Weixin Si

Point-based visuo-haptic simulation of multi-organ for virtual surgery

Background and Objectives: Realistically and efficiently simulating dynamic behavior of human organs under interactions is crucial for the immersive user experience of the surgical simulator. Conventional methods are time-consuming to simulate this phenomenon due to topological modifications. Materials and Methods: This paper proposes a robust and efficient point-based framework for surgical simulation, allowing realistically simulating mechanical response of human organs under interactions with visual and tactile feedback. Considering the inevitable topological modifications occurred in surgical simulation, we adopt sparse point cloud to model the mechanics of deformable bodies while employ surface mesh to represent morphological details of human organ, which can not only disconnect mechanical complexity from geometrical details, but also enable precise boundary conditions to be solved with surface mesh. Results: We validate our method on a variety of challenging surgical scenarios, and the results demonstrate that our method can realistically and efficiently provide the visuo-haptic feedback for surgical simulation. Conclusions: Our method can well tackle the inefficiency limitation of mesh-based methods related to topological modifications issue, and has great potential to be adopted in practical surgical simulators.

Animating Wall-Bounded Turbulent Smoke via Filament-Mesh Particle-Particle Method

Turbulent vortices in smoke flows are crucial for a visually interesting appearance. Unfortunately, it is challenging to efficiently simulate these appealing effects in the framework of vortex filament methods. The vortex filaments in grids scheme allows to efficiently generate turbulent smoke with macroscopic vortical structures, but suffers from the projection-related dissipation, and thus the small-scale vortical structures under grid resolution are hard to capture. In addition, this scheme cannot be applied in wall-bounded turbulent smoke simulation, which requires efficiently handling smoke-obstacle interaction and creating vorticity at the obstacle boundary. To tackle above issues, we propose an effective filament-mesh particle-particle (FMPP) method for fast wall-bounded turbulent smoke simulation with ample details. The Filament-Mesh component approximates the smooth long-range interactions by splatting vortex filaments on grid, solving the Poisson problem with a fast solver, and then interpolating back to smoke particles. The Particle-Particle component introduces smoothed particle hydrodynamics (SPH) turbulence model for particles in the same grid, where interactions between particles cannot be properly captured under grid resolution. Then, we sample the surface of obstacles with boundary particles, allowing the interaction between smoke and obstacle being treated as pressure forces in SPH. Besides, the vortex formation region is defined at the back of obstacles, providing smoke particles flowing by the separation particles with a vorticity force to simulate the subsequent vortex shedding phenomenon. The proposed approach can synthesize the lost small-scale vortical structures and also achieve the smoke-obstacle interaction with vortex shedding at obstacle boundaries in a lightweight manner. The experimental results demonstrate that our FMPP method can achieve more appealing visual effects than vortex filaments in grids scheme by efficiently simulating more vivid thin turbulent features.

Patch green coordinates based interactive embedded deformable model

Virtual surgery is a serious game which provides an opportunity to acquire cognitive and technical surgical skills via virtual surgical training and planning. However, interactively and realistically manipulating the human organ and simulating its motion under interaction is still a challenging task in this field. The underlying reason for this issue is the conflict requirements for physical constraints with high fidelity and real-time performance. To achieve realistic simulation of human organ motion with volume conservation, smooth interpolation under large deformation and precise frictional contact mechanics of global behavior in surgical scenario. This paper presents a novel and effective patch Green coordinates based interpolation for embedded deformable model to achieve the volume-preserving and smooth interpolation effects. Besides, we resolve the frictional contact mechanics for embedded deformable model, and further provide the precise boundary conditions for mechanical solver. In addition, our embedded deformable model is based on the total lagrangian explicit dynamics (TLED) finite element method (FEM) solver, which can well handle the large biological tissue deformation with both nonlinear geometric and material properties. In real compression experiments, our method can achieve liver deformation with average accuracy of 3.02 mm. Besides, the experimental results demonstrate that our method can also achieve smoother interpolation and volume-preserving effects than original embedded deformable model, and allows complex and accurate organ motion with mechanical interactions in virtual surgery.

Computational Modeling of Fluid–Structure Interaction Between Blood Flow and Mitral Valve

Mitral valve repair is a complex operation, in which the functionality of incompetent mitral valve is reconstructed by surgical techniques. Simulation-based surgical planning system, allowing surgeons to simulate and compare potential repair strategies, could greatly improve surgical outcomes. This paper presents a practical computational framework, combining the Total Lagrangian Explicit Dynamics Finite Element Method (TLED FEM) and Smoothed Particle Hydrodynamics (SPH), to solve the interaction problem of blood and immersed mitral valves. With this completed pipeline, we can not only predict the mechanical behavior of mitral valve, but also analyze the transvalvular pressures distributed on valve leaflets. The experimental results demonstrate that our method has the potential to be applied in surgical planning simulator of mitral valve repair.

