Статті в журналах: "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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4

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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8

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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11

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 (August 20, 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 blunt dissection experiment results using agar.

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12

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

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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 (October 2019): 312–17. http://dx.doi.org/10.1097/sih.0000000000000382.

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

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15

Zhong, Yongmin, Bijan Shirinzadeh, Gursel Alici, and Julian Smith. "Soft tissue modelling through autowaves for surgery simulation." Medical & Biological Engineering & Computing 44, no. 9 (August 4, 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 (June 6, 2007): 661–69. http://dx.doi.org/10.1007/s11517-007-0205-y.

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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 (April 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-frequency and amplitude-modulated single-frequency methods for the inducing tissue motion. Simulations were computed in a homogeneous soft-tissue volume. The Rayleigh integral was used in the simulations of the ultrasound fields and the tissue displacements were computed using a finite-element method (FEM). The simulations showed that amplitude-modulated sonication using a single frequency produced the largest vibration amplitude of the target tissue. These simulations demonstrate that the properties of the tissue motion are highly dependent on the sonication method and that it is important to consider the full three-dimensional distribution of the ultrasound field for controlling the induction of tissue motion.

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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 (June 19, 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 simulations in computational mechanics. The constitutive model and the strain-based failure formulations were tested using simulations of simple shear and tensile mechanical tests. The model was then applied to investigate a scenario of a penetrating injury: a low-speed projectile was released through a myocardial material slab. Sensitivity studies were performed to establish the necessary grid resolution and time-step size. Results of the simple shear and tensile test simulations demonstrated the correct implementation of the constitutive model and the influence of both fiber family and matrix failure on predictions of overall tissue failure. The slab penetration simulations produced physically realistic wound tracts, exhibiting diameter increase from entrance to exit. Simulations examining the effect of bullet initial velocity showed that the anisotropy influenced the shape and size of the exit wound more at lower velocities. Furthermore, the size and taper of the wound cavity was smaller for the higher bullet velocity. It was concluded that these effects were due to the amount of momentum transfer. The results demonstrate the feasibility of using MPM and the associated failure model for large-scale numerical simulations of soft tissue failure.

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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 (October 13, 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 process and introduce asynchronous diffusion by using the mechanical energy of mass points to achieve realistic soft tissue deformation effects. In order to meet the requirement of real-time surgery simulation, we accelerate the soft tissue deformation by using OpenCL (Open Computing Language) and optimize the parallel computing process in several means. Experimental results have shown that the GPU-assisted energy asynchronous diffusion parallel computing model for soft tissue deformation simulation implements satisfactory effects on deformation in visual realistic and real-time quality.

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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 (June 14, 2013): 1176. http://dx.doi.org/10.1364/boe.4.001176.

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Farrell, Joyce, Zheng Lyu, Zhenyi Liu, Henryk Blasinski, Zhihao Xu, Jian Rong, Feng Xiao, and Brian Wandell. "Soft-prototyping imaging systems for oral cancer screening." Electronic Imaging 2020, no. 7 (January 26, 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 fluorophores may be diagnostic in identifying precancerous tissue.

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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 present the mechanical characteristics of soft tissue which greatly promotes the realism. Secondly, SPH has features of meshless technique and self-adaption, which supply higher precision than methods based on meshes for force feedback computation. Finally, a SPH method based on dynamic interaction area is proposed to improve the real time performance of simulation. The results reveal that SPH methodology is suitable for simulating soft tissue deformation and force feedback calculation, and SPH based on dynamic local interaction area has a higher computational efficiency significantly compared with usual SPH. Our algorithm has a bright prospect in the area of virtual surgery.

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23

Alcañiz, Patricia, Jesús Pérez, Alessandro Gutiérrez, Héctor Barreiro, Ángel Villalobos, David Miraut, Carlos Illana, Jorge Guiñales, and Miguel A. Otaduy. "Soft-Tissue Simulation for Computational Planning of Orthognathic Surgery." Journal of Personalized Medicine 11, no. 10 (September 29, 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 accurate. The proposed simulation methodology has been evaluated on a cohort of 10 patients of orthognathic surgery, comparing quantitatively simulation results to post-operative scans. The results suggest that the proposed simulation methods admit the use of coarse simulation meshes, with planning computation times of less than 10 seconds in most cases, and with clinically viable accuracy.

