centering360.rst

{% extends "plugin_template.rst" %}

{% block title %}Centering360{% endblock %}

{% block description %} A plugin to calculate the centre of rotation {% endblock %}

{% block parameter_yaml %}

in_datasets:

visibility: datasets dtype: "[list[],list[str]]" description: summary: A list of the dataset(s) to process. verbose: A list of strings, where each string gives the name of a dataset that was either specified by a loader plugin or created as output to a previous plugin. The length of the list is the number of input datasets requested by the plugin. If there is only one dataset and the list is left empty it will default to that dataset. default: "[]"

out_datasets:

visibility: datasets dtype: "[list[],list[str]]" description: The default names default: "['cor_preview', 'cor_broadcast']"

preview:

visibility: basic dtype: preview description: A slice list of required frames (sinograms) to use in the calculation of the centre of rotation (this will not reduce the data size for subsequent plugins). default: "[]" example: "The typical three dimensional data structure is [angles, detY, detZ], e.g. for sinogram choose [:,sliceNo,:] [angles, detZ, detY]. If the data is four dimensional, include a time parameter."

win_width:

visibility: basic dtype: int description: Width of window used for finding the overlap area default: "100"

side:

visibility: basic dtype: "[None,int]" description: Overlap size. "None" corresponding to fully automated determination. "0" corresponding to the left side. "1" corresponding to the right side. default: None options: "['None', 1, 0]"

denoise:

visibility: intermediate dtype: bool description: Apply the gaussian filter if True default: "True"

norm:

visibility: intermediate dtype: bool description: Apply the normalisation if True default: "True"

use_overlap:

visibility: intermediate dtype: bool description: Use the combination of images in the overlap area for calculating correlation coefficients if True. default: "True"

broadcast_method:

visibility: intermediate dtype: str options: "['median', 'mean', 'nearest', 'linear_fit']" description: summary: Method of broadcasting centre values calculated from preview slices to full dataset. options: median: None mean: None nearest: None linear_fit: None default: median

datasets_to_populate:

visibility: intermediate dtype: "[list[],list[str]]" description: A list of datasets which require this information default: "[]"

{% endblock %}

{% block plugin_citations %}

Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography by Vo, Nghia T et al.

Bibtex

@article{Vo:21,
author = {Nghia T. Vo and Robert C. Atwood and Michael Drakopoulos and Thomas Connolley},
journal = {Opt. Express},
keywords = {Digital image processing; Fluorescence tomography; Image processing; Image registration; Phase contrast; Phase retrieval},
number = {12},
pages = {17849--17874},
publisher = {OSA},
title = {Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography},
volume = {29},
month = {Jun},
year = {2021},
url = {http://www.osapublishing.org/oe/abstract.cfm?URI=oe-29-12-17849},
doi = {10.1364/OE.418448},
abstract = {Parallel-beam tomography systems at synchrotron facilities have limited field of view (FOV) determined by the available beam size and detector system coverage. Scanning the full size of samples bigger than the FOV requires various data acquisition schemes such as grid scan, 360-degree scan with offset center-of-rotation (COR), helical scan, or combinations of these schemes. Though straightforward to implement, these scanning techniques have not often been used due to the lack of software and methods to process such types of data in an easy and automated fashion. The ease of use and automation is critical at synchrotron facilities where using visual inspection in data processing steps such as image stitching, COR determination, or helical data conversion is impractical due to the large size of datasets. Here, we provide methods and their implementations in a Python package, named Algotom, for not only processing such data types but also with the highest quality possible. The efficiency and ease of use of these tools can help to extend applications of parallel-beam tomography systems.},
}

Endnote

%0 Journal Article
%T Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography
%A Vo, Nghia T
%A Atwood, Robert C
%A Drakopoulos, Michael
%A Connolley, Thomas
%J Optics Express
%V 29
%N 12
%P 17849-17874
%@ 1094-4087
%D 2021
%I Optical Society of America

{% endblock %}

{% block plugin_file %}../../../plugin_api/plugins.centering.centering360.rst{% endblock %}