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Proceedings Paper

Adaptive nonlocal means-based regularization for statistical image reconstruction of low-dose X-ray CT
Author(s): Hao Zhang; Jianhua Ma; Jing Wang; Yan Liu; Hao Han; Lihong Li; William Moore; Zhengrong Liang
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Paper Abstract

To reduce radiation dose in X-ray computed tomography (CT) imaging, one of the common strategies is to lower the milliampere-second (mAs) setting during projection data acquisition. However, this strategy would inevitably increase the projection data noise, and the resulting image by the filtered back-projection (FBP) method may suffer from excessive noise and streak artifacts. The edge-preserving nonlocal means (NLM) filtering can help to reduce the noise-induced artifacts in the FBP reconstructed image, but it sometimes cannot completely eliminate them, especially under very low-dose circumstance when the image is severely degraded. To deal with this situation, we proposed a statistical image reconstruction scheme using a NLM-based regularization, which can suppress the noise and streak artifacts more effectively. However, we noticed that using uniform filtering parameter in the NLM-based regularization was rarely optimal for the entire image. Therefore, in this study, we further developed a novel approach for designing adaptive filtering parameters by considering local characteristics of the image, and the resulting regularization is referred to as adaptive NLM-based regularization. Experimental results with physical phantom and clinical patient data validated the superiority of using the proposed adaptive NLM-regularized statistical image reconstruction method for low-dose X-ray CT, in terms of noise/streak artifacts suppression and edge/detail/contrast/texture preservation.

Paper Details

Date Published: 18 March 2015
PDF: 6 pages
Proc. SPIE 9412, Medical Imaging 2015: Physics of Medical Imaging, 94123K (18 March 2015); doi: 10.1117/12.2082244
Show Author Affiliations
Hao Zhang, Stony Brook Univ. (United States)
Jianhua Ma, Stony Brook Univ. (United States)
Southern Medical Univ (China)
Jing Wang, Univ. of Texas Southwestern Medical Ctr. (United States)
Yan Liu, Stony Brook Univ. (United States)
Hao Han, Stony Brook Univ. (United States)
Lihong Li, College of Staten Island-CUNY (United States)
William Moore, Stony Brook Univ. (United States)
Zhengrong Liang, Stony Brook Univ. (United States)


Published in SPIE Proceedings Vol. 9412:
Medical Imaging 2015: Physics of Medical Imaging
Christoph Hoeschen; Despina Kontos, Editor(s)

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