The Tomviz project is developing a cross platform, open source application for the processing, visualization, and analysis of 3D tomographic data. It features a complete pipeline capable processing data from alignment, reconstruction, and segmentation through to displaying, visualizing, and interacting with 3D reconstructions of tomographic data. Many of the data operators are available as editable Python scripts that can be modified in the interface to experiment with different techniques. The pipeline can be saved to disk, and a number of common file formats are supported for importing and exporting data.
The Tomviz project was founded by Marcus D. Hanwell and Utkarsh Ayachit at Kitware, David A. Muller (Cornell University), and Robert Hovden (University of Michigan), funded by DOE Office of Science contract DE-SC0011385.
We recommend downloading the current stable release, but also provide nightly binaries built by our dashboards for Windows, macOS, and Linux.
Windows: Follow the installation instructions, double-click on the Tomviz icon to launch the application. macOS: After downloading the package double-click to begin installation. Drag the Tomviz icon into your Applications directory – or anywhere else you would like to store it. Double-click on the icon to open it, nightly builds will require right-clicking and selecting open. Linux: A binary (tar.gz) is provided, or it can be built from source. See instructions for building found in the BUILDING.md document.
- Open a sample dataset by clicking “Sample Menu > Reconstruction ” at the top menubar.
- Create a 3D volumetric visualization by clicking “Visualization > Volume” at the top menubar.
- Interact with your volume in the center panel titled “RenderView”.
Start by watching this short video to see Tomviz in action.
Also Tomviz user guide has more detailed information to get started.
When using tomviz in your research, please cite:
- A Simple Preparation Method for Full-Range Electron Tomography of Nanoparticles and Fine Powders, E. Padgett et al., Microsc. & Microanalys. (2017)
- Physical Confinement Promoting Formation of Cu2O–Au Heterostructures with Au Nanoparticles Entrapped within Crystalline Cu2O Nanorods, E. Asenath-Smith et al., Chem. Mater. (2017)
- Nanomaterial datasets to advance tomography in scanning transmission electron microscopy, B. Levin et al., Nature Scientific Data (2016)
- Graphene kirigami, M.K. Blees et al., Nature (2015)
Our project uses GitHub for code review, please fork the project and make a pull request if you would like us to consider your patch for inclusion.
