Paper
3 March 2011 Quantitative evaluation method of noise texture for iteratively reconstructed x-ray CT images
Author Affiliations +
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.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
Lens.org Logo
CITATIONS
Cited by 4 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Reconstruction algorithms

X-ray computed tomography

X-rays

Diagnostics

Image restoration

X-ray imaging

Image filtering

RELATED CONTENT

Radon inversion via deep learning
Proceedings of SPIE (March 01 2019)
X ray dual energy computed tomography by use of a...
Proceedings of SPIE (January 01 1900)
A ray-tracing backprojection algorithm for cone beam CT
Proceedings of SPIE (March 28 2007)
Improved WGMAP image restoration
Proceedings of SPIE (August 29 2009)

Back to Top