Time-varying volume visualization

Computer Graphics Forum, 2010

The selection of an appropriate global transfer function is essential for visualizing time-varying simulation data. This is especially challenging when the global data range is not known in advance, as is often the case in remote and in-situ visualization settings. Since the data range may vary dramatically as the simulation progresses, volume rendering using local transfer functions may not be coherent for all time steps. We present an exploratory technique that enables coherent classification of time-varying volume data. Unlike previous approaches, which require pre-processing of all time steps, our approach lets the user explore the transfer function space without accessing the original 3D data. This is useful for interactive visualization, and absolutely essential for in-situ visualization, where the entire simulation data range is not known in advance. Our approach generates a compact representation of each time step at rendering time in the form of ray attenuation functions, which are used for subsequent operations on the opacity and color mappings. The presented approach offers interactive exploration of time-varying simulation data that alleviates the cost associated with reloading and caching large data sets.

2006

This paper presents a parallel rendering approach that allows high-quality visualization of large time-varying volume datasets. Multiresolution and adaptive-resolution techniques are also incorporated to improve the efficiency of the rendering. Three basic steps are needed to implement this kind of an application. First we divide the task through decomposition of data. This decomposition can be either temporal or spatial or a mix of both. After data has been divided, each of the data portions is rendered by a separate processor to create sub-images or frames. Finally these sub-images or frames are assembled together into a final image or animation. After developing this application, several experiments were performed to show that this approach indeed saves time when a reasonable number of processors are used. Also, we conclude that the optimal number of processors is dependent on the size of the dataset used.

Wscg, 2002

The differential volume rendering method is a ray casting based method for time-varying-volume-data. In the differential volume rendering method, the changed fractions of volume data between consecutive time steps are extracted to form differential files. Based on the differential files, only the changed pixels, instead of all the pixels in the image, are updated by casting new rays at the positions in each time step. The main overhead of the differential volume rendering method is to determine the changed pixel positions before casting new rays for the changed pixels. In this paper, we propose a two-level differential volume rendering method, which is a modified differential volume rendering method with faster determination of the changed pixel positions. In the proposed method, the determination of the changed pixel positions is accelerated by the aid of second-order-difference. Since voxels in two consecutive differential files may partially overlap in the space, the computation spent on determining the changed pixel positions due to the overlapped area is redundant. We use this property to extract the difference of changed voxel positions between consecutive differential files to form the second-order-difference. Based on the second-order-difference, the changed pixel positions can be determined efficiently. The experimental results show that the proposed method outperforms the differential volume rendering method for all test datasets.

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.

Computers & Graphics, 2004

In this paper, we present a novel method to meet the time-critical requirement in rendering time-varying volume data. In time-critical rendering, the rendering is demanded to be completed in a given time budget. Our approach is modified from the differential volume rendering, which updates only the changed pixels instead of all pixels on the image plane. The level of modification (LOM) is used to measure the degree of modification of the changed data between consecutive time steps. To meet the time-critical requirement, the presented method chooses the most important changed pixels to be updated. The experimental results show that our method is very good in both controlling the rendering time to meet the time constraint and preserving the important features of a data set within a limited time budget.

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.

Journal of Visual Languages & Computing, 2003

The differential volume rendering method is a ray casting based method for time-varying volume data. In the differential volume rendering method, the changed voxels between consecutive time steps are extracted to form differential files in advance. When the dataset is to be rendered, changed voxels are projected onto the image plane to determine the positions of changed pixels. Only the changed pixels, instead of all pixels on the image, are updated by casting new rays in each time step. The main overhead of the differential volume rendering method is the determination of changed pixels. In this paper, we propose a two-level differential volume rendering method, in which the determination of changed pixels is accelerated by the aid of the second-order difference. Since changed voxels in two consecutive differential files may partially overlap in the space, the projection computation spent on the overlapped area is redundant. We use this property to extract the difference of changed voxels between consecutive differential files to form the second-order difference. Based on the second-order difference, the changed pixels can be determined more efficiently. The experimental results show that the proposed method outperforms the comparative methods for all test datasets in most cases. In addition, the rendering time can be predicted once the data files are loaded in each time step.

2006

Abstract Interactive visualization of time-varying volume data is essential for many scientific simulations. This is a challenging problem since this data is often large, can be organized in different formats (regular or irregular grids), with variable instances of time (from few hundreds to thousands) and variable domain fields.

International Journal of Imaging Systems and Technology, 2000

With the advent of high-powered, commodity volume visualization hardware comes a new challenge: effectively harnessing the visualization power to enable greater understanding of data through dynamic interaction. We examine Cube-4/VolumePro as the latest advance in real-time volume visualization hardware. We describe tools to utilize this hardware including a software developers' kit, called the Cube Toolkit (CTK). We show how the CTK supports algorithms such as perspective rendering, overlapping volumes, and geometry mixing within volumes. We examine a case study of a virtual colonoscopy application developed using the CTK.

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.