Iterative reconstruction for photon-counting CT using prior image constrained total generalized variation

Shanzhou Niu, You Zhang, Yuncheng Zhong, Guoliang Liu, Shaohui Lu, Xile Zhang, Shengzhou Hu, Tinghua Wang, Gaohang Yu, Jing Wang

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

In this paper, we present an iterative reconstruction for photon-counting CT using prior image constrained total generalized variation (PICTGV). This work aims to exploit structural correlation in the energy domain to reduce image noise in photon-counting CT with narrow energy bins. This is motived by the fact that the similarity between high-quality full-spectrum image and target image is an important prior knowledge for photon-counting CT reconstruction. The PICTGV method is implemented using a splitting-based fast iterative shrinkage-threshold algorithm (FISTA). Evaluations conducted with simulated and real photon-counting CT data demonstrate that PICTGV method outperforms the existing prior image constrained compressed sensing (PICCS) method in terms of noise reduction, artifact suppression and resolution preservation. In the simulated head data study, the average relative root mean squared error is reduced from 2.3% in PICCS method to 1.2% in PICTGV method, and the average universal quality index increases from 0.67 in PICCS method to 0.76 in PICTGV method. The results show that the present PICTGV method improves the performance of the PICCS method for photon-counting CT reconstruction with narrow energy bins.

Original languageEnglish (US)
Pages (from-to)167-182
Number of pages16
JournalComputers in Biology and Medicine
Volume103
DOIs
StatePublished - Dec 1 2018

Keywords

  • Image reconstruction
  • Photon-counting CT
  • Prior image
  • Total generalized variation

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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