Adaptive Nonlocal Means-Regularized Iterative Image Reconstruction for Sparse-View CT

作者:Zhang, Hao; Ma, Jianhua; Wang, Jing; Liu, Yan; Han, Hao; Moore, William; Salerno, Michael; Liang, Zhengrong*
来源:IEEE Nuclear Science Symposium / Medical Imaging Conference (NSS/MIC), 2014-11-08 To 2014-11-15.
DOI:10.1109/nssmic.2014.7430948

摘要

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.