Tutorial on the Visualization of Volumetric Data Using tomviz
Barnaby D.A. Levin , 1 , 2 * Yi Jiang , 3 Elliot Padgett , 1 Shawn Waldon , 4 Cory Quammen , 4 Chris Harris , 4 Utkarsh Ayachit , 4 Marcus Hanwell , 4 Peter Ercius , 5 David A. Muller , 1 , 6 and
Robert Hovden 7 1 School of Applied and Engineering Physics , 271 Clark Hall , Cornell University , Ithaca , NY 14853 2 School for Engineering of Matter , Transport and Energy , 501 E. Tyler Mall , Arizona State University , Tempe , AZ 85287 3 Department of Physics , 109 Clark Hall , Cornell University , Ithaca , NY 14853 4 Kitware Inc. , 28 Corporate Dr. , #101 , Cliſt on Park , NY 12065 5 National Center for Electron Microscopy , Molecular Foundry , Lawrence Berkeley National Laboratory , One Cyclotron Rd. ,
Berkeley , CA 94720 6 Kavli Institute for Nanoscale Science , 420 Physical Sciences Building , Cornell University , Ithaca , NY 14853 7 Department of Materials Science and Engineering , 2300 Hayward St. , University of Michigan , Ann Arbor , MI 48109
*
blevin2@asu.edu
Abstract: Tomography produces complex volumetric datasets containing the entire internal structure and density of an object in three dimensions (3D). Interpreting volumetric data requires 3D visualization but needs specialized software distinguishable from more familiar tools used in animation for 3D surface data. This tutorial reviews 3D visualization techniques for volumetric data using the open-source tomviz software package. A suite of tools including two-dimensional (2D) slices, surface contours, and full volume rendering provide quantitative and qualitative analysis of volumetric information. The principles outlined here are applicable to a wide range of 3D tomography techniques and can be applied to volumetric datasets beyond materials characterization.
Keywords: 3D visualization, tomography, electron microscopy, open science, tomviz
Introduction T is article is a short tutorial on the principles of visual- izing complex 3D volumetric datasets, demonstrated using tomviz . Across a diverse range of scientifi c disciplines, understanding the 3D internal structure of material and biological specimens is essential for scientifi c progress. T ere are numerous methods for characterizing the 3D internal structure of objects at diff erent length scales, including X-ray computed tomography [ 1 ], transmission electron microscopy (TEM) tomography [ 2 , 3 ], scanning transmission electron microscopy (STEM) tomography [ 4 ], focused ion beam– scanning electron microscopy (FIB-SEM) tomography [ 5 ], and atom-probe tomography [ 6 , 7 ]. Whatever the application, or the technique used, the volumetric datasets generated by these methods require visualization. Data visualization adds more than just aesthetic value to scientifi c research. High-quality and interpretable visualization is essential for extracting meaningful information from complex 3D structures. Visualizing 3D volumetric datasets requires specialized
soſt ware, distinguishable from the more familiar 3D visual- ization of wireframes (used in animated cinema) or molecular coordinates (crystallography). T is specialized soſt ware requires interactive volume rendering tools, surface rendering tools, and the ability to display cross sections through the volume—at a minimum. Visualizations should be reproducible and shareable, enabling other scientists to inspect and validate data as well as understand the steps taken to turn the raw data
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into the fi nal result. Ultimately, the soſt ware should be able to produce publication quality fi gures for scientifi c articles. T e open-source tomviz soſt ware package aims to meet each of these criteria, whilst also being free to download and use. Tomviz , can be run on Windows, Mac OS, or Linux operating systems and is available for download at
www.tomviz. org . Tomviz can be run on a laptop and can leverage powerful GPUs for visualizing large, intricate datasets. We use tomviz to illustrate this tutorial, but the principles outlined apply to other soſt ware packages, including the free non-commercial Chimera (University of California at San Francisco) and various commercial soſt ware packages. In the following sections, we describe the format of 3D datasets and several techniques that can be used to visualize the data.
Defi nitions and the User Interface 3D datasets . A data cube refers to any 3D (or higher) array of values—such as a stack of 2D black-and-white images representing a spatial volume. Unlike crystallographic and surface data, data cubes grow rapidly—a 1024 × 1024 × 1024 data cube of 32-bit values occupies 4.29 gigabytes of memory. Each 3D element in the data cube is termed a voxel , analogous to a pixel in a 2D image. In tomography, each voxel in a data cube contains a value that represents intensity at a point ( x , y , z ) in the volume, which may relate to the composition or some other characteristic of the object at that point. For example, in medical X-ray computed tomography (CT), higher intensity represents denser material; a human head has brighter values at points where the hard skull is located than where soſt brain matter is located [ 1 , 8 ]. In nanoscale STEM tomography, high- Z gold nanoparticles appear brighter than low- Z silica nanopar- ticles [ 4 ]. Because intensity values are provided at all voxels, not just at material surfaces, tomography reveals the entire 3D internal structure of an object [ 1 ].
3D data cubes are not limited to spatial volumes. In a hyperspectral map the third dimension is spectroscopic ( x - y -wavelength, or x - y -energy) [ 9 ]. In a black-and-white movie, the third dimension is time ( x - y -time). In a tomographic tilt series, the data cube contains images at diff erent projection
doi: 10.1017/S1551929517001213
www.microscopy-today.com • 2018 January
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