Real-time visualization of large volume datasets on standard PC hardware (original) (raw)
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GPU-based volume rendering for medical imagery
We present a method for fast volume rendering using graphics hardware (GPU). To our knowledge, it is the first implementation on the GPU. Based on the Shear-Warp algorithm, our GPU-based method provides real-time frame rates and outperforms the CPU-based implementation. When the number of slices is not sufficient, we add in-between slices computed by interpolation. This improves then the quality of the rendered images. We have also implemented the ray marching algorithm on the GPU. The results generated by the three algorithms (CPU-based and GPU-based Shear-Warp, GPU-based Ray Marching) for two test models has proved that the ray marching algorithm outperforms the shear-warp methods in terms of speed up and image quality.
Mapping High-Fidelity Volume Rendering for Medical Imaging to CPU, GPU and Many-Core Architectures
IEEE Transactions on Visualization and Computer Graphics, 2000
Medical volumetric imaging requires high fidelity, high performance rendering algorithms. We motivate and analyze new volumetric rendering algorithms that are suited to modern parallel processing architectures. First, we describe the three major categories of volume rendering algorithms and confirm through an imaging scientist-guided evaluation that ray-casting is the most acceptable. We describe a thread-and data-parallel implementation of ray-casting that makes it amenable to key architectural trends of three modern commodity parallel architectures: multi-core, GPU, and upcoming Intel Larrabee. We achieve more than an order of magnitude performance improvement on a number of large 3D medical datasets. We further describe a data compression scheme that significantly reduces data-transfer overhead. This allows our approach to scale well to large numbers of Larrabee cores.
Real-Time Rendering of Temporal Volumetric Data on a GPU
2011 15th International Conference on Information Visualisation, 2011
Real-time rendering of static volumetric data is generally known to be a memory and computationally intensive process. With the advance of graphic hardware, especially GPU, it is now possible to do this using desktop computers. However, with the evolution of real-time CT and MRI technologies, volumetric rendering is an even bigger challenge. The first one is how to reduce the data transmission between the main memory and the graphic memory. The second one is how to efficiently take advantage of the time redundancy which exists in time-varying volumetric data. We proposed an optimized compression scheme that explores the time redundancy as well as space redundancy of time-varying volumetric data. The compressed data is then transmitted to graphic memory and directly rendered by the GPU, reducing significantly the data transfer between main memory and graphic memory.
Procedia Computer Science, 2015
The advances in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scanning techniques are improving the resolution and size of the volume datasets. The prevalence of three dimensional volumetric data is rapidly expanding with the internal information in the different resolution sizes of the dataset. In this paper we proposed an approach that can visualize the inner organs structure of the visible human male dataset in Multi-coordinate Viewing (MCV) framework. This can help to medical experts that are able to peer inside anatomy of the human medical dataset. The volume rendering part has been carried out by the utilization of enhanced ray casting algorithm for the crossing points of 3D square strategy for voxels. We present this system using Graphics Processing Unit or GPU-accelerated Compute Unified Device Architecture (CUDA) based approach for the focusing a specific region while zooming operation. The final results would allow the doctors to diagnose and analyze the atlas of 8-bit CT-scan data using three dimensional visualization with the efficient frame rate rendering speed in multi-operations like zooming, rotating, dragging. The framework is tested for visible human male dataset prepared by National Library of Medicine (NLM, USA) of size 1.2 GB.
Advances in Water Resources, 2010
We describe a system for volume rendering via ray casting, targeted at medical data and clinicians. We discuss the benefits of server vs client rendering, and of GPU vs CPU rendering, and show how we combine these two advantages using nVidia's Tesla hardware and CUDA toolkit. The resulting system allows hopsital-acquired data to be visualized on-demand and in real-time by multiple simultaneous users, with low latency even on low bandwidth networks and on thin clients. Each GPU serves multiple clients, and our system scales to many GPUs, with data distribution and load balancing, to create a fully scalable system for commercial deployment. To optimize rendering performance, we present our novel solution for empty space skipping, which improves on previous techniques used with CUDA. To demonstrate the flexibility of our system, we show several new visualization techniques, including assisted interaction through automatic organ detection and the ability to toggle visibility of pre-segmented organs. These visualizations have been deemed by clinicians to be highly useful for diagnostic purposes. Our performance results indicate that our system may be the best-value option for hospitals to provide ubiquitous access to state-of-the-art 3D visualizations.
