TIMBIR: A method for time-space reconstruction from interlaced views

K. Aditya Mohan, S. V. Venkatakrishnan, John W. Gibbs, Emine Begum Gulsoy, Xianghui Xiao, Marc De Graef, Peter W. Voorhees, Charles A. Bouman

Research output: Contribution to journalArticlepeer-review

67 Scopus citations


Synchrotron X-ray computed tomography (SXCT) is increasingly being used for 3-D imaging of material samples at micron and finer scales. The success of these techniques has increased interest in 4-D reconstruction methods that can image a sample in both space and time. However, the temporal resolution of widely used 4-D reconstruction methods is severely limited by the need to acquire a very large number of views for each reconstructed 3-D volume. Consequently, the temporal resolution of current methods is insufficient to observe important physical phenomena. Furthermore, measurement nonidealities also tend to introduce ring and streak artifacts into the 4-D reconstructions. In this paper, we present a time-interlaced model-based iterative reconstruction (TIMBIR) method, which is a synergistic combination of two innovations. The first innovation, interlaced view sampling, is a novel method of data acquisition, which distributes the view angles more evenly in time. The second innovation is a 4-D model-based iterative reconstruction algorithm (MBIR), which can produce time-resolved volumetric reconstruction of the sample from the interlaced views. In addition to modeling both the sensor noise statistics and the 4-D object, the MBIR algorithm also reduces ring and streak artifacts by more accurately modeling the measurement nonidealities. We present reconstructions of both simulated and real X-ray synchrotron data, which indicate that TIMBIR can improve temporal resolution by an order of magnitude relative to existing approaches.

Original languageEnglish (US)
Article number7105892
Pages (from-to)96-111
Number of pages16
JournalIEEE Transactions on Computational Imaging
Issue number2
StatePublished - Jun 1 2015


  • 4D reconstruction
  • Compressed sensing
  • Interlaced views
  • MBIR
  • X-ray computed tomography
  • optimization
  • ring artifacts
  • streak artifacts
  • synchrotron
  • timespace imaging
  • zingers

ASJC Scopus subject areas

  • Signal Processing
  • Computer Science Applications
  • Computational Mathematics


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