astra_recon_gpu.rst

{% extends "plugin_template.rst" %}

{% block title %}Astra Recon Gpu{% endblock %}

{% block description %} A Plugin to run the astra reconstruction {% 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: summary: A list of the dataset(s) to create. verbose: A list of strings, where each string is a name to be assigned to a dataset output by the plugin. If there is only one input dataset and one output dataset and the list is left empty, the output will take the name of the input dataset. The length of the list is the number of output datasets created by the plugin. default: "[]"

centre_of_rotation:

visibility: basic dtype: "[float, str, dict{int:float}]" description: Centre of rotation to use for the reconstruction. default: "0.0" example: It could be a fixed value, a dictionary of (sinogram number, value) pairs for a polynomial fit of degree 1, or a dataset name.

init_vol:

visibility: intermediate dtype: "[None, str]" description: Dataset to use as volume initialiser (does not currently work with preview) default: None example: "Type the name of the initialised dataset e.g. ['tomo']"

log:

visibility: intermediate dtype: bool description: summary: Option to take the log of the data before reconstruction. verbose: Should be set to false if you use PaganinFilter default: "True" example: Set to True to take the log of the data before reconstruction.

preview:

visibility: intermediate dtype: preview description: A slice list of required frames. default: "[]"

force_zero:

visibility: intermediate dtype: "[list[float,float],list[None,None]]" description: Set any values in the reconstructed image outside of this range to zero. default: "['None', 'None']" example: "[0, 1]"

ratio:

visibility: intermediate dtype: "[float, list[float, float]]" description: Ratio of the masks diameter in pixels to the smallest edge size along given axis. If a list of two floats is given, the second value is used to fill up the area outside the mask. default: "0.95"

log_func:

visibility: advanced dtype: str description: Override the default log function with a numpy statement default: np.nan_to_num(-np.log(sino))

vol_shape:

visibility: intermediate dtype: "[str, int]" description: summary: Override the size of the reconstruction volume with an integer value. verbose: When fixed, you get the dimension of the horizontal detector or you can specify any reconstruction size you like with an integer. default: fixed

n_iterations:

visibility: basic dtype: int description: Number of iterations to perform default: "1" dependency: algorithm: SIRT_CUDA SART_CUDA CGLS_CUDA BP3D_CUDA CGLS3D_CUDA SIRT3D_CUDA

outer_pad:

visibility: intermediate dtype: "[bool, float]" description: Pad the sinogram width to fill the reconstructed volume for asthetic purposes. Choose from True (defaults to sqrt(2)), False or float <= 2.1. warning: This will increase the size of the data and the time to compute the reconstruction. Only available for selected algorithms and will be ignored otherwise. default: "False" dependency: algorithm: FBP_CUDA BP_CUDA

centre_pad:

visibility: intermediate dtype: "[bool, float]" description: Pad the sinogram to centre it in order to fill the reconstructed volume ROI for asthetic purposes. warning: This will significantly increase the size of the data and the time to compute the reconstruction) default: "False" dependency: algorithm: FBP_CUDA BP_CUDA

res_norm:

visibility: basic dtype: "[int,bool]" description: Output the residual norm at each iteration (Error in the solution) default: "False" dependency: algorithm: SIRT_CUDA SART_CUDA CGLS_CUDA CGLS3D_CUDA SIRT3D_CUDA

algorithm:

visibility: basic dtype: str options: "['FBP_CUDA', 'SIRT_CUDA', 'SART_CUDA', 'CGLS_CUDA', 'BP_CUDA', 'BP3D_CUDA', 'FBP3D_CUDA', 'SIRT3D_CUDA', 'CGLS3D_CUDA']" description: summary: Reconstruction type options: FBP_CUDA: Filtered Backprojection Method SIRT_CUDA: Simultaneous Iterative Reconstruction Technique SART_CUDA: Simultaneous Algebraic Reconstruction Technique CGLS_CUDA: Conjugate Gradient Least Squares BP_CUDA: Backward Projection BP3D_CUDA: Backward Projection 3D FBP3D_CUDA: Filtered Backprojection Method 3D SIRT3D_CUDA: Simultaneous Iterative Reconstruction Technique 3D CGLS3D_CUDA: Conjugate Gradient Least Squares 3D default: FBP_CUDA