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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 (August 25, 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 months after surgery, a 3D face scan of every patient was performed and compared to the preoperative simulation. The quality of the soft-tissue simulation was validated with the help of a distance map. This distance map was calculated using the inter-surface distance algorithm between the preoperative simulation of the soft-tissue and the actual scan of the postoperative soft-tissue surface. The prediction of the cranial parts of the face (upper cheek, nose, upper lip) was more precise than the prediction of the lower areas (lower cheek, lower lip, chin). The percentage of correctly predicted soft-tissue for the face in total reached values from 69.4% to 96.0%. The VSP system IPS CaseDesigner® (KLS Martin Group; Tuttlingen, Germany) predicts the patient’s post-surgical soft-tissue accurately. Still, this simulation has to be seen as an approximation, especially for the lower part of the face, and continuous improvement of the underlying algorithm is needed for further development.

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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 maximum likelihood estimation maximization (MLEM) method. The scatter kernels are obtained by simulating the PMMA sheet on the Monte Carlo simulation (MCS) software. In the experiment, we used the QRM phantom to quantitatively compare with fan-beam CT (FBCT) data in terms of CT number values, contrast to noise ratio, cupping artifacts, and low contrast detectability. Moreover, the PH3 angiography phantom was also used to mimic human soft tissues in the brain. The reconstructed images with our proposed scatter correction show significant improvement on image quality. Thus the proposed scatter correction technique has high potential to detect soft tissues in the brain.

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Moreno-Guerra, Mario R., Oscar Martínez-Romero, Luis Manuel Palacios-Pineda, Daniel Olvera-Trejo, José A. Diaz-Elizondo, Eduardo Flores-Villalba, Jorge V. L. da Silva, Alex Elías-Zúñiga, and Ciro A. Rodriguez. "Soft Tissue Hybrid Model for Real-Time Simulations." Polymers 14, no. 7 (March 30, 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 states. To assess the accuracy achieved by the EESM, experimental data was collected from liver porcine samples via uniaxial loading and unloading tensile tests. Validation of the model through numerical predictions achieved a refresh rate of 31 fps (31.49 ms of computation time for each frame), achieving a coefficient of determination R2 from 93.23% to 99.94% when compared to experimental data. The proposed hybrid formulation to characterize soft tissue mechanical behavior is fast enough for real-time simulation and captures the soft material nonlinear virgin and stress-softened effects with high accuracy.

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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 (June 14, 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 of scan parameters and dose. The experimental study was complemented by simulations of dose distributions for microfocused X-ray beams in soft muscle tissue. As a simulation tool, the Monte Carlo software package EGSnrc was used that is widely used in radiation dosimetry research. Simulations also give additional guidance for a more careful planning of dose distribution in tissue.

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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 matrix was fabricated, and fibers made of a stiffer elastomer material were embedded within the matrix material to generate the test coupons. The coupons were tested on a mechanical testing machine, and the resulting stress-versus-stretch responses were studied. The fiber volume fraction (FVF), fiber spacing, and orientations were varied to estimate the changes in the mechanical responses. The mechanical behavior of the soft composites was characterized using hyperelastic material models such as Mooney-Rivlin’s, Humphrey’s, and Veronda-Westmann’s model and also compared with the anisotropic mechanical behavior of the human skin, pelvic tissues, and brain tissues. This work lays the foundation for the experimental modelling of tissue anisotropy, which combined with microscopic studies on tissues can lead to refinements in the simulation of localized fiber distribution and orientations, and enable the development of biofidelic anisotropic tissue phantom materials for various tissue engineering and testing applications.

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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 (April 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 mandibular incisor inclination and protrusion values. Next, using the treatment simulation module, the aforementioned values for the T0 cephalograms were manually entered to match the actual T1 values taking into account the T0-T1 differences. Paired sample t-test were applied to determine the difference between actual and treatment simulation measurements. Results: No significant differences were found for the anteroposterior location of the landmarks. Upper lip, soft tissue A point, soft tissue pogonion, and soft tissue B point measurements showed statistically significant difference between actual and treatment simulation in the vertical plane. Conclusion: Quick Ceph program was reliable in terms of reflecting the sagittal changes that would probably occur with treatment and growth. However, vertical positions of the upper lip, soft tissue pogonion, soft tissue A point, and soft tissue B point were statistically different from actual results.

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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 (November 2014): 2698–706. http://dx.doi.org/10.1109/tbme.2014.2326009.

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31

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 (September 14, 2012): 800–811. http://dx.doi.org/10.1080/10255842.2012.716829.

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32

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 (September 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 (March 2000): 57–66. http://dx.doi.org/10.1016/s1361-8415(00)00002-5.

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34

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 (September 2003): 283–91. http://dx.doi.org/10.1016/s1361-8415(03)00008-2.

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35

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

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36

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 (August 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 (November 2006): 24–34. http://dx.doi.org/10.1109/mcg.2006.134.

