Domain and range decomposition methods for coded aperture x-ray coherent scatter imaging

Ikenna Odinaka, Yan Kaganovsky, Joseph A. O'Sullivan, David G. Politte, Andrew D. Holmgren, Joel A. Greenberg, Lawrence Carin, David J. Brady

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Coded aperture X-ray coherent scatter imaging is a novel modality for ascertaining the molecular structure of an object. Measurements from different spatial locations and spectral channels in the object are multiplexed through a radiopaque material (coded aperture) onto the detectors. Iterative algorithms such as penalized expectation maximization (EM) and fully separable spectrally-grouped edge-preserving reconstruction have been proposed to recover the spatially-dependent coherent scatter spectral image from the multiplexed measurements. Such image recovery methods fall into the category of domain decomposition methods since they recover independent pieces of the image at a time. Ordered subsets has also been utilized in conjunction with penalized EM to accelerate its convergence. Ordered subsets is a range decomposition method because it uses parts of the measurements at a time to recover the image. In this paper, we analyze domain and range decomposition methods as they apply to coded aperture X-ray coherent scatter imaging using a spectrally-grouped edge-preserving regularizer and discuss the implications of the increased availability of parallel computational architecture on the choice of decomposition methods. We present results of applying the decomposition methods on experimental coded aperture X-ray coherent scatter measurements. Based on the results, an underlying observation is that updating different parts of the image or using different parts of the measurements in parallel, decreases the rate of convergence, whereas using the parts sequentially can accelerate the rate of convergence.

Original languageEnglish
Title of host publicationAnomaly Detection and Imaging with X-Rays (ADIX)
EditorsMichael E. Gehm, Amit Ashok, Mark A. Neifeld
PublisherSPIE
ISBN (Electronic)9781510600881
DOIs
StatePublished - Jan 1 2016
EventAnomaly Detection and Imaging with X-Rays (ADIX) Conference - Baltimore, United States
Duration: Apr 19 2016Apr 20 2016

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume9847
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceAnomaly Detection and Imaging with X-Rays (ADIX) Conference
Country/TerritoryUnited States
CityBaltimore
Period04/19/1604/20/16

Keywords

  • ADMM
  • Coded aperture X-ray imaging
  • Distributed optimization
  • Domain decomposition
  • Ordered subsets
  • Range decomposition
  • Spectrally grouped total variation regularizer
  • X-ray coherent scatter imaging

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