FBP_filter:

visibility: intermediate dtype: str options: "['none', 'ram-lak', 'shepp-logan', 'cosine', 'hamming', 'hann', 'tukey', 'lanczos', 'triangular', 'gaussian', 'barlett-hann', 'blackman', 'nuttall', 'blackman-harris', 'blackman-nuttall', 'flat-top', 'kaiser', 'parzen']" description: summary: The FBP reconstruction filter type options: none: No filtering ram-lak: Ram-Lak or ramp filter shepp-logan: Multiplies the Ram-Lak filter by a sinc function cosine: Multiplies the Ram-Lak filter by a cosine function hamming: Multiplies the Ram-Lak filter by a hamming window hann: Multiplies the Ram-Lak filter by a hann window tukey: None lanczos: None triangular: None gaussian: None barlett-hann: None blackman: None nuttall: None blackman-harris: None blackman-nuttall: None flat-top: None kaiser: None parzen: None default: ram-lak dependency: algorithm: FBP_CUDA

{% endblock %}

{% block plugin_citations %}

Fast and flexible X-ray tomography using the ASTRA toolbox by Van Aarle, Wim et al.

Bibtex

@article{van2016fast,
title={Fast and flexible X-ray tomography using the ASTRA toolbox},
author={Van Aarle, Wim and Palenstijn, Willem Jan and Cant, Jeroen and Janssens, Eline and Bleichrodt, Folkert and Dabravolski, Andrei and De Beenhouwer, Jan and Batenburg, K Joost and Sijbers, Jan},
journal={Optics express},
volume={24},
number={22},
pages={25129--25147},
year={2016},
publisher={Optical Society of America}
}

Endnote

%0 Journal Article
%T Fast and flexible X-ray tomography using the ASTRA toolbox
%A Van Aarle, Wim
%A Palenstijn, Willem Jan
%A Cant, Jeroen
%A Janssens, Eline
%A Bleichrodt, Folkert
%A Dabravolski, Andrei
%A De Beenhouwer, Jan
%A Batenburg, K Joost
%A Sijbers, Jan
%J Optics express
%V 24
%N 22
%P 25129-25147
%@ 1094-4087
%D 2016
%I Optical Society of America

The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography by Van Aarle, Wim et al.

Bibtex

@article{van2015astra,
title={The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography},
author={Van Aarle, Wim and Palenstijn, Willem Jan and De Beenhouwer, Jan and Altantzis, Thomas and Bals, Sara and Batenburg, K Joost and Sijbers, Jan},
journal={Ultramicroscopy},
volume={157},
pages={35--47},
year={2015},
publisher={Elsevier}
}

Endnote

%0 Journal Article
%T The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography
%A Van Aarle, Wim
%A Palenstijn, Willem Jan
%A De Beenhouwer, Jan
%A Altantzis, Thomas
%A Bals, Sara
%A Batenburg, K Joost
%A Sijbers, Jan
%J Ultramicroscopy
%V 157
%P 35-47
%@ 0304-3991
%D 2015
%I Elsevier

Performance improvements for iterative electron tomography reconstruction using graphics processing units (GPUs) by Palenstijn, WJ et al.

Bibtex

@article{palenstijn2011performance,
  title={Performance improvements for iterative electron tomography reconstruction using graphics processing units (GPUs)},
  author={Palenstijn, WJ and Batenburg, KJ and Sijbers, J},
  journal={Journal of structural biology},
  volume={176},
  number={2},
  pages={250--253},
  year={2011},
  publisher={Elsevier}
}

Endnote

%0 Journal Article
%T Performance improvements for iterative electron tomography reconstruction using graphics processing units (GPUs)
%A Palenstijn, WJ
%A Batenburg, KJ
%A Sijbers, J
%J Journal of structural biology
%V 176
%N 2
%P 250-253
%@ 1047-8477
%D 2011
%I Elsevier

{% endblock %}

{% block plugin_file %}../../../../plugin_api/plugins.reconstructions.astra_recons.astra_recon_gpu.rst{% endblock %}