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Proceedings Paper

A noise variance estimation approach for CT
Author(s): Le Shen; Xin Jin; Yuxiang Xing
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Paper Abstract

The Poisson-like noise model has been widely used for noise suppression and image reconstruction in low dose computed tomography. Various noise estimation and suppression approaches have been developed and studied to enhance the image quality. Among them, the recently proposed generalized Anscombe transform (GAT) has been utilized to stabilize the variance of Poisson-Gaussian noise. In this paper, we present a variance estimation approach using GAT. After the transform, the projection data is denoised conventionally with an assumption that the noise variance is uniformly equals to 1. The difference of the original and the denoised projection is treated as pure noise and the global variance σ2 can be estimated from the residual difference. Thus, the final denoising step with the estimated σ2 is performed. The proposed approach is verified on a cone-beam CT system and demonstrated to obtain a more accurate estimation of the actual parameter. We also examine FBP algorithm with the two-step noise suppression in the projection domain using the estimated noise variance. Reconstruction results with simulated and practical projection data suggest that the presented approach could be effective in practical imaging applications.

Paper Details

Date Published: 17 October 2012
PDF: 8 pages
Proc. SPIE 8506, Developments in X-Ray Tomography VIII, 85061M (17 October 2012); doi: 10.1117/12.928828
Show Author Affiliations
Le Shen, Tsinghua Univ. (China)
Xin Jin, Tsinghua Univ. (China)
Yuxiang Xing, Tsinghua Univ. (China)


Published in SPIE Proceedings Vol. 8506:
Developments in X-Ray Tomography VIII
Stuart R. Stock, Editor(s)

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