Model-based iterative tomographic reconstruction with adaptive sparsifying transforms

Luke Pfister, Yoram Bresler

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

Abstract

Model based iterative reconstruction algorithms are capable of reconstructing high-quality images from lowdose CT measurements. The performance of these algorithms is dependent on the ability of a signal model to characterize signals of interest. Recent work has shown the promise of signal models that are learned directly from data. We propose a new method for low-dose tomographic reconstruction by combining adaptive sparsifying transform regularization within a statistically weighted constrained optimization problem. The new formulation removes the need to tune a regularization parameter. We propose an algorithm to solve this optimization problem, based on the Alternating Direction Method of Multipliers and FISTA proximal gradient algorithm. Numerical experiments on the FORBILD head phantom illustrate the utility of the new formulation and show that adaptive sparsifying transform regularization outperforms competing dictionary learning methods at speeds rivaling total-variation regularization.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Computational Imaging XII
PublisherSPIE
ISBN (Print)9780819499370
DOIs
StatePublished - Jan 1 2014
EventComputational Imaging XII - San Francisco, CA, France
Duration: Feb 5 2014Feb 6 2014

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume9020
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

OtherComputational Imaging XII
CountryFrance
CitySan Francisco, CA
Period2/5/142/6/14

Keywords

  • CT dose reduction
  • Iterative reconstruction
  • Sparisfying transform learning
  • Sparse representations

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Applied Mathematics
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

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  • Cite this

    Pfister, L., & Bresler, Y. (2014). Model-based iterative tomographic reconstruction with adaptive sparsifying transforms. In Proceedings of SPIE-IS and T Electronic Imaging - Computational Imaging XII [90200H] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 9020). SPIE. https://doi.org/10.1117/12.2041011