Volume visualization on shared memory architectures

Lecture Notes in Computer Science, 2007

Volumetric data rendering has become an important tool in various medical procedures as it allows the unbiased visualization of fine details of volumetric medical data (CT, MRI, fMRI). However, due to the large amount of computation involved, the rendering time increases dramatically as the size of the data set grows. This paper presents several acceleration techniques of volume rendering using general-purpose GPU. Some techniques enhance the rendering speed of software ray casting based on voxels' opacity information, while the others improve traditional hardware-accelerated object-order volume rendering. Remarkable speedups are observed using the proposed GPU-based algorithm from experiments on routine medical data sets.

1993

Abstract A solution is proposed to the problem of interactive visualization and rendering of volume data. Designed for parallel distributed memory MIMD architectures, the volume rendering system is based on the ray tracing (RT) visualization technique, the Sticks representation scheme (a data structure exploiting data coherence for the compression of classified data sets), the use of a slice-partitioning technique for the distribution of the data between the processing nodes and the consequent ray-data-flow parallelizing strategy.

The Visual Computer, 1995

Images generated from volumetric datasets are increasingly being used in many biomedical disciplines, archeology, geology, high energy physics, computational chemistry, computational fluid dynamics, meteorology, astronomy, computer aided design, environmental sciences, and many others.

Computer Methods and Programs in Biomedicine, 2008

In medical area, interactive three-dimensional volume visualization of large volume datasets is a challenging task. One of the major challenges in graphics processing unit (GPU)-based volume rendering algorithms is the limited size of texture memory imposed by current GPU architecture. We attempt to overcome this limitation by rendering only visible parts of large CT datasets. In this paper, we present an efficient, high-quality volume rendering algorithm using GPUs for rendering large CT datasets at interactive frame rates on standard PC hardware. We subdivide the volume dataset into uniform sized blocks and take advantage of combinations of early ray termination, empty-space skipping and visibility culling to accelerate the whole rendering process and render visible parts of volume data. We have implemented our volume rendering algorithm for a large volume data of 512 × 304 × 1878 dimensions (visible female), and achieved real-time performance (i.e., 3-4 frames per second) on a Pentium 4 2.4 GHz PC equipped with NVIDIA Geforce 6600 graphics card (256 MB video memory). This method can be used as a 3D visualization tool of large CT datasets for doctors or radiologists.

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.

Computers & Graphics, 1995

Architecture and applications of a massively parallel system currently developed are described, which allows real-time visualization using volume oriented visualization algorithms. Volumes of 256 x 256 x 128 voxels can be visualized with a frame rate of 10 Hz. The system is scalable and modular, and will allow a multi-user access over high-speed networks. 3D-rotation around arbitrary rotation axis, perspective, zooming and arbitrary grey value mapping are provided in real-time. A volume oriented algorithm is used that is tailored to the requirements in medicine [H.-I'. Meinzer et d., IEEE Comp. Graph. Appl., 34 (Nov. 1991)]. With this algorithm, small structures without defined surfaces, e.g., tumours, can be visualized as well as semi-transparent objects. One planned application of the system is heart surgery.

Scalable High-Performance …, 1994

Three-dimensional arrays of digital data representing spatial volumes are generated from such diverse elds as the geosciences, space exploration and astrophysics, medical imaging, computational uid dynamics, molecular modeling, microelectronic eld modeling and computer simulation. With current advances in imaging devices and high performance computing, more and more applications will generate volumetric data in the near future. This paper presents a new distributed memory algorithm for volume rendering in a message-passing environment. The algorithm, which uses a slab technique for data partitioning, is a hybrid between the ray-casting and cell projection approaches for volumetric rendering. The results of some scaling experiments using ParaSoft Express on an Intel Paragon at the University of South Carolina are also presented.

Proceedings of the 1993 ACM/IEEE conference on Supercomputing - Supercomputing '93, 1993

Rendering volumes represented as a 3D grid of voxels requires an overwhelming amount of processing power. In this paper we investigate efficient techniques for rendering semi-transparent volumes on vector and parallel processors. Parallelism inherent in a regular grid is obtained by decomposing the volume into geometric primitives called beams, slices and slabs of voxels. By using the adjacency properties of voxels in beams and slices, efficient incremental transformation schemes are developed. The slab decomposition of the volume allows the implementation of an efficient parallel feed-forward renderer which includes the splatting technique for image reconstruction and a back-to-front method for creating images. We report the implementation of this feed-forward volume renderer on a hierarchical shared memory machine with individual pipelined processors.

Computers & Graphics, 2000

Direct volume rendering using volume ray-casting and indirect volume rendering using the Marching Cubes isosurface extraction are popular techniques for volume visualization. Surface ray-tracing is a popular graphics technique for rendering scenes composed of well-de"ned surface primitives. However, these techniques are relatively computationally intensive. Thus, near-real-time computational performance is a di$cult goal. This paper presents approaches to these rendering and volume visualization techniques that are tuned for e$cient performance on a vector-parallel supercomputer. The approaches decompose and reconstruct the techniques to exploit inherent data parallelism and the speci"c characteristics of the CPU. Experimental results for several datasets are also exhibited. : S 0 0 9 7 -8 4 9 3 ( 0 0 ) 0 0 0 7 7 -7

Computers & Graphics, 1992

This paper examines seven computer architectures specifically designed to rapidly render 3D medical images from voxel data. The paper opens with a discussion of work on architectures for 3D medical image rendering and then specifies parameters for assessing the performance of a 3D medical image rendering architecture. We then describe and assess the 3DP 4, the Cube, the INSIGHT, the PARCUM II, the PICAP II, the Voxel Flinger, and the Voxel Processor architectures. For each machine the rendering speed, image resolution, underlying data model, image quality, parallel processing strategy, and 3D display technique are discussed. The architecture for each machine is characterized by its data storage technique, computational architecture, and parallelism strategy.