Meshless algorithm for soft tissue cutting in surgical simulation

Three-dimensional anisotropic unified continuum model for simulating the healing of damaged soft biological tissues

The soft biological tissues have the ability to heal and self-repair after damage or injury. During the healing process, damaged tissues are replaced by newly produced undamaged tissue to restore homeostasis. Computational modeling serves as an effective tool for simulating the healing process and understanding the underlying mechanisms. In previous work, we developed the first unified continuum damage model for the healing of soft biological tissues. However, the initial theory lacked generalizability to more realistic scenarios and applicability to biomechanical problems due to the simplicity of the isotropic constitutive model and two-dimensional simulations. Therefore, we further improve our approach by developing a three-dimensional anisotropic unified healing model to address more realistic challenges. By using the Holzapfel-Gasser-Ogden model as the hyperelastic term, the influence of the collagen fibers is considered and the reorientation of fibers in healing is simulated. Three numerical examples related to hypertension, aneurysm, and restenosis of the atherosclerotic artery after balloon angioplasty are presented to demonstrate the effectiveness of the proposed model. By comparing numerical solutions and reference solutions, we demonstrate the ability of the proposed model in simulating long-term tissue healing process and analyze the impact of anisotropic terms.

Development of a maxillofacial virtual surgical system based on biomechanical parameters of facial soft tissue

Purpose

Lack of biomechanical force model of soft tissue hinders the development of virtual surgical simulation in maxillofacial surgery. In this study, a physical model of facial soft tissue based on real biomechanical parameters was constructed, and a haptics-enabled virtual surgical system was developed to simulate incision-making process on facial soft tissue and to help maxillofacial surgery training.

Methods

CT data of a 25-year-old female patient were imported into Mimics software to reconstruct 3D models of maxillofacial soft and skeletal tissues. 3dMD stereo-photo of the patient was fused on facial surface to include texture information. Insertion and cutting parameters of facial soft tissue measured on fresh cadavers were integrated, and a maxillofacial biomechanical force model was established. Rapid deformation and force feedback were realized through localized deformation algorithm and axis aligned bounding box (AABB)-based collision detection. The virtual model was validated quantitatively and qualitatively.

Results

A patient-specific physical model composed of skeletal and facial soft tissue was constructed and embedded in the virtual surgical system. Insertion and cutting in different regions of facial soft tissue were simulated using omega 6, and real-time feedback force was recorded. The feedback force was consistent with acquired force data of experiments conducted on tissue specimen. Real-time graphic and haptic feedback were realized. The mean score of the system performance was 3.71 given by surgeons in evaluation questionnaires.

Conclusion

The maxillofacial physical model enabled operators to simulate insertion and cutting on facial soft tissue with realization of realistic deformation and haptic feedback. The combination of localized deformation algorithm and AABB-based collision detection improved computational efficiency. The proposed virtual surgical system demonstrated excellent performance in simulation and training of incision-making process.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11548-022-02657-5.

Mathematical modeling and computer simulation of needle insertion into soft tissue

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.

Suite of meshless algorithms for accurate computation of soft tissue deformation for surgical simulation

The ability to predict patient-specific soft tissue deformations is key for computer-integrated surgery systems and the core enabling technology for a new era of personalized medicine. Element-Free Galerkin (EFG) methods are better suited for solving soft tissue deformation problems than the finite element method (FEM) due to their capability of handling large deformation while also eliminating the necessity of creating a complex predefined mesh. Nevertheless, meshless methods based on EFG formulation, exhibit three major limitations: (i) meshless shape functions using higher order basis cannot always be computed for arbitrarily distributed nodes (irregular node placement is crucial for facilitating automated discretization of complex geometries); (ii) imposition of the Essential Boundary Conditions (EBC) is not straightforward; and, (iii) numerical (Gauss) integration in space is not exact as meshless shape functions are not polynomial. This paper presents a suite of Meshless Total Lagrangian Explicit Dynamics (MTLED) algorithms incorporating a Modified Moving Least Squares (MMLS) method for interpolating scattered data both for visualization and for numerical computations of soft tissue deformation, a novel way of imposing EBC for explicit time integration, and an adaptive numerical integration procedure within the Meshless Total Lagrangian Explicit Dynamics algorithm. The appropriateness and effectiveness of the proposed methods is demonstrated using comparisons with the established non-linear procedures from commercial finite element software ABAQUS and experiments with very large deformations. To demonstrate the translational benefits of MTLED we also present a realistic brain-shift computation.

A review of virtual cutting methods and technology in deformable objects

BACKGROUND

Virtual cutting of deformable objects has been a research topic for more than a decade and has been used in many areas, especially in surgery simulation.

METHODS

We refer to the relevant literature and briefly describe the related research. The virtual cutting method is introduced, and we discuss the benefits and limitations of these methods and explore possible research directions.

RESULTS

Virtual cutting is a category of object deformation. It needs to represent the deformation of models in real time as accurately, robustly and efficiently as possible. To accurately represent models, the method must be able to: (1) model objects with different material properties; (2) handle collision detection and collision response; and (3) update the geometry and topology of the deformable model that is caused by cutting.

CONCLUSION

Virtual cutting is widely used in surgery simulation, and research of the cutting method is important to the development of surgery simulation.

A Novel Haptic Interactive Approach to Simulation of Surgery Cutting Based on Mesh and Meshless Models

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.

