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TomoPhantom [1] is a toolbox to generate customisable 2D-4D phantoms (with a temporal capability) and their analytical tomographic projection data for parallel-beam geometry. It can be used for testing various tomographic reconstruction methods, as well as image processing methods, such as, denoising, deblurring, segmentation, and machine/deep learning tasks. |
TomoPhantom has been refactored, please see changes. Everyone is welcome to Documentation page.
TomoPhantom is recommended for various image processing tasks that require extensive numerical testing: image reconstruction, denoising, deblurring, etc. In particular, TomoPhantom is best-suited for testing various tomographic image reconstruction (TIR) methods. For TIR algorithms testing, the popular Shepp-Logan phantom is not always a good choice due to its piecewise-constant nature. This toolbox provides a simple modular approach to efficiently build customisable 2D-4D phantoms consisting of piecewise-constant, piecewise-smooth, and smooth analytical objects as well as their analytical Radon transforms .
- Generate 2D and 3D synthetic phantoms made of Gaussians, parabolas, ellipses, cones and rectangulars.
- Generate simple temporal extensions of 2D and 3D phantoms.
- Calculate analytical Radon transforms of 2D-4D models and also their numerical projections.
- Model a variety of tomographic data artefacts (noise models, zingers, rings, shifts, partial volume effect and others).
Tomophantom is distributed as a Python conda package for Linux/Windows/Mac OS's:
conda install -c httomo tomophantom
Please see more detailed information on Installation and development environments.
- xdesign XDesign is an open-source Python package for generating configurable simulation phantoms for benchmarking tomographic image reconstruction.
- syris Syris (synchrotron radiation imaging simulation) is a framework for simulations of X-ray absorption and phase contrast dynamic imaging experiments, like time-resolved radiography, tomography or laminography.
[2] D. Kazantsev, V. Pickalov "New iterative reconstruction methods for fan-beam tomography", IPSE, 2017
- TOmographic MOdel-BAsed Reconstruction (ToMoBAR)
- Joint image reconstruction method with correlative multi-channel prior for X-ray spectral computed tomography
- Deep learning segmentation of synthetic tomographic data using Pytorch U-net
Software related questions/comments please e-mail to Daniil Kazantsev at [email protected]