3D Visulization of Volumetric Data
angles ( x - y -angle). Although tomographic volume data is the most directly interpretable, tomviz is capable of visualizing any type of 3D data cube.
Loading volumetric data into tomviz . In tomviz , users can load a variety of datatypes stored as a TIFF, PNG, raw binary data, MRC, or HDF5 under “File > Open” in the top menubar. For convenience, tomviz comes packaged with an example dataset, a “Star Nanoparticle (Reconstruction),” so users can immediately explore diff erent visualization techniques. T e dataset contains the 3D structure of a Co 2 P hyperbranched nanoparticle; it can be opened from the menubar under “Sample Data.” For further exploration of visualization techniques, fi ve high-quality electron tomography datasets from the peer-reviewed literature have been made publicly available for researchers to use (Levin et. al., 2016). T e datasets can be accessed under “Sample Datasets > Download More Datasets,” or they can be downloaded at https://
dx.doi.org/10.6084/m9.fi gshare.c.2185342 . T e tomviz user interface . New visualizations can be added from the Visualization menu or by clicking an icon from the Toolbar:
All tomviz visualizations are interactive 3D objects that can be rotated by clicking and dragging in the render window(s). T e dataset and its associated visualizations—an outline , a
volume , and a slice in the case of Figure 1 —are listed in the upper leſt -hand Pipelines ( Figure 1a ). As the user adds new visualization modules to a dataset, these will be displayed in the pipeline for that dataset. Each dataset that is opened in tomviz is assigned its own pipeline. T is allows the user to easily keep track of all changes made to every dataset. Further properties for each selected visualization module or dataset are shown in the lower leſt -hand Properties Panel ( Figure 1b ). In addition to a suite of visualization tools—the subject of this tutorial—3D data processing is also integrated into the tomviz user interface. A Python console ( Figure 1c ) can be opened from “Tools > Python” Console under the top menubar. Users can execute pre-written python scripts or create their own code using numpy, scipy, or ITK. T e Data Transform and Tomography menus contain pre-written algorithms for image processing, alignment, and tomographic reconstruction of raw electron microscopy data. Aſt er these scripts are executed in Python, they are sent back to tomviz for immediate visualization. Data transforms appear in the pipeline in the order in which they are executed. Double- clicking on a transform listed in the pipeline opens a new window displaying the Python code used to implement the transformation, which the user may edit if desired. T e tomviz pipeline uniquely preserves all steps for reproducible workfl ow. Aſt er a dataset is loaded, a histogram panel appears
( Figure 1d ). Colors in the histogram represent a colormap that quantitatively correlates colors in a visualization to corresponding
Figure 1 : The tomviz graphical user interface for 3D visualization of tomographic data. Once a dataset has been loaded, the data pipeline is populated (a). A variety of visualization types are available to be used in combination for analysis—with parameters specifi ed in the module properties panel (b). A Python interface offers advanced scientifi c processing of data (c). A histogram of voxel intensities is displayed top center (d). The line across the histogram represents the opacity map and can be altered interactively. Here a reconstruction of porous PtCu nanoparticles is visualized using a volume render (e), a non-orthogonal slice through one particle (f), and a contour surface (g). Tabs, and divisions within a tab, allow multiple simultaneous renderings and camera angles.
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