2. Features

2.1. Capabilities

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.

../_images/fig_2_1.png

  • 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.

    ../_images/fig_2_2.jpg

  • Methods for processing helical scans (with/without the offset rotation-axis).

    ../_images/fig_2_3.jpg

  • 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.

    ../_images/fig_2_4.jpg

  • 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.

    ../_images/fig_2_5.png

  • Methods for phase-contrast imaging: phase unwrapping, speckle-based phase retrieval, image correlation, and image alignment.

    ../_images/fig_2_6.png

  • Methods for downsampling, rescaling, and reslicing (+rotating, cropping) 3D reconstructed image without large memory usage.

    ../_images/fig_2_7.jpg

2.2. Development principles

  • While Algotom offers a comprehensive range of tools for tomographic data processing covering raw-data reading, pre-processing, reconstruction, post-processing, and data saving; its development primarily 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 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.