust simulations with physically accurate results. The main interest lies in simulating elastic soft tissues at small and large strains for medical purposes. We show however, that in the existing methods the accuracyto freely simulate deformable bodies and the real-time performance do not go hand in hand. Additionally, to reach the goal of simulating fast, many of the approaches move the necessary calculations to pre-computational part of the simulation, which results in inability to perform topological operations like cutting or refining.The framework used for simulations in this thesis is designed to simulate materials using Mass Spring Systems (MSS) with particular input parameters. Using Mass-Spring System, which is known for its simplicity and ability to perform fast simulations, we present several physically-based improvements to control global features of MSS which play the key role in simulation of real bodies
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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 most of the applications require a large number of elements i. e. points and springs in the modeling process it is extremely difficult to reach realtime performance with an iterative method. We have developed a new parameter identification method based on neural networks. The structure of the mass spring system is modified and neural networks are integrated into this structure. The input space consists of changes in spring lengths and velocities while a "teacher" signal is chosen as the total spring force, which is expressed in terms of positional changes and applied external forces. Neural networks are trained to learn nonlinear tissue characteristics represented by spring stiffness and damping in the mass spring algorithm. The learning algorithm is further enhanced by an adaptive learning rate, developed particularly for mass spring systems. In order to avoid the iterative approach in deformation simulations we have developed a new deformation algorithm. This algorithm defines the relationships between points and springs and specifies a set of rules on spring movements and deformations. These rules result in a deformation surface, which is called the search space. The deformation algorithm then finds the deformed points and springs in the search space with the help of the defined rules. The algorithm also sets rules on each element i. e. triangle or tetrahedron so that they do not pass through each other. The new algorithm is considerably faster than the original mass spring systems algorithm and provides an opportunity for various deformation applications. We have used mass spring systems and the developed method in the simulation of craniofacial surgery. For this purpose, a patient-specific head model was generated from MRI medical data by applying medical image processing tools such as, filtering, the segmentation and polygonal representation of such model is obtained using a surface generation algorithm. Prism volume elements are generated between the skin and bone surfaces so that different tissue layers are included to the head model. Both methods produce plausible results verified by surgeons.
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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 est non-linéaire, anisotropique et viscoélastique pour les structures pleines. Notre seconde contribution est un environnement pour modéliser en temps réel les structures fines via un modèle parallèlisable et co-rotationnel utilisant des éléments coques et une approche pour mailler une surface complexe avec des éléments coques courbes. Bien que les deux modèles de tissus soient basés sur la mécanique continue pour une meilleure précision, ils sont tous les deux capables de simuler la déformation d'organes en temps réel. Enfin, leur implémentation dans l'environnement open source SOFA permettra la diffusion de ces deux modèles afin de participer à l'amélioration du réalisme des simulateurs médicaux.
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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 ainsi le développement, par la méthode des éléments finis, d’un modèle numérique du mouvement des organes pelviens et de leurs interactions. Un modèle est construit à partir d’une segmentation de l’IRM des patiente, permettant de générer la géométrie des organes pelviens. Des lois hyperélastiques sont ensuite adoptées pour modéliser le comportement mécanique des organes. Des résultats qualitatifs sont obtenus, permettant de comprendre les causes de certaines formes de prolapsus et d’estimer l’effet virtuel des interactions entre les organes
Pelvic organ prolapse is a health problem that occurs only in women and becomes more common. These disorders whose frequency increases with the aging of the population affect the patients’ quality of life. However, the causes of these diseases are poorly understood and the surgical practices remain poorly evaluated. The realization of a simulator will allow surgeon to estimate the functional impact of his actions before implementation, to perform the surgery in a more controlled and reliable way. This work concerns the development of a numerical model of pelvic organs’ movement and their interactions based on the finite element methods. A first model is constructed from patients MRI images, allowing the generation of the organ geometries. Hyperelastic modeling of the organs behaviors were considered. Qualitative results could help to understand the reasons for the prolapse and to estimate the potential effect of organs interactions
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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-based methods, especially the finite element technique. To achieve realistic surgery simulation, soft tissue modelling is of crucial importance. Thus, in this thesis, considerable effort has been directed to develop constitutive equations for facial skeletal muscles. The skeletal muscle model subsequently developed is able to capture the complex mechanical properties of skeletal muscle, which are active, quasi-incompressible, fibre-reinforced and hyperelastic. In addition, to improve the characterisation of in-vivo muscle behaviour, a technique has been developed to visualise the internal fibre arrangement of skeletal muscle using the FEM-NURBS method, which is the combination of the finite element method and the non-uniform rational B-spline solid mathematical representation. Another principal contribution made in this thesis is the three-dimensional finite element facial model, which can be used for the simulations of facial surgery and facial movement. The procedure of one cranio-facial surgery is simulated by using this facial model and the numerical predictions show a good agreement with the patient post-surgical data. In addition, it would be very helpful to also simulate the facial movement and facial expressions. In this thesis, two facial expressions (smile and disgust) and the mouth opening are simulated to assess the post-surgical appearance and test the muscle-driven facial movement simulation method.
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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 are modelled using a commercial 3D model and a mass-spring model together with distance constraints and Verlet integration. The simulator is easily adjusted in real-time to many different cases of ventilation and validated through inspection and comparison with existing models. More research is needed to validate the model using patient specific data, as well as extending the model to include additional physiological and pathophysiological components. Long term goals includes considering the microscopic aspects of cellular mechanics to capture the underlying causes of ventilator-induced diaphragmatic dysfunction.
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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
Texto em português e inglês
Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Odontologia de Piracicaba
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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 três cabeças humanas, com tecidos moles intactos, para determinação do padrão-ouro. Essas cabeças foram submetidas a exames de TCFC e posteriormente descarnadas e tomografadas novamente, agora com diferentes tipos de simuladores de tecido mole, seguindo o mesmo protocolo de aquisição. Para simulação dos tecidos moles foram confeccionadas três caixas de acrílico com diferentes dimensões e espessuras. Estas caixas foram utilizadas isoladamente, conjugadas entre si e em combinação com a água, totalizando dez diferentes tipos de simuladores. Um único avaliador experiente realizou as mensurações em quatro regiões de interesse para maxila e mandíbula, incluindo dentes e ossos alveolares. As regiões de interesse consistiram em áreas quadrangulares, nas quais foram determinados todos os valores de cinzas expressos em pixels. Os resultados mostraram que tanto a região avaliada quanto os tipos de simuladores testados interferiram diretamente nos valores de pixels obtidos. As caixas de acrílico de 0,5 e 1,5 cm de espessura foram os simuladores que mais se assemelharam ao padrão-ouro, não apresentando diferença significativa. No entanto, essa similaridade apenas foi observada para a maxila, limitada às regiões dos dentes e ossos alveolares anteriores. A simulação dos tecidos moles realizada apenas com o acrílico foi a que mais se aproximou dos tecidos moles humanos nas imagens de TCFC, apenas para maxila
Abstract: The aim of this study was to validate the materials water and acrylic as soft tissue simulators in an in vitro study conducted with cone beam computed tomography (CBCT). For this we used three human heads, with soft tissues intact, to determine the "gold standard". These heads were submitted to CBCT exams, and subsequently stripped and scanned again, this time with different types of soft tissue simulators, following the same acquisition protocol. For soft tissue simulation, three acrylic boxes of differing dimensions and thicknesses were prepared. These boxes were used separately, combined together, and in combination with water, totaling ten different types of simulators. A single experienced evaluator did measurements in four regions of interest for the maxilla and mandible, including teeth and alveolar bone. The regions of interest consisted of quadrangular areas, in which all gray values were determined, expressed in pixels. The results sowed both the region evaluated as well as the types of simulators tested directly affected the pixel values obtained. The acrylic boxes with 0.5 and 1.5 cm thickness were the simulators that more closely resembled the gold standard, presenting no significant difference. However, this similarity was observed only for the maxilla, limited to the anterior tooth and alveolar bone regions. The simulation of soft tissues done solely with acrylic was the one closest to human soft tissues in the CBCT images, only for maxilla
Doutorado
Radiologia Odontologica
Doutora em Radiologia Odontológica
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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. Berlin, Heidelberg: 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. Berlin, Heidelberg: 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. Berlin: Springer, 2003.

