TomoPy: A framework for the analysis of synchrotron tomographic data

Doğa Gürsoy; Francesco De Carlo; Xianghui Xiao; Chris Jacobsen

doi:10.1117/12.2061373

11 September 2014 TomoPy: A framework for the analysis of synchrotron tomographic data

Doğa Gürsoy; Francesco De Carlo; Xianghui Xiao; Chris Jacobsen

Author Affiliations +

Proceedings Volume 9212, Developments in X-Ray Tomography IX; 92120N (2014) https://doi.org/10.1117/12.2061373
Event: SPIE Optical Engineering + Applications, 2014, San Diego, California, United States

Abstract

Analysis of large tomographic datasets at synchrotron light sources 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 is expected to boost the current data rates by several orders of magnitude and stressing the need for the development and integration of efficient analysis tools more than ever. Here we describe in detail an attempt to provide such 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. The proposed Python/C++ based framework is open-source, OS and data format independent, parallelizable and supports functional programming that many researchers prefer. This collaborative platform will affect all major synchrotron facilities where new effort is now dedicated into 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.

Citation Download Citation

Doğa Gürsoy, Francesco De Carlo, Xianghui Xiao, Chris Jacobsen, "TomoPy: A framework for the analysis of synchrotron tomographic data", Proc. SPIE 9212, Developments in X-Ray Tomography IX, 92120N (11 September 2014); doi: 10.1117/12.2061373; https://doi.org/10.1117/12.2061373

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