Rendering techniques for multimodal data

Many different direct volume rendering methods have been developed to visualize 3D scalar fields on uniform rectilinear grids. However, little work has been done on rendering simultaneously various properties of the same 3D region measured with different registration devices or at different instants of time. The demand for this type of visualization is rapidly increasing in scientific applications such as medicine in which the visual integration of multiple modalities allows a better comprehension of the anatomy and a perception of its relationships with activity. This paper presents different strategies of Direct Multimodal Volume Rendering (DMVR). It is restricted to voxel models with a known 3D rigid alignment transformation. The paper evaluates at which steps of the render-ing pipeline must the data fusion be realized in order to accomplish the desired visual integration and to provide fast re-renders when some fusion parameters are modified. In addition, it analyzes how existing monomodal visualization al-gorithms can be extended to multiple datasets and it compares their efficiency and their computational cost.Postprint (published version

Dynamic Volume Rendering of Functional Medical Data on Dissimilar Hardware Platforms

In the last 30 years, medical imaging has become one of the most used diagnostic tools in the medical profession. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) technologies have become widely adopted because of their ability to capture the human body in a non-invasive manner. A volumetric dataset is a series of orthogonal 2D slices captured at a regular interval, typically along the axis of the body from the head to the feet. Volume rendering is a computer graphics technique that allows volumetric data to be visualized and manipulated as a single 3D object. Iso-surface rendering, image splatting, shear warp, texture slicing, and raycasting are volume rendering methods, each with associated advantages and disadvantages. Raycasting is widely regarded as the highest quality renderer of these methods. Originally, CT and MRI hardware was limited to providing a single 3D scan of the human body. The technology has improved to allow a set of scans capable of capturing anatomical movements like a beating heart. The capturing of anatomical data over time is referred to as functional imaging. Functional MRI (fMRI) is used to capture changes in the human body over time. While fMRI’s can be used to capture any anatomical data over time, one of the more common uses of fMRI is to capture brain activity. The fMRI scanning process is typically broken up into a time consuming high resolution anatomical scan and a series of quick low resolution scans capturing activity. The low resolution activity data is mapped onto the high resolution anatomical data to show changes over time. Academic research has advanced volume rendering and specifically fMRI volume rendering. Unfortunately, academic research is typically a one-off solution to a singular medical case or set of data, causing any advances to be problem specific as opposed to a general capability. Additionally, academic volume renderers are often designed to work on a specific device and operating system under controlled conditions. This prevents volume rendering from being used across the ever expanding number of different computing devices, such as desktops, laptops, immersive virtual reality systems, and mobile computers like phones or tablets. This research will investigate the feasibility of creating a generic software capability to perform real-time 4D volume rendering, via raycasting, on desktop, mobile, and immersive virtual reality platforms. Implementing a GPU-based 4D volume raycasting method for mobile devices will harness the power of the increasing number of mobile computational devices being used by medical professionals. Developing support for immersive virtual reality can enhance medical professionals’ interpretation of 3D physiology with the additional depth information provided by stereoscopic 3D. The results of this research will help expand the use of 4D volume rendering beyond the traditional desktop computer in the medical field. Developing the same 4D volume rendering capabilities across dissimilar platforms has many challenges. Each platform relies on their own coding languages, libraries, and hardware support. There are tradeoffs between using languages and libraries native to each platform and using a generic cross-platform system, such as a game engine. Native libraries will generally be more efficient during application run-time, but they require different coding implementations for each platform. The decision was made to use platform native languages and libraries in this research, whenever practical, in an attempt to achieve the best possible frame rates. 4D volume raycasting provides unique challenges independent of the platform. Specifically, fMRI data loading, volume animation, and multiple volume rendering. Additionally, real-time raycasting has never been successfully performed on a mobile device. Previous research relied on less computationally expensive methods, such as orthogonal texture slicing, to achieve real-time frame rates. These challenges will be addressed as the contributions of this research. The first contribution was exploring the feasibility of generic functional data input across desktop, mobile, and immersive virtual reality. To visualize 4D fMRI data it was necessary to build in the capability to read Neuroimaging Informatics Technology Initiative (NIfTI) files. The NIfTI format was designed to overcome limitations of 3D file formats like DICOM and store functional imagery with a single high-resolution anatomical scan and a set of low-resolution anatomical scans. Allowing input of the NIfTI binary data required creating custom C++ routines, as no object oriented APIs freely available for use existed. The NIfTI input code was built using C++ and the C++ Standard Library to be both light weight and cross-platform. Multi-volume rendering is another challenge of fMRI data visualization and a contribution of this work. fMRI data is typically broken into a single high-resolution anatomical volume and a series of low-resolution volumes that capture anatomical changes. Visualizing two volumes at the same time is known as multi-volume visualization. Therefore, the ability to correctly align and scale the volumes relative to each other was necessary. It was also necessary to develop a compositing method to combine data from both volumes into a single cohesive representation. Three prototype applications were built for the different platforms to test the feasibility of 4D volume raycasting. One each for desktop, mobile, and virtual reality. Although the backend implementations were required to be different between the three platforms, the raycasting functionality and features were identical. Therefore, the same fMRI dataset resulted in the same 3D visualization independent of the platform itself. Each platform uses the same NIfTI data loader and provides support for dataset coloring and windowing (tissue density manipulation). The fMRI data can be viewed changing over time by either animation through the time steps, like a movie, or using an interface slider to “scrub” through the different time steps of the data. The prototype applications data load times and frame rates were tested to determine if they achieved the real-time interaction goal. Real-time interaction was defined by achieving 10 frames per second (fps) or better, based on the work of Miller [1]. The desktop version was evaluated on a 2013 MacBook Pro running OS X 10.12 with a 2.6 GHz Intel Core i7 processor, 16 GB of RAM, and a NVIDIA GeForce GT 750M graphics card. The immersive application was tested in the C6 CAVE™, a 96 graphics node computer cluster comprised of NVIDIA Quadro 6000 graphics cards running Red Hat Enterprise Linux. The mobile application was evaluated on a 2016 9.7” iPad Pro running iOS 9.3.4. The iPad had a 64-bit Apple A9X dual core processor with 2 GB of built in memory. Two different fMRI brain activity datasets with different voxel resolutions were used as test datasets. Datasets were tested using both the 3D structural data, the 4D functional data, and a combination of the two. Frame rates for the desktop implementation were consistently above 10 fps, indicating that real-time 4D volume raycasting is possible on desktop hardware. The mobile and virtual reality platforms were able to perform real-time 3D volume raycasting consistently. This is a marked improvement for 3D mobile volume raycasting that was previously only able to achieve under one frame per second [2]. Both VR and mobile platforms were able to raycast the 4D only data at real-time frame rates, but did not consistently meet 10 fps when rendering both the 3D structural and 4D functional data simultaneously. However, 7 frames per second was the lowest frame rate recorded, indicating that hardware advances will allow consistent real-time raycasting of 4D fMRI data in the near future

