An Efficient Compressed Volume Rendering Algorithm Based on GPU

2012 ◽  
Vol 433-440 ◽  
pp. 5448-5452 ◽  
Author(s):  
Li Ping Zhao ◽  
Mei Fang ◽  
Yuan Wang Wei
Keyword(s):  
Vector Quantization ◽  
Volume Rendering ◽  
Graphic Processing Unit ◽  
Three Dimensional ◽  
Mapping Method ◽  
Processing Unit ◽  
Volume Data ◽  
Levels Of Detail ◽  
Graphic Hardware ◽  
Multiple Levels

An efficient compressed volume rendering algorithm is presented. Firstly, the original volume data is compressed by a content-based classified hierarchical vector quantization algorithm. Secondly, the compressed volume data is then transferred to Graphic Processing Unit and decompressed in real time, subsequently, the decompressed data is rendered by a three-dimensional textures mapping method to accelerate the speed of rendering. Experimental results show that, in addition to reasonable fidelity and faster rendering speed, the presented algorithm can obtain multiple levels of detail on the off-the-shelf graphic hardware.

GPU-ACCELERATED INTERACTIVE VISUALIZATION OF 3D VOLUMETRIC DATA USING CUDA

2013 ◽  
Vol 13 (02) ◽  
pp. 1340003 ◽  
Author(s):  
PIYUSH KUMAR ◽  
ANUPAM AGRAWAL
Keyword(s):  
Ct Scan ◽  
Volume Rendering ◽  
Large Scale ◽  
Graphics Processing Unit ◽  
Three Dimensional ◽  
Processing Unit ◽  
Volume Data ◽  
Scan Data ◽  
Visualization Techniques ◽  
The Cost

Improving the image quality and the rendering speed have always been a challenge to the programmers involved in large scale volume rendering especially in the field of medical image processing. The paper aims to perform volume rendering using the graphics processing unit (GPU), in which, with its massively parallel capability has the potential to revolutionize this field. This work is now better with the help of GPU accelerated system. The final results would allow the doctors to diagnose and analyze the 2D computed tomography (CT) scan data using three dimensional visualization techniques. The system is used in multiple types of datasets, from 10 MB to 350 MB medical volume data. Further, the use of compute unified device architecture (CUDA) framework, a low learning curve technology, for such purpose would greatly reduce the cost involved in CT scan analysis; hence bring it to the common masses. The volume rendering has been done on Nvidia Tesla C1060 (there are 240 CUDA cores, which provides execution of data parallely) card and its performance has also been benchmarked.

Modeling and volume rendering approach of underwater three-dimensional acoustic energy field

2016 ◽  
Vol 07 (03) ◽  
pp. 1650019
Author(s):  
Yanyang Zeng ◽  
Panpan Jia
Keyword(s):  
Real Time ◽  
Volume Rendering ◽  
Underwater Acoustics ◽  
Three Dimensional ◽  
Texture Mapping ◽  
Propagation Model ◽  
Acoustic Energy ◽  
Frame Rate ◽  
Volume Data ◽  
Energy Field

The underwater acoustics is primary and most effective method for underwater object detection and the complex underwater acoustics battlefield environment can be visually described by the three-dimensional (3D) energy field. Through solving the 3D propagation models, the traditional underwater acoustics volume data can be obtained, but it is large amount of calculation. In this paper, a novel modeling approach, which transforms two-dimensional (2D) wave equation into 2D space and optimizes energy loss propagation model, is proposed. In this way, the information for the obtained volume data will not be lost too much. At the same time, it can meet the requirements of data processing for the real-time visualization. In the process of volume rendering, 3D texture mapping methods is used. The experimental results are evaluated on data size and frame rate, showing that our approach outperforms other approaches and the approach can achieve better results in real time and visual effects.

Parallel Algorithm for DC Resistivity Method Based on CUDA-Enabled GPU

2013 ◽  
Vol 765-767 ◽  
pp. 1752-1756 ◽  
Author(s):  
Tuo Wang ◽  
Rui Chen
Keyword(s):  
Computer Architecture ◽  
Large Scale ◽  
Graphic Processing Unit ◽  
Three Dimensional ◽  
Parallel Computer ◽  
Processing Unit ◽  
The Finite Element Method ◽  
Electrical Method ◽  
Resistivity Method ◽  
Electrical Prospecting

During the electrical prospecting, the three-dimensional forward problem has been the hot topic in the research of DC electrical method. When the forward computation results are solved through the finite element method and the finite difference method, a large-scale sparse linear equation set should be obtained, moreover the computation in the solution of large-scale linear algebraic equation sets are very heavy. If we adopt serial computation, the computing efficiency is very low, which greatly affects the application efficiency. With the increasing maturation of the parallel computer architecture, the Graphic Processing Unit (GPU) parallel computing modeling can apply in this field to enable the efficiency of the three-dimensional forward modeling to be significantly improved.

