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Proceedings Paper

Analytical noise treatment for low-dose CT projection data by penalized weighted least-square smoothing in the K-L domain
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Paper Abstract

By analyzing the noise properties of calibrated low-dose Computed Tomography (CT) projection data, it is clearly seen that the data can be regarded as approximately Gaussian distributed with a nonlinear signal-dependent variance. Based on this observation, the penalized weighted least-square (PWLS) smoothing framework is a choice for an optimal solution. It utilizes the prior variance-mean relationship to construct the weight matrix and the two-dimensional (2D) spatial information as the penalty or regularization operator. Furthermore, a K-L transform is applied along the z (slice) axis to further consider the correlation among different sinograms, resulting in a PWLS smoothing in the K-L domain. As a tool for feature extraction and de-correlation, the K-L transform maximizes the data variance represented by each component and simplifies the task of 3D filtering into 2D spatial process slice by slice. Therefore, by selecting an appropriate number of neighboring slices, the K-L domain PWLS smoothing fully utilizes the prior statistical knowledge and 3D spatial information for an accurate restoration of the noisy low-dose CT projections in an analytical manner. Experimental results demonstrate that the proposed method with appropriate control parameters improves the noise reduction without the loss of resolution.

Paper Details

Date Published: 3 May 2002
PDF: 7 pages
Proc. SPIE 4682, Medical Imaging 2002: Physics of Medical Imaging, (3 May 2002); doi: 10.1117/12.465552
Show Author Affiliations
Hongbing Lu, SUNY/Stony Brook (United States)
Xiang Li, SUNY/Stony Brook (United States)
Ing-Tsung Hsiao, SUNY/Stony Brook (Taiwan)
Zhengrong Liang, SUNY/Stony Brook (United States)


Published in SPIE Proceedings Vol. 4682:
Medical Imaging 2002: Physics of Medical Imaging
Larry E. Antonuk; Martin Joel Yaffe, Editor(s)

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