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Proceedings Paper

Statistical CT noise reduction with multi-scale decomposition and penalized weighted least square for incomplete projection data
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Paper Abstract

Tremendous efforts have been devoted to decreasing x-ray radiation dose in diagnostic CT while maintaining the image quality. The statistical noise reduction with iterative algorithm in the projection domain has been one of the major research subjects in CT technologies. Previously, we have proposed a statistical noise reduction with multi-scale decomposition and penalized weighted least square (PWLS) in the projection domain, in which the Markov Random Field (MRF) penalty function is incorporated. In this work, by taking the variation or irregularity of sampling interval along each dimension of the projection domain, we extend our previous method to deal with the situations of incomplete projection data, covering sparse view sampling, latitudinal data truncation and photon starvation. Using the computersimulated projection data of a performance phantom and the FORBILD thorax phantom, we evaluate and verify the performance of the proposed method.

Paper Details

Date Published: 6 March 2013
PDF: 10 pages
Proc. SPIE 8668, Medical Imaging 2013: Physics of Medical Imaging, 866839 (6 March 2013); doi: 10.1117/12.2008042
Show Author Affiliations
Shaojie Tang, Emory Univ. School of Medicine (United States)
Xi’an Univ. of Posts and Telecommunications (China)
Xiangyang Tang, Emory Univ. School of Medicine (United States)


Published in SPIE Proceedings Vol. 8668:
Medical Imaging 2013: Physics of Medical Imaging
Robert M. Nishikawa; Bruce R. Whiting; Christoph Hoeschen, Editor(s)

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