Phase retrieval in femtosecond X-ray nanocrystallography

Joe P J Chen, John Spence, Rick P. Millane

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Protein X-ray crystallography is a method for determining the 3-dimensional structures of large biological molecules arranged in regular arrays inside a crystal. Samples of the Fourier magnitude of the molecular charge density can be measured from the amplitudes of the scattered X-rays but the determination of the Fourier phases requires chemical modification to the sample and collection of additional data. There is thus a need for a direct digital phasing method that does not require modified specimens. The diffraction from very small crystals allows for a finer sampling of the diffraction amplitude and although highly attenuated, these additional samples offer the possibility of iterative phase retrieval without the use of ancillary experimental data. Following on from a previous study [6], we examine in detail the noise characteristics of finite crystal diffraction and propose a data selection strategy to improve 3-dimensional reconstructions of the molecular charge density using iterative phase retrieval algorithms. Simulation results verify that higher noise levels can indeed be tolerated by employing such a strategy to precondition the data.

Original languageEnglish (US)
Title of host publicationProceedings of IVCNZ 2012 - The 27th Image and Vision Computing New Zealand Conference
Number of pages6
StatePublished - 2012
Event27th Image and Vision Computing New Zealand Conference, IVCNZ 2012 - Dunedin, New Zealand
Duration: Nov 26 2012Nov 28 2012

Publication series

NameACM International Conference Proceeding Series


Other27th Image and Vision Computing New Zealand Conference, IVCNZ 2012
Country/TerritoryNew Zealand


  • nanocrystallography
  • phase retrieval
  • selective sampling
  • shape transform
  • x-ray free-electron laser

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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