algotom 1.0.2

Data processing algorithms for tomography

Algotom

Data processing (ALGO)rithms for (TOM)ography.

Algotom is a Python package implementing methods for processing tomographic data acquired by non-standard scanning techniques such as grid scans, helical scans, half-acquisition scans, or their combinations. Certainly, Algotom can also be used for standard scans. The software includes 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.

The software is made available for the paper; "Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography," Nghia T. Vo, Robert C. Atwood, Michael Drakopoulos, and Thomas Connolley, Opt. Express 29, 17849-17874 (2021); https://doi.org/10.1364/OE.418448. Selected answers to technical questions of anonymous reviewers about methods in the paper is here.

"... Algotom development was started at the I12-JEEP beamline in 2014 as Python codes to process data acquired by the beamline's large field-of-view (FOV) detector, which uses two imaging sensors to cover a rectangular FOV. Images from these cameras must be stitched before tomographic reconstruction can take place. Data processing methods for improving the quality of tomographic data; removing artifacts caused by imperfections of hardware components; making use the beamline capabilities; processing data acquired by non-traditional scanning techniques; and automating data processing pipeline have been actively developed at I12 over the years. These methods have been used internally by I12's users and refined further for publication and sharing with the research community through open-source software such as Tomopy and Savu ...

... In contrast to Savu and Tomopy which are optimized for speed, Algotom is a package of data processing algorithms and tools which are designed to be easy-to-use and easy-to-deploy prototype methods. The development of Algotom has focused on pre-processing methods which work in the sinogram space to reduce computational cost. Methods working in the projection space such as phase filter, distortion correction, or rotation correction have been adapted to work in the sinogram space..."

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 a synchrotron beamline where massive datasets are produced; image features can change significantly between experiments depending on X-ray energy and sample types which can be biological, medical, material science, or geological in origin. Users often don't have sufficient experience with image processing methods to know how to properly tune parameters. All these factors drive the methods developed to be easy-to-use, robust, and practical. Some featuring methods in Algotom are as follows:

• 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).

• Methods in a full data processing pipeline: reading-writing data, pre-processing, tomographic reconstruction, and post-processing.

  

• 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, 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.

  

Author

Nghia T. Vo - Diamond Light Source, UK.

How to install

• From source code:

  • Download or git clone the source code to a local folder.

  • Download and install Miniconda software: https://docs.conda.io/en/latest/miniconda.html

  • Open Linux terminal or Windows command prompt and run the following commands:

    conda create -n algotom python=3.7

    conda activate algotom

    cd <path-to-source-code>

    python setup.py install

• Using conda:

  • Install Miniconda as instructed above.

  • Open terminal or command prompt and run the following command:

    conda install -c algotom algotom

• Using pip:

  • Install Miniconda as instructed above.

  • Open terminal or command prompt and run the following command:

    pip install algotom

How to use

• Examples of how to use the package are in the "examples/" folder on github. They cover most of use-cases which users can adapt to process their own data.

• Real tomographic data for testing methods can be downloaded from zenodo.org

• Methods can also be tested using simulation data as shown in "examples/example_08*.py"

• Users can use Algotom to re-process some old data collected at synchrotron facilities suffering from:

  • Various types of ring artifacts.
  • Cupping artifacts (also known as beam hardening artifacts) which are caused by using: FFT-based reconstruction methods without proper padding; polychromatic X-ray sources; or low-dynamic-range detectors to record high-dynamic-range projection-images.

  Methods distributed in Algotom can run on a normal computer which enable users to process these data locally.

• There are tools and methods users can use to customize their own algorithms:

  • Methods to transform images back-and-forth between the polar coordinate system and the Cartesian coordinate system.
  • Methods to separate stripe artifacts.
  • Methods to transform back-and-forth between reconstruction images and sinogram images.

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.