Mathematical Modeling and Virtual Reality Simulation of Surgical Tool Interactions With Soft Tissue: A Review and Prospective

This is an informed assessment of the state of the art and an extensive inventory of modeling approaches and methods for soft tissue/medical cutting tool interaction and of the associated medical processes and phenomena. Modeling and simulation through numerical, theoretical, computational, experimental, and other methods was discussed in comprehensive review sections each of which is concluded with a plausible prospective discussion biased toward the development of so-called virtual reality (VR) simulator environments. The finalized prospective section reflects on the future demands in the area of soft tissue cutting modeling and simulation mostly from a conceptual angle with emphasis on VR development requirements including real-time VR simulator response, cost-effective “close-to-reality” VR implementations, and other demands. The review sections that serve as the basis for the suggested prospective needs are categorized based on: (1) Major VR simulator applications including virtual surgery education, training, operation planning, intraoperative simulation, image-guided surgery, etc. and VR simulator types, e.g., generic, patient-specific and surgery-specific and (2) Available numerical, theoretical, and computational methods in terms of robustness, time effectiveness, computational cost, error control, and accuracy of modeling of certain types of virtual surgical interventions and their experimental validation, geared toward ethically driven artificial “phantom” tissue-based approaches. Digital data processing methods used in modeling of various feedback modalities in VR environments are also discussed.

Efficient Surgical Cutting with Position-Based Dynamics

Simulations of cuts on deformable bodies have been an active research subject for more than two decades. However, previous works based on finite element methods and mass spring meshes cannot scale to complex surgical scenarios. This article presents a novel method that uses position-based dynamics (PBD) for mesh-free cutting simulation. The proposed solutions include a method to efficiently render force feedback while cutting, an efficient heat diffusion model to simulate electrocautery, and a novel adaptive skinning scheme based on oriented particles.https://extras.computer.org/extra/mcg2017030024s1.mp4.

Modeling Soft Tissue Damage and Failure Using a Combined Particle/Continuum Approach

Biological soft tissues experience damage and failure as a result of injury, disease, or simply age; examples include torn ligaments and arterial dissections. Given the complexity of tissue geometry and material behavior, computational models are often essential for studying both damage and failure. Yet, because of the need to account for discontinuous phenomena such as crazing, tearing, and rupturing, continuum methods are limited. Therefore, we model soft tissue damage and failure using a particle/continuum approach. Specifically, we combine continuum damage theory with Smoothed Particle Hydrodynamics (SPH). Because SPH is a meshless particle method, and particle connectivity is determined solely through a neighbor list, discontinuities can be readily modeled by modifying this list. We show, for the first time, that an anisotropic hyperelastic constitutive model commonly employed for modeling soft tissue can be conveniently implemented within a SPH framework and that SPH results show excellent agreement with analytical solutions for uniaxial and biaxial extension as well as finite element solutions for clamped uniaxial extension in 2D and 3D. We further develop a simple algorithm that automatically detects damaged particles and disconnects the spatial domain along rupture lines in 2D and rupture surfaces in 3D. We demonstrate the utility of this approach by simulating damage and failure under clamped uniaxial extension and in a peeling experiment of virtual soft tissue samples. In conclusion, SPH in combination with continuum damage theory may provide an accurate and efficient framework for modeling damage and failure in soft tissues.

From Finite Element Meshes to Clouds of Points: A Review of Methods for Generation of Computational Biomechanics Models for Patient-Specific Applications

It has been envisaged that advances in computing and engineering technologies could extend surgeons' ability to plan and carry out surgical interventions more accurately and with less trauma. The progress in this area depends crucially on the ability to create robustly and rapidly patient-specific biomechanical models. We focus on methods for generation of patient-specific computational grids used for solving partial differential equations governing the mechanics of the body organs. We review state-of-the-art in this area and provide suggestions for future research. To provide a complete picture of the field of patient-specific model generation, we also discuss methods for identifying and assigning patient-specific material properties of tissues and boundary conditions.

Robot-assisted surgery: an emerging platform for human neuroscience research

Classic studies in human sensorimotor control use simplified tasks to uncover fundamental control strategies employed by the nervous system. Such simple tasks are critical for isolating specific features of motor, sensory, or cognitive processes, and for inferring causality between these features and observed behavioral changes. However, it remains unclear how these theories translate to complex sensorimotor tasks or to natural behaviors. Part of the difficulty in performing such experiments has been the lack of appropriate tools for measuring complex motor skills in real-world contexts. Robot-assisted surgery (RAS) provides an opportunity to overcome these challenges by enabling unobtrusive measurements of user behavior. In addition, a continuum of tasks with varying complexity-from simple tasks such as those in classic studies to highly complex tasks such as a surgical procedure-can be studied using RAS platforms. Finally, RAS includes a diverse participant population of inexperienced users all the way to expert surgeons. In this perspective, we illustrate how the characteristics of RAS systems make them compelling platforms to extend many theories in human neuroscience, as well as, to develop new theories altogether.

Biomechanical model as a registration tool for image-guided neurosurgery: evaluation against BSpline registration

In this paper we evaluate the accuracy of warping of neuro-images using brain deformation predicted by means of a patient-specific biomechanical model against registration using a BSpline-based free form deformation algorithm. Unlike the BSpline algorithm, biomechanics-based registration does not require an intra-operative MR image which is very expensive and cumbersome to acquire. Only sparse intra-operative data on the brain surface is sufficient to compute deformation for the whole brain. In this contribution the deformation fields obtained from both methods are qualitatively compared and overlaps of Canny edges extracted from the images are examined. We define an edge based Hausdorff distance metric to quantitatively evaluate the accuracy of registration for these two algorithms. The qualitative and quantitative evaluations indicate that our biomechanics-based registration algorithm, despite using much less input data, has at least as high registration accuracy as that of the BSpline algorithm.