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Proceedings Paper

Unbiased statistical image reconstruction in low-dose CT
Author(s): John Hayes; Ran Zhang; Chengzhu Zhang; Daniel Gomez-Cardona; Guang-Hong Chen
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Paper Abstract

When x-ray exposure level is lowered in the attempt to reduce radiation dose in x-ray computed tomography (CT), noise level is elevated. While this is well known in the community, less attention has been paid to another critically important fact in low dose CT: the accuracy of CT number is also compromised. Namely, CT numbers for some organs are increased while the CT number may be decreased in some other organs. The application of denoising methods can reduce noise level, but the denoising method, generally speaking, does not reduce the CT number biases. This has been shown in systematic experimental studies using clinically available reconstruction methods such as the conventional filtered backprojection or the statistical model based image reconstruction method. Although it has been known that the bias can be eliminated in statistical reconstruction if the Poisson log-likelihood function is not approximated by its quadratic form, the computation cost is quite expensive and thus these types of methods are not used in currently available commercial CT products. In this paper, we present an innovative way to design the statistical weighting function to enable unbiased statistical reconstruction with a quadratic data fidelity term and a regularizer.

Paper Details

Date Published: 13 March 2019
PDF: 6 pages
Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 1094859 (13 March 2019); doi: 10.1117/12.2512848
Show Author Affiliations
John Hayes, Univ. of Wisconsin-Madison (United States)
Ran Zhang, Univ. of Wisconsin-Madison (United States)
Chengzhu Zhang, Univ. of Wisconsin-Madison (United States)
Daniel Gomez-Cardona, Univ. of Wisconsin-Madison (United States)
Guang-Hong Chen, Univ. of Wisconsin-Madison (United States)

Published in SPIE Proceedings Vol. 10948:
Medical Imaging 2019: Physics of Medical Imaging
Taly Gilat Schmidt; Guang-Hong Chen; Hilde Bosmans, Editor(s)

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