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4

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

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

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

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F, Nielsen Poul M., Miller Karol, and SpringerLink (Online service), eds. Computational Biomechanics for Medicine: Soft Tissues and the Musculoskeletal System. New York, NY: Springer Science+Business Media, LLC, 2011.

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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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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 (ESPRIT Basic Research Series). Springer, 2003.

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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, 1–23. Berlin, Heidelberg: 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, 206–14. Berlin, Heidelberg: 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, 74–87. Berlin, Heidelberg: 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, 197–205. Berlin, Heidelberg: 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, 28–37. Berlin, Heidelberg: 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, 67–76. Berlin, Heidelberg: 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, 568–76. Berlin, Heidelberg: 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, 108–20. Berlin, Heidelberg: 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, 48–58. Berlin, Heidelberg: 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, 1–10. Berlin, Heidelberg: 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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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 stimulus sources, as well as relative influences of different sources of uncertainty.
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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. Assuming Kelvin model for tissues, the current paper develops a method for determining unknown tissue parameters using input-output data from TRID. Moreover, it is proved that the TRID’s parameters as well as the Kelvin tissue model parameters are globally identifiable. A parametric identification method using the prediction error approach is proposed for identifying the unknown tissue parameters in a grey-box state-space model. The reliability and effectiveness of the method for measuring soft tissue’s viscoelastic properties is demonstrated through simulation in the presence of considerable input and output noises.
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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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Ying Wu, Jie, Adnan Munawar, Mathias Unberath, and Peter Kazanzides. "Learning Soft-Tissue Simulation from Models and Observation." In 2021 International Symposium on Medical Robotics (ISMR). IEEE, 2021. http://dx.doi.org/10.1109/ismr48346.2021.9661507.

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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. Fort Belvoir, VA: Defense Technical Information Center, July 2006. http://dx.doi.org/10.21236/ada455112.

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