Data processing algorithms for tomography
Project description
Algotom
Data processing (ALGO)rithms for (TOM)ography.
Algotom is a Python package implementing data processing methods for tomography. It has methods in a full pipeline of data processing: reading-writing data, pre-processing, tomographic reconstruction, post-processing, and data simulation. Many utility methods are provided to help users quickly develop prototype-methods or build a pipeline for processing their own data. From version 1.1, methods for speckle-based phase-contrast tomography were added to the package. The software was published for the paper: https://doi.org/10.1364/OE.418448.
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:
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Methods in a full data processing pipeline: reading-writing data, pre-processing, tomographic reconstruction, and post-processing.
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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.
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Methods for processing helical scans (with/without the offset rotation-axis).
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Methods for determining the center-of-rotation (COR) and auto-stitching images in half-acquisition scans (360-degree acquisition with the offset COR).
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Some practical methods developed and implemented for the package: zinger removal, tilted sinogram generation, sinogram distortion correction, beam hardening correction, DFI (direct Fourier inversion) reconstruction, FBP reconstruction, and double-wedge filter for removing sample parts larger than the FOV in a sinogram.
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Utility methods for customizing ring/stripe artifact removal methods and parallelizing computational work.
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Calibration methods for determining pixel-size in helical scans.
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Methods for generating simulation data: phantom creation, sinogram calculation based on the Fourier slice theorem, and artifact generation.
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Methods for phase-contrast imaging: phase unwrapping, speckle-based phase retrieval, image correlation, and image alignment.
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Methods for downsampling, rescaling, and reslicing (+rotating, cropping) 3D reconstructed image without large memory usage.
Update notes
Author
- Nghia T. Vo - NSLS-II, Brookhaven National Lab, USA; Diamond Light Source, UK.
How to install
- 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.
How to use
- https://algotom.readthedocs.io/en/latest/toc/section4.html
- https://algotom.readthedocs.io/en/latest/toc/section1/section1_4.html
- https://github.com/algotom/algotom/tree/master/examples
Highlights
Algotom was used for some experiments featured on media:
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Scanning Moon rocks and Martian meteorites using helical scans with offset rotation-axis. Featured on Reuters.
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Scanning Herculaneum Scrolls using grid scans with offset rotation-axis respect to the grid's FOV. Featured on BBC.
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Scanning 'Little Foot' fossil using two-camera detector with offset rotation-axis. Featured on BBC.
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