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

Pascal Thériault Lauzier; Jie Tang; Guang-Hong Chen

doi:10.1117/12.878408

3 March 2011 Quantitative evaluation method of noise texture for iteratively reconstructed x-ray CT images

Pascal Thériault Lauzier, Jie Tang, Guang-Hong Chen

Author Affiliations +

Proceedings Volume 7961, Medical Imaging 2011: Physics of Medical Imaging; 796135 (2011) https://doi.org/10.1117/12.878408
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

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.

Citation Download Citation

Pascal Thériault Lauzier, Jie Tang, and Guang-Hong Chen "Quantitative evaluation method of noise texture for iteratively reconstructed x-ray CT images", Proc. SPIE 7961, Medical Imaging 2011: Physics of Medical Imaging, 796135 (3 March 2011); https://doi.org/10.1117/12.878408

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