# Algotom
### Data processing (**ALGO**)rithms for (**TOM**)ography.
 [](https://pepy.tech/project/algotom)      
**Algotom** is a Python package designed for tomography data processing. It
offers a complete data processing pipeline; including reading and writing data,
pre-processing, tomographic reconstruction, post-processing, data simulation,
and calibration techniques. The package provides many utility methods to
assist users in constructing a pipeline 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, data reduction; and the capability of
processing non-standard tomography acquisitions such as grid scans 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 exploit full beamline
capabilities.
Features
--------
Algotom is a lightweight package. 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).
Raw data
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"description": "# Algotom\r\n### Data processing (**ALGO**)rithms for (**TOM**)ography.\r\n\r\n [](https://pepy.tech/project/algotom)      \r\n\r\n\r\n\r\n\r\n**Algotom** is a Python package designed for tomography data processing. It \r\noffers a complete data processing pipeline; including reading and writing data, \r\npre-processing, tomographic reconstruction, post-processing, data simulation, \r\nand calibration techniques. The package provides many utility methods to \r\nassist users in constructing a pipeline for processing their own data or \r\ndeveloping new methods. Key features of Algotom include a wide range of \r\nprocessing methods such as artifact removal, distortion correction, \r\nspeckle-based phase-contrast imaging, data reduction; and the capability of \r\nprocessing non-standard tomography acquisitions such as grid scans or helical scans. \r\nThe software stands out for its readability, minimal dependencies, and rich documentation. \r\nDeveloped specifically for synchrotron-based tomographic beamlines, Algotom aims to \r\nmaximize data quality, enhance workflow throughput, and exploit full beamline \r\ncapabilities.\r\n\r\nFeatures\r\n--------\r\n\r\nAlgotom is a lightweight package. The software is built on top of a few core\r\nPython libraries to ensure its ease-of-installation. Methods distributed in \r\nAlgotom have been developed and tested at synchrotron beamlines where massive\r\ndatasets are produced. This factor drives the methods developed to be easy-to-use, \r\nrobust, and practical. Algotom can be used on a normal computer to process large \r\ntomographic data. Some featuring methods in Algotom are as follows:\r\n\r\n- Methods in a full data processing pipeline: reading-writing data, \r\n pre-processing, tomographic reconstruction, and post-processing.\r\n \r\n  \r\n \r\n- Methods for processing grid scans (or tiled scans) with the offset rotation-axis \r\n to multiply double the field-of-view (FOV) of a parallel-beam tomography system.\r\n These techniques enable high-resolution tomographic scanning of large samples.\r\n \r\n \r\n \r\n- Methods for processing helical scans (with/without the offset rotation-axis).\r\n \r\n \r\n\r\n- Methods for determining the center-of-rotation (COR) and auto-stitching images \r\n in half-acquisition scans (360-degree acquisition with the offset COR).\r\n\r\n- Practical methods developed and implemented for the package: zinger removal, \r\n tilted sinogram generation, sinogram distortion correction, simplified form of Paganin's filter,\r\n beam hardening correction, DFI (direct Fourier inversion) reconstruction,\r\n FBP (filtered back-projection) reconstruction, BPF (back-projection filtering) reconstruction, \r\n and double-wedge filter for removing sample parts larger than the FOV in a sinogram.\r\n \r\n \r\n \r\n- Utility methods for [customizing ring/stripe artifact removal methods](https://algotom.readthedocs.io/en/latest/toc/section4/section4_3.html) \r\n and parallelizing computational work.\r\n\r\n- Calibration methods for helical scans and tomography alignment.\r\n\r\n- Methods for generating simulation data: phantom creation, sinogram calculation\r\n based on the Fourier slice theorem, and artifact generation.\r\n \r\n \r\n\r\n- Methods for phase-contrast imaging: phase unwrapping, speckle-based phase retrieval, image correlation, and image alignment.\r\n\r\n \r\n\r\n- Methods for downsampling, rescaling, and reslicing (+rotating, cropping) \r\n 3D reconstructed image without large memory usage.\r\n\r\n \r\n\r\n- Direct vertical reconstruction for single slice, multiple slices, and multiple slices at \r\n different orientations.\r\n \r\n \r\n\r\n \r\n \r\n \r\n\r\nInstallation\r\n------------\r\n\r\n- https://algotom.readthedocs.io/en/latest/toc/section3.html\r\n- If users install Algotom to an existing environment and Numba fails to install due to the latest Numpy:\r\n + Downgrade Numpy and install Algotom/Numba again.\r\n + Create a new environment and install Algotom first, then other packages.\r\n + Use conda instead of pip.\r\n- Avoid using the latest version of Python or Numpy as the Python ecosystem taking time to keep up with these twos.\r\n\r\nUsage\r\n-----\r\n- https://algotom.readthedocs.io/en/latest/toc/section4/section4_5.html\r\n- https://algotom.readthedocs.io/en/latest/toc/section1/section1_4.html\r\n- https://algotom.readthedocs.io/en/latest/toc/section4.html\r\n- https://github.com/algotom/algotom/tree/master/examples\r\n\r\nDevelopment principles\r\n----------------------\r\n\r\n- While Algotom offers a complete set of tools for tomographic data processing covering \r\n pre-processing, reconstruction, post-processing, data simulation, and calibration techniques;\r\n its development strongly focuses on pre-processing techniques. This distinction makes it a\r\n prominent feature among other tomographic software. \r\n\r\n- To ensure that the software can work across platforms and is easy-to-install; dependencies are minimized, and only \r\n well-maintained [Python libraries](https://github.com/algotom/algotom/blob/master/requirements.txt) are used.\r\n\r\n- To achieve high-performance computing and leverage GPU utilization while ensuring ease of understanding, usage, and software \r\n maintenance, Numba is used instead of Cupy or PyCuda.\r\n\r\n- Methods are structured into modules and functions rather than classes to enhance usability, debugging, and maintenance.\r\n\r\n- Algotom is highly practical as it can run on computers with or without a GPU, multicore CPUs; and accommodates both small \r\n and large memory capacities.\r\n\r\nUpdate notes\r\n------------\r\n\r\n- https://algotom.readthedocs.io/en/latest/toc/section6.html\r\n\r\nAuthor\r\n------\r\n\r\n- Nghia T. 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