We present the first parallel surface reconstruction algorithm that runs entirely on the GPU. Like existing implicit surface reconstruction methods, our algorithm first builds an octree for the given set of oriented points, then computes an implicit function over the space of the octree, and finally extracts an isosurface as a watertight triangle mesh. A key component of our algorithm is a novel technique for octree construction on the GPU. This technique builds octrees in real time and uses level-order traversals to exploit the fine-grained parallelism of the GPU. Moreover, the technique produces octrees that provide fast access to the neighborhood information of each octree node, which is critical for fast GPU surface reconstruction. With an octree so constructed, our GPU algorithm performs Poisson surface reconstruction, which produces high-quality surfaces through a global optimization. Given a set of 500 K points, our algorithm runs at the rate of about five frames per second, which is over two orders of magnitude faster than previous CPU algorithms. To demonstrate the potential of our algorithm, we propose a user-guided surface reconstruction technique which reduces the topological ambiguity and improves reconstruction results for imperfect scan data. We also show how to use our algorithm to perform on-the-fly conversion from dynamic point clouds to surfaces as well as to reconstruct fluid surfaces for real-time fluid simulation.

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Data-Parallel Octrees for Surface Reconstruction

With the increasing availability of high-resolution isotropic three- or four-dimensional medical datasets from sources such as magnetic resonance imaging, computed tomography, and ultrasound, volumetric image visualization techniques have increased in importance. Over the past two decades, a number of new algorithms and improvements have been developed for practical clinical image display. More recently, further efficiencies have been attained by designing and implementing volume-rendering algorithms on graphics processing units (GPUs). In this paper, we review volumetric image visualization pipelines, algorithms, and medical applications. We also illustrate our algorithm implementation and evaluation results, and address the advantages and drawbacks of each algorithm in terms of image quality and efficiency. Within the outlined literature review, we have integrated our research results relating to new visualization, classification, enhancement, and multimodal data dynamic rendering. Finally, we illustrate issues related to modern GPU working pipelines, and their applications in volume visualization domain.

Volume Visualization: A Technical Overview with a Focus on Medical Applications

The current simulation technology used for neurosurgical training leaves much to be desired. Significant efforts are thoroughly exhausted in hopes of developing simulations that translate to give learners the "real-life" feel. Though a respectable goal, this may not be necessary as the application for simulation in neurosurgical training may be most useful in early learners. The ultimate uniformly agreeable endpoint of improved outcome and patient safety drives these investments. We explore the development, availability, educational taskforces, cost burdens and the simulation advancements in neurosurgical training. The technologies can be directed at achieving early resident milestones placed by the Accreditation Council for Graduate Medical Education. We discuss various aspects of neurosurgery disciplines with specific technologic advances of simulation software. An overview of the scholarly landscape of the recent publications in the realm of medical simulation and virtual reality pertaining to neurologic surgery is provided. We analyze concurrent concept overlap between PubMed headings and provide a graphical overview of the associations between these terms.

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Simulation training in neurosurgery: advances in education and practice

Mass-Spring Models (MSMs) are used to simulate the mechanical behavior of deformable bodies such as soft tissues in medical applications. Although they are fast to compute, they lack accuracy and their design remains still a great challenge. The major difficulties in building realistic MSMs lie on the spring stiffness estimation and the topology identification. In this work, the mechanical behavior of MSMs under tensile loads is analyzed before studying the spring stiffness estimation. In particular, the performed qualitative and quantitative analysis of the behavior of cubical MSMs shows that they have a nonlinear response similar to hyperelastic material models. According to this behavior, a new method for spring stiffness estimation valid for linear and nonlinear material models is proposed. This method adjusts the stress-strain and compressibility curves to a given reference behavior. The accuracy of the MSMs designed with this method is tested taking as reference some soft-tissue simulations based on nonlinear Finite Element Method (FEM). The obtained results show that MSMs can be designed to realistically model the behavior of hyperelastic materials such as soft tissues and can become an interesting alternative to other approaches such as nonlinear FEM.