A Data-Virtualization System for Large Model Visualization

Interactive scientific visualizations are widely used for the visual exploration and examination of physical data resulting from measurements or simulations. Driven by technical advancements of data acquisition and simulation technologies, especially in the geo-scientific domain, large amounts of highly detailed subsurface data are generated. The oil and gas industry is particularly pushing such developments as hydrocarbon reservoirs are increasingly difficult to discover and exploit. Suitable visualization techniques are vital for the discovery of the reservoirs as well as their development and production. However, the ever-growing scale and complexity of geo-scientific data sets result in an expanding disparity between the size of the data and the capabilities of current computer systems with regard to limited memory and computing resources. In this thesis we present a unified out-of-core data-virtualization system supporting geo-scientific data sets consisting of multiple large seismic volumes and height-field surfaces, wherein each data set may exceed the size of the graphics memory or possibly even the main memory. Current data sets fall within the range of hundreds of gigabytes up to terabytes in size. Through the mutual utilization of memory and bandwidth resources by multiple data sets, our data-management system is able to share and balance limited system resources among different data sets. We employ multi-resolution methods based on hierarchical octree and quadtree data structures to generate level-of-detail working sets of the data stored in main memory and graphics memory for rendering. The working set generation in our system is based on a common feedback mechanism with inherent support for translucent geometric and volumetric data sets. This feedback mechanism collects information about required levels of detail during the rendering process and is capable of directly resolving data visibility without the application of any costly occlusion culling approaches. A central goal of the proposed out-of-core data management system is an effective virtualization of large data sets. Through an abstraction of the level-of-detail working sets, our system allows developers to work with extremely large data sets independent of their complex internal data representations and physical memory layouts. Based on this out-of-core data virtualization infrastructure, we present distinct rendering approaches for specific visualization problems of large geo-scientific data sets. We demonstrate the application of our data virtualization system and show how multi-resolution data can be treated exactly the same way as regular data sets during the rendering process. An efficient volume ray casting system is presented for the rendering of multiple arbitrarily overlapping multi-resolution volume data sets. Binary space-partitioning volume decomposition of the bounding boxes of the cube-shaped volumes is used to identify the overlapping and non-overlapping volume regions in order to optimize the rendering process. We further propose a ray casting-based rendering system for the visualization of geological subsurface models consisting of multiple very detailed height fields. The rendering of an entire stack of height-field surfaces is accomplished in a single rendering pass using a two-level acceleration structure, which combines a minimum-maximum quadtree for empty-space skipping and sorted lists of depth intervals to restrict ray intersection searches to relevant height fields and depth ranges. Ultimately, we present a unified rendering system for the visualization of entire geological models consisting of highly detailed stacked horizon surfaces and massive volume data. We demonstrate a single-pass ray casting approach facilitating correct visual interaction between distinct translucent model components, while increasing the rendering efficiency by reducing processing overhead of potentially invisible parts of the model. The combination of image-order rendering approaches and the level-of-detail feedback mechanism used by our out-of-core data-management system inherently accounts for occlusions of different data types without the application of costly culling techniques. The unified out-of-core data-management and virtualization infrastructure considerably facilitates the implementation of complex visualization systems. We demonstrate its applicability for the visualization of large geo-scientific data sets using output-sensitive rendering techniques. As a result, the magnitude and multitude of data sets that can be interactively visualized is significantly increased compared to existing approaches

