Showing papers by "David H. Laidlaw published in 2021"

A Virtual Reality Memory Palace Variant Aids Knowledge Retrieval from Scholarly Articles

Abstract: We present exploratory research of virtual reality techniques and mnemonic devices to assist in retrieving knowledge from scholarly articles. We used abstracts of scientific publications to represent knowledge in scholarly articles; participants were asked to read, remember, and retrieve knowledge from a set of abstracts. We conducted an experiment to compare participants’ recall and recognition performance in three different conditions: a control condition without a pre-specified strategy to test baseline individual memory ability, a condition using an image-based variant of a mnemonic called a “memory palace,” and a condition using a virtual reality-based variant of a memory palace. Our analyses show that using a virtual reality-based memory palace variant greatly increased the amount of knowledge retrieved and retained over the baseline, and it shows a moderate improvement over the other image-based memory palace variant. Anecdotal feedback from participants suggested that personalizing a memory palace variant would be appreciated. Our results support the value of virtual reality for some high-level cognitive tasks and help improve future applications of virtual reality and visualization.

Tractography Processing with the Sparse Closest Point Transform

Abstract: We propose a novel approach for processing diffusion MRI tractography datasets using the sparse closest point transform (SCPT). Tractography enables the 3D geometry of white matter pathways to be reconstructed; however, algorithms for processing them are often highly customized, and thus, do not leverage the existing wealth of machine learning (ML) algorithms. We investigated a vector-space tractography representation that aims to bridge this gap by using the SCPT, which consists of two steps: first, extracting sparse and representative landmarks from a tractography dataset, and second transforming curves relative to these landmarks with a closest point transform. We explore its use in three typical tasks: fiber bundle clustering, simplification, and selection across a population. The clustering algorithm groups fibers from single whole-brain datasets using a non-parametric k-means clustering algorithm, with performance compared with three alternative methods and across four datasets. The simplification algorithm removes redundant curves to improve interactive visualization, with performance gauged relative to random subsampling. The selection algorithm extracts bundles across a population using a one-class Gaussian classifier derived from an atlas prototype, with performance gauged by scan-rescan reliability and sensitivity to normal aging, as compared to manual mask-based selection. Our results demonstrate how the SCPT enables the novel application of existing vector-space ML algorithms to create effective and efficient tools for tractography processing. Our experimental data is available online, and our software implementation is available in the Quantitative Imaging Toolkit.

Virtual and augmented reality: New tools for visualizing, analyzing, and communicating complex morphology.

Abstract: Virtual and augmented reality (VR/AR) are new technologies with the power to revolutionize the study of morphology. Modern imaging approaches such as computed tomography, laser scanning, and photogrammetry have opened up a new digital world, enabling researchers to share and analyze morphological data electronically and in great detail. Because this digital data exists on a computer screen, however, it can remain difficult to understand and unintuitive to interact with. VR/AR technologies bridge the analog-to-digital divide by presenting 3D data to users in a very similar way to how they would interact with actual anatomy, while also providing a more immersive experience and greater possibilities for exploration. This manuscript describes VR/AR hardware, software, and techniques, and is designed to give practicing morphologists and educators a primer on using these technologies in their research, pedagogy, and communication to a wide variety of audiences. We also include a series of case studies from the presentations and workshop given at the 2019 International Congress of Vertebrate Morphology, and suggest best practices for the use of VR/AR in comparative morphology.

Visualization of 3D Stress Tensor Fields Using Superquadric Glyphs on Displacement Streamlines

Abstract: Stress tensor fields play a central role in solid mechanics studies, but their visualization in 3D space remains challenging as the information-dense multi-variate tensor needs to be sampled in 3D space while avoiding clutter. Taking cues from current tensor visualizations, we adapted glyph-based visualization for stress tensors in 3D space. We also developed a testing framework and performed user studies to evaluate the various glyph-based tensor visualizations for objective accuracy measures, and subjective user feedback for each visualization method. To represent the stress tensor, we color encoded the original superquadric glyph, and in the user study, we compared it to superquadric glyphs developed for second-order symmetric tensors. We found that color encoding improved the user accuracy measures, while the users also rated our method the highest. We compared our method of placing stress tensor glyphs on displacement streamlines to the glyph placement on a 3D grid. In the visualization, we modified the glyph to show both the stress tensor and the displacement vector at each sample point. The participants preferred our method of glyph placement on displacement streamlines as it highlighted the underlying continuous structure in the tensor field.

Stick Stippling for Joint 3D Visualization of Diffusion MRI Fiber Orientations and Density

Abstract: We investigate a stick stippling approach for glyph-based visualization of neural fiber architecture derived from diffusion magnetic resonance imaging. The presence of subvoxel crossing fibers in the brain has prompted the development of advanced modeling techniques; however, there remains a need for improved visualization techniques to more clearly convey their complex structure. While tractography can illustrate large scale anatomy, visualization of diffusion models can provide a more complete picture of local anatomy without the known limitations of tracking. We identify challenges and evaluate techniques for visualizing multi-fiber models and identify benefits of a stick stippling technique relative to existing methods. We conducted experiments to compare these representations and evaluated them with in vivo diffusion MR datasets that vary in voxel resolution and anisotropy. We found that stick rendering as 3D tubes increased legibility of fiber orientation and that encoding fiber density by tube radius reduced clutter and reduced dependence on viewing orientation. Furthermore, we identified techniques to reduce the negative perceptual effects of voxel gridding through a jittering and resampling approach to produce a stippling effect. Looking forward, this approach provides a new way to explore diffusion MRI datasets that may aid in the visual analysis of white matter fiber architecture and microstructure. Our software implementation is available in the Quantitative Imaging Toolkit (QIT).