Cubical Mass-Spring Model Design Based on a Tensile Deformation Test and Nonlinear Material Model

The purpose of creating the virtual reality (VR) simulator is to facilitate and supplement the training opportunities provided to orthopedic residents. The use of VR simulators has increased rapidly in the field of medical surgery for training purposes. This paper discusses the creation of the virtual surgical environment (VSE) for training residents in an orthopedic surgical process called less invasive stabilization system (LISS) surgery which is used to address fractures of the femur. The overall methodology included first obtaining an understanding of the LISS plating process through interactions with expert orthopedic surgeons and developing the information centric models. The information centric models provided a structured basis to design and build the simulator. Subsequently, the haptic-based simulator was built. Finally, the learning assessments were conducted in a medical school. The results from the learning assessments confirm the effectiveness of the VSE for teaching medical residents and students. The scope of the assessment was to ensure (1) the correctness and (2) the usefulness of the VSE. Out of 37 residents/students who participated in the test, 32 showed improvements in their understanding of the LISS plating surgical process. A majority of participants were satisfied with the use of teaching Avatars and haptic technology. A paired t test was conducted to test the statistical significance of the assessment data which showed that the data were statistically significant. This paper demonstrates the usefulness of adopting information centric modeling approach in the design and development of the simulator. The assessment results underscore the potential of using VR-based simulators in medical education especially in orthopedic surgery.

An advanced simulator for orthopedic surgical training.

A proposed real-time neurosurgery simulator handles skull drilling and surgical interaction with the brain. This involves the development and combination of areas such as collision handling, haptic rendering, physical simulation, and volumetric visualization. The simulator's input data comes from computed-tomography and magnetic-resonance-imaging images of the patients. Collision detection for drilling uses only density data; collision detection for interaction with the brain is based on uniform spatial subdivision of a tetrahedral mesh. To take advantage of all the information, the simulator employs visualization methods such as volumetric isosurfaces and deformable volume rendering.

A Brain Surgery Simulator

Development of a maxillofacial surgery simulation software capable of predicting a patient’s appearance after surgery. We have derived a new mass-spring model (MSM) equivalent to a linear finite element (FE) model for cubic elements. In addition, we propose the scaled displacement method as a new method to perform the simulation more realistically. The average error of eight soft tissue landmarks measured between 0.37 and 2.01 mm except from a landmark that had an error of 4.44 mm; values close to those obtained with the linear FE method. On the other hand, the scaled displacement method allows avoiding punctual stress concentration and bending effects making a much more realistic simulation in the region of the bone cut. Good results have been achieved with our two proposed methods. In addition, the simple way in which MSM can be parallelized makes it an interesting alternative to FE method.

Maxillofacial surgery simulation using a mass-spring model derived from continuum and the scaled displacement method

A GPU capable method for surface reconstruction from unorganized point clouds without additional information, called GLT (GPU Local Triangulation), is presented. The main objective of this research is the generation of a GPU interpolating reconstruction based on local Delaunay triangulations, inspired by a pre-existing reconstruction algorithm. Current graphics hardware accelerated algorithms are approximating approaches, where the final triangulation is usually performed through either marching cubes or marching tetrahedras. GPUcompatible methods and data structures to perform normal estimation and the local triangulation have been developed, plus a variation of the Bitonic Merge Sort algorithm to work with multi-lists. Our method shows an average gain of one order of magnitude over previous research.

GPU local triangulation: an interpolating surface reconstruction algorithm

Currently, many visualization methods are used in computer assisted medicine. It is commonly considered that a unique visualization scheme makes difficult the interaction and limits the quality and quantity of the information shown. In this paper we study the specific requirements of a maxillofacial surgery simulation tool for facial appearance prediction. The different stages of the application lead to present medical information in different ways. We propose to adapt visualization techniques to give a more suitable answer to these needs: a hybrid volumetric and polygonal visualization for the planning stage, as well as a scheme for surgery definition in 2D. Finally, we propose the use of mesh visualization for the simulated model, which previously requires the 3D reconstruction of the surface in order to be visualized.

Hybrid Visualization for Maxillofacial Surgery Planning and Simulation

We present a GPU method for surface reconstruction from unorganized point clouds without additional information, based on the work of [Gopi et al 2000] The main objective of this work is the generation of a GPU interpolating reconstruction method by using local Delaunay triangulations Existing algorithms accelerated by graphic hardware are approximating approaches, usually based on either marching cubes or marching tetrahedras Our work most valuable innovation is the GPU adaptation itself; we developed GPU suitable methods to replace those steps which could not to be implemented in graphics hardware The two most noticeable ones are the O(log(k)) method which identifies Delaunay neighbors (replacing the O(k) backtracking) and the sorting of a great number of small lists in O(log2(k)) Additionally, a divide and conquer approach was adopted to suit video memory constrains; passes are then independent, so multi-GPU parallelization can be performed without changes Our tests show an 11 times average gain over the CPU version

Carlos Buchart

Papers

A Brain Surgery Simulator

Maxillofacial surgery simulation using a mass-spring model derived from continuum and the scaled displacement method

GPU local triangulation: an interpolating surface reconstruction algorithm

Hybrid Visualization for Maxillofacial Surgery Planning and Simulation

A GPU interpolating reconstruction from unorganized points