algotom 1.3.0

Data processing algorithms for tomography

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:

• 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.

  

• 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).

• 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.

  

• Utility methods for customizing ring/stripe artifact removal methods and parallelizing computational work.

• Calibration methods for determining pixel-size in helical scans.

• 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.

  

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.

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:

• Scanning Moon rocks and Martian meteorites using helical scans with offset rotation-axis. Featured on Reuters.

  

• Scanning Herculaneum Scrolls using grid scans with offset rotation-axis respect to the grid's FOV. Featured on BBC.

  

• Scanning 'Little Foot' fossil using two-camera detector with offset rotation-axis. Featured on BBC.