Interactive ray tracing for volume visualization

Journal ArticleWe present a brute-force ray tracing system for interactive volume visualization, The system runs on a conventional (distributed) shared-memory multiprocessor machine. For each pixel we trace a ray through a volume to compute the color for that pixel. Although this method has high intrinsic computational cost, its simplicity and scalability make it ideal for large datasets on current high-end parallel systems

Parallel lumigraph reconstruction

Journal ArticleThis paper presents three techniques for reconstructing Lumigraphs/ Lightfields on commercial ccNUMA parallel distributed shared memory computers. The first method is a parallel extension of the software-based method proposed in the Lightfield paper. This expands the ray/two-plane intersection test along the film plane, which effectively becomes scan conversion. The second method extends this idea by using a shear/warp factorization that accelerates rendering. The third technique runs on an SGI Reality Monster using up to eight graphics pipes and texture mapping hardware to reconstruct images. We characterize the memory access patterns exhibited using the hardware-based method and use this information to reconstruct images from a tiled UV plane. We describe a method to use quad-cubic reconstruction kernels. We analyze the memory access patterns that occur when viewing Lumigraphs. This allows us to ascertain the cost/benefit ratio of various tilings of the texture plane

Level set modeling and segmentation of diffusion tensor magnetic resonance imaging brain data

Segmentation of anatomical regions of the brain is one of the fundamental problems in medical image analysis. It is traditionally solved by iso-surfacing or through the use of active contours/deformable models on a gray-scale magnetic resonance imaging (MRI) data. We develop a technique that uses anisotropic diffusion properties of brain tissue available from diffusion tensor (DT)-MRI to segment brain structures. We develop a computational pipeline starting from raw diffusion tensor data through computation of invariant anisotropy measures to construction of geometric models of the brain structures. This provides an environment for user-controlled 3-D segmentation of DT-MRI datasets. We use a level set approach to remove noise from the data and to produce smooth, geometric models. We apply our technique to DT-MRI data of a human subject and build models of the isotropic and strongly anisotropic regions of the brain. Once geometric models have been constructed they can be combined to study spatial relationships and quantitatively analyzed to produce the volume and surface area of the segmented regions

Integrating component-based scientific computing software

Book ChapterIn recent years, component technology has been a successful methodology for large-scale commercial software development. Component technology combines a set of frequently used functions in a component and makes the implementation transparent to users. Software application developers typically connect a group of components from a component repository, connecting them to create a single application

Accelerated isosurface extraction in time-varying fields

Journal ArticleFor large time-varying data sets, memory and disk limitations can lower the performance of visualization applications. Algorithms and data structures must be explicitly designed to handle these data sets in order to achieve more interactive rates. The Temporal Branch-on-Need Octree (T-BON) extends the three-dimensional branch-on-need octree for time-varying isosurface extraction. This data structure minimizes the impact of the I/O bottleneck by reading from disk only those portions of the search structure and data necessary to construct the current isosurface

Memory sharing for interactive ray tracing on clusters

ManuscriptWe present recent results in the application of distributed shared memory to image parallel ray tracing on clusters. Image parallel rendering is traditionally limited to scenes that are small enough to be replicated in the memory of each node, because any processor may require access to any piece of the scene. We solve this problem by making all of a cluster's memory available through software distributed shared memory layers. With gigabit ethernet connections, this mechanism is sufficiently fast for interactive rendering of multi-gigabyte datasets. Object- and page-based distributed shared memories are compared, and optimizations for efficient memory use are discussed

Ray Tracing Structured AMR Data Using ExaBricks

Structured Adaptive Mesh Refinement (Structured AMR) enables simulations to adapt the domain resolution to save computation and storage, and has become one of the dominant data representations used by scientific simulations; however, efficiently rendering such data remains a challenge. We present an efficient approach for volume- and iso-surface ray tracing of Structured AMR data on GPU-equipped workstations, using a combination of two different data structures. Together, these data structures allow a ray tracing based renderer to quickly determine which segments along the ray need to be integrated and at what frequency, while also providing quick access to all data values required for a smooth sample reconstruction kernel. Our method makes use of the RTX ray tracing hardware for surface rendering, ray marching, space skipping, and adaptive sampling; and allows for interactive changes to the transfer function and implicit iso-surfacing thresholds. We demonstrate that our method achieves high performance with little memory overhead, enabling interactive high quality rendering of complex AMR data sets on individual GPU workstations

3D modeling and segmentation of diffusion weighted MRI data

Diffusion weighted magnetic resonance imaging (DW MRI) is a technique that measures the diffusion properties of water molecules to produce a tensor-valued volume dataset. Because water molecules can diffuse more easily along fiber tracts, for example in the brain, rather than across them, diffusion is anisotropic and can be used for segmentation. Segmentation requires the identification of regions with different diffusion properties. In this paper we propose a new set of rotationally invariant diffusion measures which may be used to map the tensor data into a scalar representation. Our invariants may be rapidly computed because they do not require the calculation of eigenvalues. We use these invariants to analyze a 3D DW MRI scan of a human head and build geometric models corresponding to isotropic and anisotropic regions. We then utilize the models to perform quantitative analysis of these regions, for example calculating their surface area and volume