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. 2014 Sep;21(Pt 5):1188-93.
doi: 10.1107/S1600577514013939. Epub 2014 Aug 1.

TomoPy: a framework for the analysis of synchrotron tomographic data

Dogˇa Gürsoy  1 , Francesco De Carlo  1 , Xianghui Xiao  1 , Chris Jacobsen  1
Affiliations

TomoPy: a framework for the analysis of synchrotron tomographic data

Dogˇa Gürsoy et al. J Synchrotron Radiat. 2014 Sep.

Abstract

Analysis of tomographic datasets at synchrotron light sources (including X-ray transmission tomography, X-ray fluorescence microscopy and X-ray diffraction tomography) is becoming progressively more challenging due to the increasing data acquisition rates that new technologies in X-ray sources and detectors enable. The next generation of synchrotron facilities that are currently under design or construction throughout the world will provide diffraction-limited X-ray sources and are expected to boost the current data rates by several orders of magnitude, stressing the need for the development and integration of efficient analysis tools. Here an attempt to provide a collaborative framework for the analysis of synchrotron tomographic data that has the potential to unify the effort of different facilities and beamlines performing similar tasks is described in detail. The proposed Python-based framework is open-source, platform- and data-format-independent, has multiprocessing capability and supports procedural programming that many researchers prefer. This collaborative platform could affect all major synchrotron facilities where new effort is now dedicated to developing new tools that can be deployed at the facility for real-time processing, as well as distributed to users for off-site data processing.

Keywords: X-ray imaging; phase retrieval; tomography.

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Figures

Figure 1
Figure 1
The TomoPy framework. The analysis chain is divided into pre-processing, reconstruction and post-processing modules. Any method or a collection of methods in any language can be hooked to one of these modules without compromising the modularity. A Python front-end is used to interface these modules and interact with the user. A customizable Python-based multiprocessing interface is provided for time-consuming computations.
Figure 2
Figure 2
Reconstructed images of a bird feather (a–c) and a shale rock samples (b–d) before (a–b) and after (c–d) the combined wavelet-Fourier method for stripe removal has been applied on data.
Figure 3
Figure 3
Phase-contrast projection data of an ant placed in a capillary (a) lead to a recovered phase image obtained with the single-step phase-retrieval method using a Pagannin filter (b). The corresponding reconstructed images of a single slice without and with phase retrieval are shown in (c) and (d), respectively.
Figure 4
Figure 4
(a) Raw 0–360° sinogram of a large sample (porous yttriz-stabilized zirconia) that is not fitting in the detector’s field-of-view. (b) Paired 0–180 and 180–360° sinograms. (c) Registered sinogram using phase correlation. (d) Reconstructed image from the registered sinogram.
Figure 5
Figure 5
Reconstructed images of a shale rock sample obtained with different centers of rotations: (a) correct center, (b) 16 pixels off-center horizontally, (c) 32 pixels off-center horizontally.
Figure 6
Figure 6
Plot of Shannon entropy as a function of different rotation centers.
Figure 7
Figure 7
Reconstructed images of a shale rock sample obtained with Gridrec (a), ART (tenth iteration) (b) and MLEM (50th iteration) (c) methods using 46 projections out of an available 1500 projections.
See this image and copyright information in PMC

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