3D Deeply-Supervised U-Net Based Whole Heart Segmentation

Accurate whole-heart segmentation from multi-modality medical images (MRI, CT) plays an important role in many clinical applications, such as precision surgical planning and improvement of diagnosis and treatment. This paper presents a deeply-supervised 3D U-Net for fully automatic whole-heart segmentation by jointly using the multi-modal MRI and CT images. First, a 3D U-Net is employed to coarsely detect the whole heart and segment its region of interest, which can alleviate the impact of surrounding tissues. Then, we artificially enlarge the training set by extracting different regions of interest so as to train a deep network. We perform voxel-wise whole-heart segmentation with the end-to-end trained deeply-supervised 3D U-Net. Considering that different modality information of the whole heart has a certain complementary effect, we extract multi-modality features by fusing MRI and CT images to define the overall heart structure, and achieve final results. We evaluate our method on cardiac images from the multi-modality whole heart segmentation (MM-WHS) 2017 challenge.

Filament‐based realistic turbulent wake synthesis

Turbulent wake is crucial for the visually appealing effects of liquid. Unfortunately, it is challenging to realistically simulate this phenomenon with ring‐shaped vortical structures. To tackle this issue, we propose a filament‐based turbulent wake synthesis method for realistically simulating the turbulent wake with ring‐shaped vortical structures. The filaments are sampled at the separation points on the obstacle surface and emitted into the liquid flow to generate structured turbulent wake. Besides, the surface tension model is incorporated to generate natural turbulent wake diffusion visual effects in liquid by the anticurvature effects. The proposed approach can realistically and effectively synthesize the turbulent wake with ring‐shaped vortical structures and make it diffuse naturally. The experimental results demonstrate that our method outperforms than the vortex particle‐based method in synthesizing appealing turbulent wake.

Personalized heterogeneous deformable model for fast volumetric registration

BackgroundBiomechanical deformable volumetric registration can help improve safety of surgical interventions by ensuring the operations are extremely precise. However, this technique has been limited by the accuracy and the computational efficiency of patient-specific modeling.MethodsThis study presents a tissue–tissue coupling strategy based on penalty method to model the heterogeneous behavior of deformable body, and estimate the personalized tissue–tissue coupling parameters in a data-driven way. Moreover, considering that the computational efficiency of biomechanical model is highly dependent on the mechanical resolution, a practical coarse-to-fine scheme is proposed to increase runtime efficiency. Particularly, a detail enrichment database is established in an offline fashion to represent the mapping relationship between the deformation results of high-resolution hexahedral mesh extracted from the raw medical data and a newly constructed low-resolution hexahedral mesh. At runtime, the mechanical behavior of human organ under interactions is simulated with this low-resolution hexahedral mesh, then the microstructures are synthesized in virtue of the detail enrichment database.ResultsThe proposed method is validated by volumetric registration in an abdominal phantom compression experiments. Our personalized heterogeneous deformable model can well describe the coupling effects between different tissues of the phantom. Compared with high-resolution heterogeneous deformable model, the low-resolution deformable model with our detail enrichment database can achieve 9.4× faster, and the average target registration error is 3.42 mm, which demonstrates that the proposed method shows better volumetric registration performance than state-of-the-art.ConclusionsOur framework can well balance the precision and efficiency, and has great potential to be adopted in the practical augmented reality image-guided robotic systems.

Hybrid vortex model for efficiently simulating turbulent smoke

Simulating fluids based on vortex methods can produce attractive visual effects for movies, games and virtual reality systems. However, for any vortex method, such as vortex filament, vortex sheet and vortex particle, it is still a challenging task to efficiently and stably simulate the high-quality smoke, as the motion of each fluid element is influenced by all the vortex elements in the simulation domain and different method has its certain advantages or limitations. In this paper, we introduce the vortex filaments in grids scheme, in which the uniform grids dynamically bridge the vortex filaments and smoke particles for efficient smoke simulation with fine visual effects. We present the novel hybrid vortex model that employs Vortex-in-Cell for efficient smoke simulation with turbulent visual effects. Our model can sensibly estimate the turbulence status of fluid and base on that to utilize the hybrid model that describes the vortex motion for more efficiency. We further break down the overly stretched filaments into vortex particles, with which we obtain the stable smoke simulation with diffusion of vorticity. Our experimental results have showed that the hybrid vortex model are enabled to produce the visual plausibility with effective and stable performance.