algotom 1.6.0

Description:

algotom 1.6.0

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


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
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 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
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 (pixel size of 7.9 micron;
total size of 11.3 TB). Featured on BBC.
The latest updates on the scroll's reading progress are here.



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