Interactive Multiscale Tensor Reconstruction for Multiresolution Volume Visualization (original) (raw)

Multiscale Tensor Approximation for Volume Data

2010

Advanced 3D microstructural analysis in natural sciences and engineering depends ever more on modern data acquisition and imaging technologies such as micro-computed or synchrotron tomography and interactive visualization. The acquired high-resolution volume data sets have sizes in the order of tens to hundreds of GBs, and typically exhibit spatially complex internal structures. Such large structural volume data sets represent a grand challenge to be explored, analyzed and interpreted by means of interactive visualization, since the amount of data to be rendered is typically far beyond the current performance limits of interactive graphics systems. As a new approach to tackle this bottleneck problem, we employ higher-order tensor approximations (TAs). We demonstrate the power of TA to represent, and focus on, structural features in volume data. We show that TA yields a high data reduction at competitive rate distortion and that, at the same time, it provides a natural means for mult...

Fast and Memory Efficient GPU-based Rendering of Tensor Data

Graphics hardware is advancing very fast and offers new possibilities to programmers. The new features can be used in scientific visualization to move calculations from the CPU to the graphics processing unit (GPU). This is useful especially when mixing CPU intense calculations with on the fly visualization of intermediate results. We present a method to display a large amount of superquadric glyphs and demonstrate its use for visualization of measured second--order tensor data in diffusion tensor imaging (DTI) and to stress and strain tensors of computational fluid dynamic and material simulations.

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.

TRex: Interactive Texture Based Volume Rendering for Extremely Large Datasets

IEEE Computer Graphics and Applications, 2001

Many of today's scientific simulations are capable of producing ter- abytes to petabytes of data. Visualization plays a critical role in understanding and analyzing the results of these simulations. Hard- ware accelerated direct volume rendering has proven to be an ex- cellent visualization modality for both scientific and medical data sets. Current graphics hardware implementations impose limits on interactive data

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.

Application of Tensor Approximation to Multiscale Volume Feature Representations

Advanced 3D microstructural analysis in natural sciences and engineering depends ever more on modern data acquisition and imaging technologies such as micro-computed or synchrotron tomography and interactive visualization. The acquired volume data sets are not only of high-resolution but in particular exhibit complex spatial structures at different levels of scale (e.g. variable spatial expression of multiscale periodic growth structures in tooth enamel). Such highly structured volume data sets represent a tough challenge to be analyzed and explored by means of interactive visualization due to the amount of raw volume data to be processed and filtered for the desired features. As an approach to address this bottleneck by multiscale feature preserving data reduction, we propose higher-order tensor approximations (TAs). We demonstrate the power of TA to represent, and highlight the structural features in volume data. We visually and quantitatively show that TA yields high data reducti...

Real-time visualization of large volume datasets on standard PC hardware

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.

GPU-accelerated direct volume rendering of finite element data sets

2012

Direct Volume Rendering of Finite Element models is challenging since the visualisation process is performed in world coordinates, whereas data fields are usually defined over the elements' material coordinate system. In this paper we present a framework for Direct Volume Rendering of Finite Element models. We present several novel implementations visualising Finite Element data directly without requiring resampling into world coordinates. We evaluate the methods using several biomedical Finite Element models. Our GPU implementation of ray-casting in material coordinates using depth peeling is several orders of magnitude faster than the corresponding CPU approach, and our new ray interpolation approach achieves near interactive frame rates for high-order finite element models at high resolutions.

Hierarchical Visualization and Compression of Large Volume Datasets Using GPU Clusters

2004

We describe a system for the texture-based direct volume visualization of large data sets on a PC cluster equipped with GPUs. The data is partitioned into volume bricks in object space, and the intermediate images are combined to a final picture in a sort-last approach. Hierarchical wavelet compression is applied to increase the effective size of volumes that can be handled. An adaptive rendering mechanism takes into account the viewing parameters and the properties of the data set to adjust the texture resolution and number of slices. We discuss the specific issues of this adaptive and hierarchical approach in the context of a distributed memory architecture and present solutions for these problems. Furthermore, our compositing scheme takes into account the footprints of volume bricks to minimize the costs for reading from framebuffer, network communication, and blending. A detailed performance analysis is provided and scaling characteristics of the parallel system are discussed. F...