An Interactive Concave Volume Clipping Method Based on GPU Ray Casting with Boolean Operation

Volume clipping techniques can display inner structures and avoid difficulties in specifying an appropriate transfer function. We present an interactive concave volume clipping method by implementing both rendering and Boolean operation on GPU. Common analytical convex objects, such as polyhedrons and spheres, are determined by parameters. So it consumes very little video memory to implement concave volume clipping with Boolean operations on GPU. The intersection, subtraction and union operations are implemented on GPU by converting 3D Boolean operation into 1D Boolean operation. To enhance visual effects, a pseudo color based rendering model is proposed and the Phong illumination model is enabled on the clipped surfaces. Users are allowed to select a color scheme from several schemes that are pre-defined or specified by users, to obtain clear views of inner anatomical structures. At last, several experiments were performed on a standard PC with a GeForce FX8600 graphics card. Experimental results show that the three basic Boolean operations are correctly performed, and our approach can freely clip and visualize volumetric datasets at interactive frame rates

Efficient Geometry and Illumination Representations for Interactive Protein Visualization

This dissertation explores techniques for interactive simulation and visualization for large protein datasets. My thesis is that using efficient representations for geometric and illumination data can help in developing algorithms that achieve better interactivity for visual and computational proteomics. I show this by developing new algorithms for computation and visualization for proteins. I also show that the same insights that resulted in better algorithms for visual proteomics can also be turned around and used for more efficient graphics rendering. Molecular electrostatics is important for studying the structures and interactions of proteins, and is vital in many computational biology applications, such as protein folding and rational drug design. We have developed a system to efficiently solve the non-linear Poisson-Boltzmann equation governing molecular electrostatics. Our system simultaneously improves the accuracy and the efficiency of the solution by adaptively refining the computational grid near the solute-solvent interface. In addition, we have explored the possibility of mapping the PBE solution onto GPUs. We use pre-computed accumulation of transparency with spherical-harmonics-based compression to accelerate volume rendering of molecular electrostatics. We have also designed a time- and memory-efficient algorithm for interactive visualization of large dynamic molecules. With view-dependent precision control and memory-bandwidth reduction, we have achieved real-time visualization of dynamic molecular datasets with tens of thousands of atoms. Our algorithm is linearly scalable in the size of the molecular datasets. In addition, we present a compact mathematical model to efficiently represent the six-dimensional integrals of bidirectional surface scattering reflectance distribution functions (BSSRDFs) to render scattering effects in translucent materials interactively. Our analysis first reduces the complexity and dimensionality of the problem by decomposing the reflectance field into non-scattered and subsurface-scattered reflectance fields. While the non-scattered reflectance field can be described by 4D bidirectional reflectance distribution functions (BRDFs), we show that the scattered reflectance field can also be represented by a 4D field through pre-processing the neighborhood scattering radiance transfer integrals. We use a novel reference-points scheme to compactly represent the pre-computed integrals using a hierarchical and progressive spherical harmonics representation. Our algorithm scales linearly with the number of mesh vertices

