Sparsity-regularized image reconstruction of decomposed K-edge data in spectral CT

Qiaofeng Xu, Alex Sawatzky, Mark A. Anastasio, Carsten O. Schirra

Research output: Contribution to journalArticlepeer-review

Abstract

The development of spectral computed tomography (CT) using binned photon-counting detectors has garnered great interest in recent years and has enabled selective imaging of K-edge materials. A practical challenge in CT image reconstruction of K-edge materials is the mitigation of image artifacts that arise from reduced-view and/or noisy decomposed sinogram data. In this note, we describe and investigate sparsity-regularized penalized weighted least squares-based image reconstruction algorithms for reconstructing K-edge images from few-view decomposed K-edge sinogram data. To exploit the inherent sparseness of typical K-edge images, we investigate use of a total variation (TV) penalty and a weighted sum of a TV penalty and an ℓ1-norm with a wavelet sparsifying transform. Computer-simulation and experimental phantom studies are conducted to quantitatively demonstrate the effectiveness of the proposed reconstruction algorithms.

Original languageEnglish (US)
Pages (from-to)N65-N79
JournalPhysics in medicine and biology
Volume59
Issue number10
DOIs
StatePublished - May 21 2014
Externally publishedYes

Keywords

  • computed tomography (CT)
  • energy-resolved CT
  • K-edge imaging
  • material-decomposition
  • statistical image reconstruction
  • total variation regularization

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging

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