Sinogram preprocessing and binary reconstruction for determination of the shape and location of metal objects in computed tomography (CT)

Bowen Meng, Jing Wang, Lei Xing

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

Purpose: To develop a binary image reconstruction method for the autolocalization of metallic object(s) in CT with sparse projections. Methods: The authors divide the system into two types of contents: Metal(s) and nonmetal(s). The boundaries of metallic objects are obtained by using a penalized weighted least-squares algorithm with the adequate intensity gradient-controlled. A novel mechanism of "amplifying" the difference between metal(s) and nonmetallic substances is introduced by preprocessing the sinogram data, which is shown to be necessary in dealing with a case with sparse projection data. A series of experimental studies are performed to evaluate the proposed approach. Results: A novel binary CT image reconstruction formalism is established for the autodetermination of the shape and location of metallic objects in the presence of limited number of projections. Experimental studies reveal that the presented algorithm works well even when the embedded metal object(s) has different shape(s). It is also shown that when the projection data are sparse, a differential manipulation of projection data can greatly facilitate the binary reconstruction process and allow the authors to obtain accurate binary CT images that would otherwise be unattainable. Conclusions: Binary CT reconstruction provides a viable method for determining the geometric distribution information of the implanted metal objects in CT imaging.

Original languageEnglish (US)
Pages (from-to)5867-5875
Number of pages9
JournalMedical physics
Volume37
Issue number11
DOIs
StatePublished - Nov 2010

Keywords

  • CT
  • image reconstruction
  • iterative method
  • metal artifacts
  • sparse projections

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

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