TY - GEN
T1 - Adaptive nonlocal means-regularized iterative image reconstruction for sparse-view CT
AU - Zhang, Hao
AU - Ma, Jianhua
AU - Wang, Jing
AU - Liu, Yan
AU - Han, Hao
AU - Moore, William
AU - Salerno, Michael
AU - Liang, Zhengrong
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2016/3/10
Y1 - 2016/3/10
N2 - Low-dose X-ray computed tomography (CT) imaging is desirable for various clinical applications due to the growing concerns about excessive radiation exposure to the patients. One strategy to achieve low-dose CT imaging is to lower the number of projection views per rotation during data acquisition. However, the resulting image by the conventional filtered back-projection method may suffer from view-aliasing artifacts due to insufficient angular sampling. In this work, we propose a nonlocal means (NLM)-regularized iterative reconstruction scheme for low-dose CT from sparse-view acquisitions. In order to improve the quality of reconstructed images, we further introduce spatial adaptivity to the NLM-based regularization by considering the local characteristics of images. The resulting approach is termed as adaptive NLM-regularized iterative image reconstruction. Experimental results demonstrated the feasibility of the presented reconstruction scheme for sparse-view CT and the superiority of incorporating the spatial adaptivity.
AB - Low-dose X-ray computed tomography (CT) imaging is desirable for various clinical applications due to the growing concerns about excessive radiation exposure to the patients. One strategy to achieve low-dose CT imaging is to lower the number of projection views per rotation during data acquisition. However, the resulting image by the conventional filtered back-projection method may suffer from view-aliasing artifacts due to insufficient angular sampling. In this work, we propose a nonlocal means (NLM)-regularized iterative reconstruction scheme for low-dose CT from sparse-view acquisitions. In order to improve the quality of reconstructed images, we further introduce spatial adaptivity to the NLM-based regularization by considering the local characteristics of images. The resulting approach is termed as adaptive NLM-regularized iterative image reconstruction. Experimental results demonstrated the feasibility of the presented reconstruction scheme for sparse-view CT and the superiority of incorporating the spatial adaptivity.
UR - https://www.scopus.com/pages/publications/84965068995
U2 - 10.1109/NSSMIC.2014.7430948
DO - 10.1109/NSSMIC.2014.7430948
M3 - 会议稿件
AN - SCOPUS:84965068995
T3 - 2014 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2014
BT - 2014 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2014
Y2 - 8 November 2014 through 15 November 2014
ER -