Dictionary learning based low-dose x-ray CT reconstruction using a balancing principle

Xuanqin Mou, Junfeng Wu, Ti Bai, Qiong Xu, Hengyong Yu, Ge Wang

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

12 Scopus citations


The high utility and wide applicability of x-ray imaging has led to a rapidly increased number of CT scans over the past years, and at the same time an elevated public concern on the potential risk of x-ray radiation to patients. Hence, a hot topic is how to minimize x-ray dose while maintaining the image quality. The low-dose CT strategies include modulation of x-ray flux and minimization of dataset size. However, these methods will produce noisy and insufficient projection data, which represents a great challenge to image reconstruction. Our team has been working to combine statistical iterative methods and advanced image processing techniques, especially dictionary learning, and have produced excellent preliminary results. In this paper, we report recent progress in dictionary learning based low-dose CT reconstruction, and discuss the selection of regularization parameters that are crucial for the algorithmic optimization. The key idea is to use a "balance principle" based on a model function to choose the regularization parameters during the iterative process, and to determine a weight factor empirically for address the noise level in the projection domain. Numerical and experimental results demonstrate the merits of our proposed reconstruction approach.

Original languageEnglish (US)
Title of host publicationDevelopments in X-Ray Tomography IX
EditorsStuart R. Stock
ISBN (Electronic)9781628412390
StatePublished - 2014
Externally publishedYes
EventDevelopments in X-Ray Tomography IX - San Diego, United States
Duration: Aug 18 2014Aug 20 2014

Publication series

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


ConferenceDevelopments in X-Ray Tomography IX
Country/TerritoryUnited States
CitySan Diego


  • Balance principle
  • Dictionary learning
  • Image reconstruction
  • Low-dose
  • X-ray CT

ASJC Scopus subject areas

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


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