https://github.com/algotom/algotom
Data processing algorithms for tomography: https://algotom.readthedocs.io
https://github.com/algotom/algotom
grid-scanning-tomography half-acquisition-scan helical-scan image-processing image-reconstruction phase-contrast-imaging phase-unwrapping preprocessing-techniques ring-artifact-removal speckle-tracking synchrotron-based-tomography tomography x-ray-imaging
Last synced: 2 months ago
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Data processing algorithms for tomography: https://algotom.readthedocs.io
- Host: GitHub
- URL: https://github.com/algotom/algotom
- Owner: algotom
- License: apache-2.0
- Created: 2018-12-19T22:16:36.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2025-10-31T20:47:36.000Z (6 months ago)
- Last Synced: 2025-11-10T04:34:22.954Z (6 months ago)
- Topics: grid-scanning-tomography, half-acquisition-scan, helical-scan, image-processing, image-reconstruction, phase-contrast-imaging, phase-unwrapping, preprocessing-techniques, ring-artifact-removal, speckle-tracking, synchrotron-based-tomography, tomography, x-ray-imaging
- Language: Python
- Homepage: https://algotom.github.io
- Size: 71.3 MB
- Stars: 59
- Watchers: 7
- Forks: 21
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Algotom
### Data processing (**ALGO**)rithms for (**TOM**)ography.
 [](https://pepy.tech/project/algotom)      
**Algotom** is a Python package designed for processing tomographic data. It offers a
complete processing pipeline and workflow, including reading and writing data, pre-processing,
tomographic reconstruction, post-processing, data simulation, and calibration techniques.
The package provides numerous utility methods to assist users in constructing pipelines
for processing their own data or developing new methods.
Key features of Algotom include a wide range of processing methods such as artifact removal,
distortion correction, speckle-based phase-contrast imaging, and data reduction, as well as
the capability to process non-standard tomography acquisitions such as grid or helical scans.
The software stands out for its readability, minimal dependencies, and rich documentation.
Developed specifically for synchrotron-based tomographic beamlines, Algotom aims to maximize
data quality, enhance workflow throughput, and fully exploit beamline capabilities.
Features
--------
Algotom is a lightweight package with minimal dependencies. The software is built
on top of a few core Python libraries to ensure its ease-of-installation. Methods distributed in
Algotom have been developed and tested at synchrotron beamlines where massive
datasets are produced. This factor drives the methods developed to be easy-to-use,
robust, and practical. Algotom can be used on a normal computer to process large
tomographic data. Some featuring methods in Algotom are as follows:
- Methods in a full data processing pipeline: reading-writing data,
pre-processing, tomographic reconstruction, and post-processing.
- Methods for processing grid scans (or tiled scans) with the offset rotation-axis
to multiply double the field-of-view (FOV) of a parallel-beam tomography system.
These techniques enable high-resolution tomographic scanning of large samples.
- Methods for processing helical scans (with/without the offset rotation-axis).
- Methods for determining the center-of-rotation (COR) and auto-stitching images
in half-acquisition scans (360-degree acquisition with the offset COR).
- Practical methods developed and implemented for the package: zinger removal,
tilted sinogram generation, sinogram distortion correction, simplified form of Paganin's filter,
beam hardening correction, DFI (direct Fourier inversion) reconstruction,
FBP (filtered back-projection) reconstruction, BPF (back-projection filtering) reconstruction,
and double-wedge filter for removing sample parts larger than the FOV in a sinogram.
- Utility methods for [customizing ring/stripe artifact removal methods](https://algotom.readthedocs.io/en/latest/toc/section4/section4_3.html)
and parallelizing computational work.
- Calibration methods for helical scans and tomography alignment.
- Methods for generating simulation data: phantom creation, sinogram calculation
based on the Fourier slice theorem, and artifact generation.
- Methods for phase-contrast imaging: phase unwrapping, speckle-based phase retrieval, image correlation, and image alignment.
- Methods for downsampling, rescaling, and reslicing (+rotating, cropping)
3D reconstructed image without large memory usage.
- Direct vertical reconstruction for single slice, multiple slices, and multiple slices at
different orientations.
Installation
------------
- https://algotom.readthedocs.io/en/latest/toc/section3.html
- If users install Algotom to an existing environment and Numba fails to install due to the latest Numpy:
+ Downgrade Numpy and install Algotom/Numba again.
+ Create a new environment and install Algotom first, then other packages.
+ Use conda instead of pip.
- Avoid using the latest version of Python or Numpy as the Python ecosystem taking time to keep up with these twos.
Usage
-----
- https://algotom.readthedocs.io/en/latest/toc/section4/section4_5.html
- https://algotom.readthedocs.io/en/latest/toc/section1/section1_4.html
- https://algotom.readthedocs.io/en/latest/toc/section4.html
- https://github.com/algotom/algotom/tree/master/examples
Development principles
----------------------
- While Algotom offers a complete set of tools for tomographic data processing covering
pre-processing, reconstruction, post-processing, data simulation, and calibration techniques;
its development strongly focuses on pre-processing techniques. This distinction makes it a
prominent feature among other tomographic software.
- To ensure that the software can work across platforms and is easy-to-install; dependencies are minimized, and only
well-maintained [Python libraries](https://github.com/algotom/algotom/blob/master/requirements.txt) are used.
- To achieve high-performance computing and leverage GPU utilization while ensuring ease of understanding, usage, and software
maintenance, Numba is used instead of Cupy or PyCuda.
- Methods are structured into modules and functions rather than classes to enhance usability, debugging, and maintenance.
- Algotom is highly practical as it can run on computers with or without a GPU, multicore CPUs; and accommodates both small
and large memory capacities.
Update notes
------------
- https://algotom.readthedocs.io/en/latest/toc/section6.html
Author
------
- Nghia T. Vo - *NSLS-II, Brookhaven National Lab, USA*; *Diamond Light Source, UK.*
Highlights
----------
Algotom was used for some experiments featured on media:
- Scanning [Moon rocks and Martian meteorites](https://www.diamond.ac.uk/Home/News/LatestNews/2019/17-07-2019.html)
using helical scans with offset rotation-axis. Featured on [Reuters](https://www.reuters.com/article/idUKKCN1UC16V?edition-redirect=uk).
- Scanning [Herculaneum Scrolls](https://www.diamond.ac.uk/Home/News/LatestNews/2019/03-10-2019.html)
using grid scans with offset rotation-axis respect to the grid's FOV (pixel size of 7.9 micron;
total size of 11.3 TB). Featured on [BBC](https://www.bbc.co.uk/news/av/uk-england-oxfordshire-49926789).
The latest updates on the scroll's reading progress are [here](https://www.nature.com/articles/d41586-023-03212-1).
- Scanning ['Little Foot' fossil](https://www.diamond.ac.uk/Home/News/LatestNews/2021/02-03-21.html)
using two-camera detector with offset rotation-axis. Featured on [BBC](https://www.bbc.co.uk/news/science-environment-56241509).
