Adaptive nonlocal means-based regularization for statistical image reconstruction of low-dose X-ray CT

Hao Zhang; Jianhua Ma; Jing Wang; Yan Liu; Hao Han; Lihong Li; William Moore; Zhengrong Liang

doi:10.1117/12.2082244

18 March 2015 Adaptive nonlocal means-based regularization for statistical image reconstruction of low-dose X-ray CT

Hao Zhang, Jianhua Ma, Jing Wang, Yan Liu, Hao Han, Lihong Li, William Moore, Zhengrong Liang

Proceedings Volume 9412, Medical Imaging 2015: Physics of Medical Imaging; 94123K (2015) https://doi.org/10.1117/12.2082244
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

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.

Citation Download Citation

Hao Zhang, Jianhua Ma, Jing Wang, Yan Liu, Hao Han, Lihong Li, William Moore, and Zhengrong Liang "Adaptive nonlocal means-based regularization for statistical image reconstruction of low-dose X-ray CT ", Proc. SPIE 9412, Medical Imaging 2015: Physics of Medical Imaging, 94123K (18 March 2015); https://doi.org/10.1117/12.2082244

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