Tomographic reconstruction with adaptive sparsifying transforms

Luke Pfister, Yoram Bresler

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

Abstract

A central problem in computed tomography (CT) imaging is to obtain useful, high-quality images from low-dose measurements. Methods that exploit the sparse representations of tomographic images have long been known to improve the quality of reconstructions from low-dose data. Recent work has shown that sparse representations learned directly from the data can outperform traditional, fixed representations, but are prohibitively expensive for practical use in CT. We propose a new method for tomographic reconstruction from low-dose data by combining the statistically weighted data fidelity term with an adaptive sparsifying transform regularizer. This regularizer can be fit to the data at lower cost than competing methods. Our algorithm alternates between reconstructing the image and learning the sparsifying transform. The Alternating Direction Method of Multipliers technique is used to provide an efficient solution to the statistically weighted minimization problem. Numerical experiments on data from clinical CT reconstructions indicate that adaptive sparsifying transform regularization outperforms synthesis sparsity methods at speeds rivaling total-variation regularization.

Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6914-6918
Number of pages5
ISBN (Print)9781479928927
DOIs
StatePublished - Jan 1 2014
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: May 4 2014May 9 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
CountryItaly
CityFlorence
Period5/4/145/9/14

Keywords

  • CT dose reduction
  • iterative reconstruction
  • Sparse representations
  • Sparsifying transform learning

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

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  • Cite this

    Pfister, L., & Bresler, Y. (2014). Tomographic reconstruction with adaptive sparsifying transforms. In 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 (pp. 6914-6918). [6854940] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2014.6854940