TY - GEN
T1 - Adaptive nonlocal means-based regularization for statistical image reconstruction of low-dose X-ray CT
AU - Zhang, Hao
AU - Ma, Jianhua
AU - Wang, Jing
AU - Liu, Yan
AU - Han, Hao
AU - Li, Lihong
AU - Moore, William
AU - Liang, Zhengrong
N1 - Publisher Copyright:
© 2015 SPIE.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Adaptive nonlocal means
KW - Low-dose
KW - Penalized weighted least-squares
KW - Regularization
KW - Statistical image reconstruction
KW - X-ray CT
UR - http://www.scopus.com/inward/record.url?scp=84943328977&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84943328977&partnerID=8YFLogxK
U2 - 10.1117/12.2082244
DO - 10.1117/12.2082244
M3 - Conference contribution
AN - SCOPUS:84943328977
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2015
A2 - Hoeschen, Christoph
A2 - Kontos, Despina
A2 - Hoeschen, Christoph
PB - SPIE
T2 - Medical Imaging 2015: Physics of Medical Imaging
Y2 - 22 February 2015 through 25 February 2015
ER -