Progressive fast volume rendering for medical images
Medical Imaging 2001: Visualization, Display, and Image-Guided Procedures, 2001
There are various 3D visualization methods such as volume rendering and surface renderingS The volume rendering (VR) is a useful tool to visualize 3D medical images. However, a requirement of large computation amount makes it difficult for the VR to be used in real-time medical applications. In order to overcome the large computation amount of the VR, we have developed a progressive VR (PVR) method that can perform the low-resolution VR for fast and intuitive processing and use the depth information from the low-resolution VR to generate the full-resolution VR image with a reduced computation time. The developed algorithm can be applicable to the real-time applications of the YR. Le., the low-resolution VR is performed interactively according to change of view direction, and the full-resolution VR is performed once we fix the view direction In this paper its computation complexity and image quality are analyzed Also an extension of its progressive refinement is introduced.
Real-time interactive visualization and manipulation of the volumetric data using GPU-based methods
Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display, 2004
This work presents a set of tools developed to provide 3D visualization and interaction with large volumetric data that relies on recent programmable capabilities of consumer-level graphics cards. We are exploiting the programmable control of calculations performed by the graphics hardware for generating the appearance of each pixel on the screen to develop real-time, interactive volume manipulation tools. These tools allow real-time modification of visualization parameters, such as color and opacity classification or the selection of a volume of interest, extending the benefit of hardware acceleration beyond display, namely for computation of voxel visibility. Three interactive tools are proposed: a cutting tool that allows the selection of a convex volume of interest, an eraser-like tool to eliminate non-relevant parts of the image and a digger-like tool that allows the user to eliminate layers of a 3D image. To interactively apply the proposed tools on a volume, we are making use of some so known user interaction techniques, as the ones used in 2D painting systems. Our strategy is to minimize the user entrainment efforts involved in the tools learning. Finally, we illustrate the potential application of the conceived tools for preoperative planning of liver surgery and for liver vascular anatomy study. Preliminary results concerning the system performance and the images quality and resolution are presented and discussed.
This paper presents Direct Volume Rendering (DVR) improvement strategies, which provide new opportunities for scientific and medical visualization which are not available in due measure in analogues: 1) multi-volume rendering in a single space of up to 3 volumetric datasets determined in different coordinate systems and having sizes as big as up to 512x512x512 16-bit values; 2) performing the above process in real time on a middle class GPU, e. g. nVidia GeForce GTS 250 512 M B; 3) a custom bounding mesh for more accurate selection of the desired region in addition to the clipping bounding box; 4) simultaneous usage of a number of visualization techniques including the shaded Direct Volume Rendering via the 1D-or 2D-transfer functions, multiple semi-transparent discrete iso-surfaces visualization, M IP, and M IDA. The paper discusses how the new properties affect the implementation of the DVR. In the DVR implementation we use such optimization strategies as the early ray termination and the empty space skipping. The clipping ability is also used as the empty space skipping approach to the rendering performance improvement. We use the random ray start position generation and the further frame accumulation in order to reduce the rendering artifacts. The rendering quality can be also improved by the onthe-fly tri-cubic filtering during the rendering process. Our framework supports 4 different stereoscopic visualization modes. Finally we outline the visualization performance in terms of the frame rates for different visualization techniques on different graphic cards.
An Overview of Volume Rendering Techniques for Medical Imaging
International Journal of Online and Biomedical Engineering (iJOE)
One of the most valuable medical imaging visualizations or computer-aided diagnosis is Volume rendering (VR). This survey’s objective is reviewing and comparing between several methods and techniques of VR, for a better and more comprehensive reading and learning of both pros and cons of each method, and their use cases.
Volume Visualization: A Technical Overview with a Focus on Medical Applications
Journal of Digital Imaging, 2010
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 volumerendering 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.