scholarly journals Whole Three-Dimensional Dosimetry of Carbon Ion Beams with an MRI-Based Nanocomposite Fricke Gel Dosimeter Using Rapid T1 Mapping Method

Gels ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. 233
Author(s):  
Shinya Mizukami ◽  
Yusuke Watanabe ◽  
Takahiro Mizoguchi ◽  
Tsutomu Gomi ◽  
Hidetake Hara ◽  
...  
Keyword(s):  
Dose Distribution ◽  
Three Dimensional ◽  
Heavy Ion ◽  
Mapping Method ◽  
Ion Beams ◽  
Volume Data ◽  
Gel Dosimeter ◽  
Scanning Time ◽  
Contrast Reduction

MRI-based gel dosimeters are attractive systems for the evaluation of complex dose distributions in radiotherapy. In particular, the nanocomposite Fricke gel dosimeter is one among a few dosimeters capable of accurately evaluating the dose distribution of heavy ion beams. In contrast, reduction of the scanning time is a challenging issue for the acquisition of three-dimensional volume data. In this study, we investigated a three-dimensional dose distribution measurement method for heavy ion beams using variable flip angle (VFA), which is expected to significantly reduce the MRI scanning time. Our findings clarified that the whole three-dimensional dose distribution could be evaluated within the conventional imaging time (20 min) and quality of one cross-section.

scholarly journals A Distributed GPU-Based Framework for Real-Time 3D Volume Rendering of Large Astronomical Data Cubes

2012 ◽  
Vol 29 (3) ◽  
pp. 340-351 ◽  
Author(s):  
A. H. Hassan ◽  
C. J. Fluke ◽  
D. G. Barnes
Keyword(s):  
Real Time ◽  
Volume Rendering ◽  
Graphics Processing Units ◽  
Three Dimensional ◽  
Data Cube ◽  
Processing Unit ◽  
Ray Casting ◽  
Data Cubes ◽  
Central Processing ◽  
3D Volume

AbstractWe present a framework to volume-render three-dimensional data cubes interactively using distributed ray-casting and volume-bricking over a cluster of workstations powered by one or more graphics processing units (GPUs) and a multi-core central processing unit (CPU). The main design target for this framework is to provide an in-core visualization solution able to provide three-dimensional interactive views of terabyte-sized data cubes. We tested the presented framework using a computing cluster comprising 64 nodes with a total of 128 GPUs. The framework proved to be scalable to render a 204 GB data cube with an average of 30 frames per second. Our performance analyses also compare the use of NVIDIA Tesla 1060 and 2050 GPU architectures and the effect of increasing the visualization output resolution on the rendering performance. Although our initial focus, as shown in the examples presented in this work, is volume rendering of spectral data cubes from radio astronomy, we contend that our approach has applicability to other disciplines where close to real-time volume rendering of terabyte-order three-dimensional data sets is a requirement.

Detection, Averaging, and 3D Reconstruction of Biological Specimens on Hypercubes (Transputer-based) Computers

1990 ◽  
Vol 48 (1) ◽  
pp. 454-455
Author(s):  
Jose-Maria Carazo ◽  
I. Benavides ◽  
S. Marco ◽  
J.L. Carrascosa ◽  
E.L. Zapata
Keyword(s):  
3D Reconstruction ◽  
3D Structure ◽  
Three Dimensional ◽  
Software Tools ◽  
Mapping Method ◽  
Computer Architectures ◽  
Parallel Computer ◽  
Reconstruction Process ◽  
Computing Power ◽  
Order Of Magnitude

Obtaining the three-dimensional (3D) structure of negatively stained biological specimens at a resolution of, typically, 2 - 4 nm is becoming a relatively common practice in an increasing number of laboratories. A combination of new conceptual approaches, new software tools, and faster computers have made this situation possible. However, all these 3D reconstruction processes are quite computer intensive, and the middle term future is full of suggestions entailing an even greater need of computing power. Up to now all published 3D reconstructions in this field have been performed on conventional (sequential) computers, but it is a fact that new parallel computer architectures represent the potential of order-of-magnitude increases in computing power and should, therefore, be considered for their possible application in the most computing intensive tasks.We have studied both shared-memory-based computer architectures, like the BBN Butterfly, and local-memory-based architectures, mainly hypercubes implemented on transputers, where we have used the algorithmic mapping method proposed by Zapata el at. In this work we have developed the basic software tools needed to obtain a 3D reconstruction from non-crystalline specimens (“single particles”) using the so-called Random Conical Tilt Series Method. We start from a pair of images presenting the same field, first tilted (by ≃55°) and then untilted. It is then assumed that we can supply the system with the image of the particle we are looking for (ideally, a 2D average from a previous study) and with a matrix describing the geometrical relationships between the tilted and untilted fields (this step is now accomplished by interactively marking a few pairs of corresponding features in the two fields). From here on the 3D reconstruction process may be run automatically.

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