Doctor of Philosophy in Computing

dissertationThe aim of direct volume rendering is to facilitate exploration and understanding of three-dimensional scalar fields referred to as volume datasets. Improving understanding is done by improving depth perception, whereas facilitating exploration is done by speeding up volume rendering. In this dissertation, improving both depth perception and rendering speed is considered. The impact of depth of field (DoF) on depth perception in direct volume rendering is evaluated by conducting a user study in which the test subjects had to choose which of two features, located at different depths, appeared to be in front in a volume-rendered image. Whereas DoF was expected to improve perception in all cases, the user study revealed that if used on the back feature, DoF reduced depth perception, whereas it produced a marked improvement when used on the front feature. We then worked on improving the speed of volume rendering on distributed memory machines. Distributed volume rendering has three stages: loading, rendering, and compositing. In this dissertation, the focus is on image compositing, more specifically, trying to optimize communication in image compositing algorithms. For that, we have developed the Task Overlapped Direct Send Tree image compositing algorithm, which works on both CPU- and GPU-accelerated supercomputers, which focuses on communication avoidance and overlapping communication with computation; the Dynamically Scheduled Region-Based image compositing algorithm that uses spatial and temporal awareness to efficiently schedule communication among compositing nodes, and a rendering and compositing pipeline that allows both image compositing and rendering to be done on GPUs of GPU-accelerated supercomputers. We tested these on CPU- and GPU-accelerated supercomputers and explain how these improvements allow us to obtain better performance than image compositing algorithms that focus on load-balancing and algorithms that have no spatial and temporal awareness of the rendering and compositing stages

Interactive simulation and rendering of fluids on graphics hardware

Computational uid dynamics can be used to reproduce the complex motion of fluids for use in computer graphics, but the simulation and rendering are both highly computationally intensive. In the past performing these tasks on the CPU could take many minutes per frame, especially for large scale scenes at high levels of detail, which limited their usage to offline applications such as in film and media. However, using the massive parallelism of GPUs, it is nowadays possible to produce uid visual effects in real time for interactive applications such as games. We present such an interactive simulation using the CUDA GPU computing environment and OpenGL graphics API. Smoothed Particle Hydrodynamics (SPH) is a popular particle-based fluid simulation technique that has been shown to be well suited to acceleration on the GPU. Our work extends an existing GPU-based SPH implementation by incorporating rigid body interaction and rendering. Solid objects are represented using particles to accumulate hydrodynamic forces from surrounding fluid, while motion and collision handling are handled by the Bullet Physics library on the CPU. Our system demonstrates two-way coupling with multiple objects floating, displacing fluid and colliding with each other. For rendering we compare the performance and memory consumption of two approaches, splatting and raycasting, we also describe the visual characteristics of each. In our evaluation we consider a target of between 24 and 30 fps to be sufficient for smooth interaction and aim to determine the performance impact of our new features. We begin by establishing a performance baseline and find that the original system runs smoothly up to 216,000 fluid particles but after introducing rendering this drops to 27,000 particles with the rendering taking up the majority of the frame time in both techniques. We find that the most significant limiting factor to splatting performance to be the onscreen area occupied by fluid while the raycasting performance is primarily determined by the resolution of the 3D texture used for sampling. Finally we find that performing solid interaction on the CPU is a viable approach that does not introduce significant overhead unless solid particles vastly outnumber fluid ones

Neural Assets: Volumetric Object Capture and Rendering for Interactive Environments

Creating realistic virtual assets is a time-consuming process: it usually involves an artist designing the object, then spending a lot of effort on tweaking its appearance. Intricate details and certain effects, such as subsurface scattering, elude representation using real-time BRDFs, making it impossible to fully capture the appearance of certain objects. Inspired by the recent progress of neural rendering, we propose an approach for capturing real-world objects in everyday environments faithfully and fast. We use a novel neural representation to reconstruct volumetric effects, such as translucent object parts, and preserve photorealistic object appearance. To support real-time rendering without compromising rendering quality, our model uses a grid of features and a small MLP decoder that is transpiled into efficient shader code with interactive framerates. This leads to a seamless integration of the proposed neural assets with existing mesh environments and objects. Thanks to the use of standard shader code rendering is portable across many existing hardware and software systems