Visualization and analysis of diffusion tensor fields

technical reportThe power of medical imaging modalities to measure and characterize biological tissue is amplified by visualization and analysis methods that help researchers to see and understand the structures within their data. Diffusion tensor magnetic resonance imaging can measure microstructural properties of biological tissue, such as the coherent linear organization of white matter of the central nervous system, or the fibrous texture of muscle tissue. This dissertation describes new methods for visualizing and analyzing the salient structure of diffusion tensor datasets. Glyphs from superquadric surfaces and textures from reactiondiffusion systems facilitate inspection of data properties and trends. Fiber tractography based on vector-tensor multiplication allows major white matter pathways to be visualized. The generalization of direct volume rendering to tensor data allows large-scale structures to be shaded and rendered. Finally, a mathematical framework for analyzing the derivatives of tensor values, in terms of shape and orientation change, enables analytical shading in volume renderings, and a method of feature detection important for feature-preserving filtering of tensor fields. Together, the combination of methods enhances the ability of diffusion tensor imaging to provide insight into the local and global structure of biological tissue

Curvature-based transfer functions for direct volume rendering: methods and applications

Journal ArticleDirect volume rendering of scalar fields uses a transfer function to map locally measured data properties to opacities and colors. The domain of the transfer function is typically the one-dimensional space of scalar data values. This paper advances the use of curvature information in multi-dimensional transfer functions, with a methodology for computing high-quality curvature measurements. The proposed methodology combines an implicit formulation of curvature with convolution-based reconstruction of the field. We give concrete guidelines for implementing the methodology, and illustrate the importance of choosing accurate filters for computing derivatives with convolution. Curvature-based transfer functions are shown to extend the expressivity and utility of volume rendering through contributions in three different application areas: nonphotorealistic volume rendering, surface smoothing via anisotropic diffusion, and visualization of isosurface uncertainty

Stream bundles - cohesive advection through flow fields

Journal ArticleStreamline advection has proven an effective method for visualizing vector flow field data. Traditional streamlines do not, however, provide for investigating the coarsergrained features of complex datasets, such as the white matter tracts in the brain or the thermal conveyor belts in the ocean. In this paper, we introduce a cohesive advection primitive, called a stream bundle. Whereas traditional streamlines describe the advection patterns of single, infinitesimal micro-particles, stream bundles indicate advection paths for large macro-particles. Implementationally, stream bundles are composed of a collection of individual streamlines (here termed fibers), each of which only advects a short distance before being terminated and re-seeded in a new location. The individual fibers combine to dictate the instantaneous distribution of the bundle, and it is this collective distribution which is used in determining where fibers are reseeded. By carefully controlling the termination and re-seeding policies of the fibers, we can prevent the bundle from becoming frayed in divergent regions. By maintaining a cohesive from, the bundles can indicate the coarse structure of complex vector fields. In this paper, we use stream bundles to investigate the oceanic currents

Interactive simulation and visualization

Journal ArticleMost of us perform data analysis and visualization only after everything else is finished, which often means that we don't discover errors invalidating the results of our simulation until postprocessing. A better approach would be to improve the integration of simulation and visualization into the entire process so that you can make adjustments along the way. We call this approach computational steering. Computational steering is the capacity to control all aspects of the computational science pipeline-the succession of steps required to solve computational science and engineering problems. When you interactively explore a simulation in time and space, you steer it. In this sense, you can rely on steering to assist in debugging and to modify the computational aspects of your application

Constrained Inverse Volume Rendering for Planetary Nebulae

Determining the three-dimensional structure of distant astronomical objects is a challenging task, given that terrestrial observations provide only one viewpoint. For this task, bipolar planetary nebulae are interesting objects of study because of their pronounced axial symmetry due to fundamental physical processes. Making use of this symmetry constraint, we present a technique to automatically recover the axisymmetric structure of bipolar planetary nebulae from two-dimensional images. With GPU-based volume rendering driving a non-linear optimization, we estimate the nebula 's local emission density as a function of its radial and axial coordinates, and we recover the orientation of the nebula relative to Earth. The optimization refines the nebula model and its orientation by minimizing the differences between the rendered image and the original astronomical image. The resulting model enables realistic 3D visualizations of planetary nebulae, e.g. for educational purposes in planetarium shows. In addition, the recovered spatial distribution of the emissive gas allows validating computer simulation results of the astrophysical formation processes of planetary nebulae

Local white matter geometry from diffusion tensor gradients

We introduce a mathematical framework for computing geometrical properties of white matter fibres directly from diffusion tensor fields. The key idea is to isolate the portion of the gradient of the tensor field corresponding to local variation in tensor orientation, and to project it onto a coordinate frame of tensor eigenvectors. The resulting eigenframe-centered representation then makes it possible to define scalar indices (or measures) that describe the local white matter geometry directly from the diffusion tensor field and its gradient, without requiring prior tractography. We derive new scalar indices of (1) fibre dispersion and (2) fibre curving, and we demonstrate them on synthetic and in vivo data. Finally, we illustrate their applicability to a group study on schizophrenia