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Proceedings Paper

Quantitative evaluation method of noise texture for iteratively reconstructed x-ray CT images
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Paper Abstract

Recently, iterative image reconstruction algorithms have been extensively studied in x-ray CT in order to produce images with lower noise variance and high spatial resolution. However, the images thus reconstructed often have unnatural image noise textures, the potential impact of which on diagnostic accuracy is still unknown. This is particularly pronounced in total-variation-minimization-based image reconstruction, where the noise background often manifests itself as patchy artifacts. In this paper, a quantitative noise texture evaluation metric is introduced to evaluate the deviation of the noise histogram from that of images reconstructed using filtered backprojection. The proposed texture similarity metric is tested using TV-based compressive sampling algorithm (CSTV). It was demonstrated that the metric is sensitive to changes in the noise histogram independent of changes in noise level. The results demonstrate the existence tradeoff between the texture similarity metric and the noise level for the CSTV algorithm, which suggests a potential optimal amount of regularization. The same noise texture quantification method can also be utilized to evaluate the performance of other iterative image reconstruction algorithms.

Paper Details

Date Published: 3 March 2011
PDF: 6 pages
Proc. SPIE 7961, Medical Imaging 2011: Physics of Medical Imaging, 796135 (3 March 2011); doi: 10.1117/12.878408
Show Author Affiliations
Pascal Thériault Lauzier, Univ. of Wisconsin-Madison (United States)
Jie Tang, Univ. of Wisconsin-Madison (United States)
Guang-Hong Chen, Univ. of Wisconsin-Madison (United States)

Published in SPIE Proceedings Vol. 7961:
Medical Imaging 2011: Physics of Medical Imaging
Norbert J. Pelc; Ehsan Samei; Robert M. Nishikawa, Editor(s)

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