TomoPhantom Save

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.

NEW! VERSION 3.0

TomoPhantom has been refactored, please see changes. Everyone is welcome to Documentation page.

What TomoPhantom can do:

• 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).

Installation:

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.

Related software projects on GitHub:

• 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.

References:

[1] D. Kazantsev et al. 2018, TomoPhantom, a software package to generate 2D-4D analytical phantoms for CT image reconstruction algorithm benchmarks, Software X, Volume 7, January–June 2018, Pages 150–155

[2] D. Kazantsev, V. Pickalov "New iterative reconstruction methods for fan-beam tomography", IPSE, 2017

Applications:

• 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]