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38

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 (November 2009): 275–88. http://dx.doi.org/10.1016/j.artmed.2009.08.003.

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39

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 dynamic process of soft tissue deformation using ANSYS software. The two methods will be available for steering the flexible needle to hit the target and avoid the obstacles precisely in the robot-assisted needle insertion.

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40

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 (December 8, 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 is introduced which, in a simplified manner, models the observed loss of stiffness of these aging elastic components due to the history of straining as well as other physiological and environmental influences. Calculations have been performed for 30 years of exposure to gravitational forces. The deformations and stress distributions in the layers of the soft tissues are described. Overall, the feasibility of using constitutive relations which reflect the highly nonlinear elastic-viscoplastic behavior of facial soft tissues in finite element based three-dimensional mechanical analyses of the human face is demonstrated. Further developments of the program are discussed in relation to possible clinical applications. Although the proposed aging function produces physically reasonable long-term response, experimental data are not yet available for more quantitative validation.

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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 (March 1, 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 for the soft tissue contact error in the longitudinal and lateral directions. Sensor parameters are selected to ensure high sensitivity and linearity with taking into consideration the cross-talk effect among the sensor springs tips. A detailed design of the sensor structure in the microscale is conducted based on some constraints related to MEMS fabrication. A finite element analysis (FEA) of the sensor structure is conducted to evaluate sensor structure performance using CoventorWare software. Then, an FEA for the piezo-resistors, as a signal transduction method, is conducted which maps the sensor output to an electrical signal. The results prove that the sensor can differentiate among different soft-tissue stiffness within the selected range independent of the applied distance between the sensor and the tissue with an error below 3% even with inclination angle between the sensor and the tissue ±3°. Furthermore, a linear performance has been achieved between the soft-tissue stiffness and the sensor output.

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Hallahan, D. E., N. J. Vogelzang, K. M. Borow, D. G. Bostwick, and M. A. Simon. "Cardiac metastases from soft-tissue sarcomas." Journal of Clinical Oncology 4, no. 11 (November 1986): 1662–69. http://dx.doi.org/10.1200/jco.1986.4.11.1662.

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Cardiac metastases were present in 30 of 120 (25%) consecutive autopsies of patients with soft-tissue sarcoma (STS). Fifty percent of the patients had metastases to the myocardium, while 33% had pericardial metastases and 17% had both. Congestive heart failure was present in ten patients and was commonly caused by diffuse myocardial or restrictive pericardial metastases. Other signs and symptoms of cardiac involvement by STS included chest pain (three patients), arrhythmias (two), conduction block (two), simulation of an atrial myxoma (one), and sudden death (one). Echocardiography was used infrequently, but was diagnostic in 80% of cases in which it was used. We conclude that metastatic STS commonly involves the heart and produces cardiac symptoms.

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White, BF, D. D'Lima, AC Drueding, J. Cox, FJ Carignan, and SW Dean. "A Simulator Study of TKR Kinematics Using Modeled Soft-Tissue Constraint: Virtual Soft-Tissue Control for Knee Simulation." Journal of ASTM International 3, no. 8 (2006): 100251. http://dx.doi.org/10.1520/jai100251.

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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 (August 21, 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 increased by leveraging the improved cutting classification algorithm. Finally, the extrusion and cutting simulation was enabled through the force feedback device, and the accuracy and effectiveness of this cutting model were validated by a comparative study of the virtual soft tissues cutting model and the stress–strain and stress–relaxation data of pig liver and kidney.

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45

Fell, Cody, Trent L. Brooks-Richards, Maria A. Woodruff, and Mark C. Allenby. "Soft pneumatic actuators for mimicking multi-axial femoropopliteal artery mechanobiology." Biofabrication 14, no. 3 (April 20, 2022): 035005. http://dx.doi.org/10.1088/1758-5090/ac63ef.

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Abstract Tissue biomanufacturing aims to produce lab-grown stem cell grafts and biomimetic drug testing platforms but remains limited in its ability to recapitulate native tissue mechanics. The emerging field of soft robotics aims to emulate dynamic physiological locomotion, representing an ideal approach to recapitulate physiologically complex mechanical stimuli and enhance patient-specific tissue maturation. The kneecap’s femoropopliteal artery (FPA) represents a highly flexible tissue across multiple axes during blood flow, walking, standing, and crouching positions, and these complex biomechanics are implicated in the FPA’s frequent presentation of peripheral artery disease. We developed a soft pneumatically actuated (SPA) cell culture platform to investigate how patient-specific FPA mechanics affect lab-grown arterial tissues. Silicone hyperelastomers were screened for flexibility and biocompatibility, then additively manufactured into SPAs using a simulation-based design workflow to mimic normal and diseased FPA extensions in radial, angular, and longitudinal dimensions. SPA culture platforms were seeded with mesenchymal stem cells, connected to a pneumatic controller, and provided with 24 h multi-axial exercise schedules to demonstrate the effect of dynamic conditioning on cell alignment, collagen production, and muscle differentiation without additional growth factors. Soft robotic bioreactors are promising platforms for recapitulating patient-, disease-, and lifestyle-specific mechanobiology for understanding disease, treatment simulations, and lab-grown tissue grafts.

