TomoPy: A framework for the analysis of synchrotron tomographic data

Doǧa Gürsoy, Francesco De Carlo, Xianghui Xiao, Chris Jacobsen

Research output: Contribution to journalArticlepeer-review

573 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1188-1193
Number of pages6
JournalJournal of Synchrotron Radiation
Issue number5
StatePublished - Sep 2014


  • X-ray imaging
  • phase retrieval
  • tomography

ASJC Scopus subject areas

  • Nuclear and High Energy Physics
  • Instrumentation
  • Radiation


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