Browsing by Author "Khan, Fahim Hasan"

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    Comparative Visualizations of Noisy and Filtered Blood Flow from 4D PC-MRI Cardiac Datasets
    (2017-10) Khan, Fahim Hasan; Rocha, Allan; Alim, Usman
    Modern phase-contrast magnetic resonance imaging (PC-MRI) can acquire both cardiac anatomy and flow function in a single acquisition and deliver high quality volumetric and time-varying (4D) datasets which enable better diagnosis and risk assessment of various cardiovascular diseases. A good way to visualize blood flow from 4D PC-MRI datasets is to use animated pathlines through the anatomical context for representing the trajectories of the blood particles. Artifact correction is one crucial step in the processing pipeline of 4D PC-MRI datasets for representing the cardiac flow using pathlines, which in turn can reduce the overall quality of the useful information in the dataset. In this work, an approach is presented for comparative visualization of 4D PC-MRI datasets before and after artifact correction for qualitative analysis.
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    Superimposed Comparative Visualization of 3D and 4D Vector Fields
    (2018-04-26) Khan, Fahim Hasan; Alim, Usman; Costa Sousa, Mario; Johansen, Craig
    Comparative visualization of multiple vector fields is frequently needed in scientific visualization to analyze static (3D) or time-varying (4D) spatial data. Efficient tools and guidelines to do this effectively are however absent. Among many available methods for comparative visualization, only superimposition or overlay techniques can display two or more data instances at the same time in the same co-registered coordinate space. As a result, while it is most effective and has many advantages, it also suffers most from occlusion and cluttering issues when working with 3D and 4D data. In this work, we present a framework for superimposed co-visualization for comparing multiple vector fields effectually from 3D and 4D data. Our framework addresses the challenges of superimposed comparative visualization in two essential ways. We propose a seeding strategy using an adaptive hierarchical grid refinement algorithm combined with importance sampling that is based on an information-theoretic probability density function that combines aspects of multiple vector fields. Furthermore, we design hybrid visual representations combining streamlines and glyphs to render the visualization in an effectively less-occluded and clutter-free way while presenting only the information necessary for comparison. Several demonstration examples are presented for validating our framework.