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Moshtaghi Yazdani, Navid. "Diagnosing Soft Tissue Sub-Surface Masses Using the XCS Classification System." Frontiers in Health Informatics 9, no. 1 (November 2, 2020): 49. http://dx.doi.org/10.30699/fhi.v9i1.239.

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Introduction: One of the most common types of cancer is breast cancer, which is considered as the second leading cause of death in women in Iran. Due to the fatality of this type of cancer, it is very important to diagnose the disease in the early stages and starting the treatment process. One of the methods to diagnose breast cancer is using mechanical arms (robot manipulator) to touch and measure the force in terms of displacement at the site of the breast touch by the robot. The hardness of the cancer tissue can affect the force diagram in terms of displacement, which can be used as a diagnostic method. The present study was performed to prepare a simulation model of breast soft tissue behavior considering subsurface masses. Then, a proposed classification system was designed to fit it.Material and Methods: In this section, first, the soft tissue behavior of the breast is simulated by considering sub-surface masses. The simulations are performed for a piece of tissue that is in the shape of a rectangular cube, as well as different dimensions of a spherical mass that is located at different depths and coordinates. Using simulation, various force-displacement diagrams have been obtained, based on which a data network.Results: The displacement force diagram for different modes is obtained using simulation. By giving the resulting diagrams to the trained system, the size and depth of the mass is determined. By comparing the obtained results with the initial model and the actual size and depth of the mass, a very good conformity is observed, which indicates the correct operation of the designed system and the performed simulation process.Conclusion: The proposed design system was used to diagnose the presence of tumors in tissue with sub-surface mass. The results show a high percentage of this method in diagnosis. However, the accuracy of this method can be greatly increased by increasing the amount of data given to the XCS system for training. On the other hand, instead of simulation data, test data on healthy and unhealthy people can be used for training.

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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 seamless way, virtual points are introduced. During the simulation of cutting, the Bezier curve is used to characterize smooth and vivid incision on the surface mesh. At the same time, the deformation of internal soft tissue caused by cutting operation can be treated as displacements of the internal point elements. Furthermore, we discussed and proved the stability and convergence of the proposed approach theoretically. The real biomechanical tests verified the validity of the introduced model. And the simulation experiments show that the proposed approach offers high computational efficiency and good visual effect, enabling cutting of soft tissue with high stability.

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Wu, Longyan, Jun Zhu, Jun Zheng, Xiang Geng, Xiaoyan He, Lisheng Tang, Ran Huang, and Xin Ma. "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 (July 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 verification of the device design, and consistent theoretical results of the material properties of plantar soft tissue as expected.

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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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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 the PRISMA protocol with three reliable scientific search engines (ScienceDirect, PubMed, and IEEE). Fifty-five relevant papers were finally selected and included into the review process, and a quality assessment procedure was also performed on them. The computational approaches were categorized into mesh, meshfree, and hybrid approaches. The interaction devices concerned about combination between virtual surgical instruments and force-feedback devices, 3D scanners, biomechanical sensors, human interface devices, 3D viewers, and 2D/3D optical cameras. System architectures were analysed based on the concepts of system execution schemes and system frameworks. In particular, system execution schemes included distribution-based, multithread-based, and multimodel-based executions. System frameworks are grouped into the input and output interaction frameworks, the graphic interaction frameworks, the modelling frameworks, and the hybrid frameworks. Clinical validation procedures are ordered as three levels: geometrical validation, model behavior validation, and user acceptability/safety validation. The present review paper provides useful information to characterize how real-time medical simulation systems with soft-tissue deformations have been developed. By clearly analysing advantages and drawbacks in each system development aspect, this review can be used as a reference guideline for developing systems of soft-tissue simulations.

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Liu, Xiaoping P., Shaoping Xu, Hua Zhang, and Linyan Hu. "A New Hybrid Soft Tissue Model for Visio-Haptic Simulation." IEEE Transactions on Instrumentation and Measurement 60, no. 11 (November 2011): 3570–81. http://dx.doi.org/10.1109/tim.2011.2161142.

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