title: Statistical image reconstruction for low-dose CT using nonlocal means-based regularization. Part II: An adaptive approach.
abstract: To reduce radiation dose in X-ray computed tomography (CT) imaging, one common strategy is to lower the tube current and exposure time settings during projection data acquisition. However, this strategy would inevitably increase the projection data noise, and the resulting image by the conventional filtered back-projection (FBP) method may suffer from excessive noise and streak artifacts. The well-known edge-preserving nonlocal means (NLM) filtering can reduce the noise-induced artifacts in the FBP reconstructed image, but it sometimes cannot completely eliminate the artifacts, especially under the very low-dose circumstance when the image is severely degraded. Instead of taking NLM filtering, we proposed a NLM-regularized statistical image reconstruction scheme, which can effectively suppress the noise-induced artifacts and significantly improve the reconstructed image quality. From our previous investigation on NLM-based strategy, we noted that using a spatially invariant filtering parameter in the regularization was rarely optimal for the entire field of view (FOV). Therefore, in this study we developed a novel strategy for designing spatially variant filtering parameters which are adaptive to the local characteristics of the image to be reconstructed. This adaptive NLM-regularized statistical image reconstruction method was evaluated with low-contrast phantoms and clinical patient data to show (1) the necessity in introducing the spatial adaptivity and (2) the efficacy of the adaptivity in achieving superiority in reconstructing CT images from low-dose acquisitions.
authors: Department of Radiology, State University of New York at Stony Brook, NY 11794, USA; Department of Biomedical Engineering, State University of New York at Stony Brook, NY 11794, USA.
authors: Hao
authors: H
authors: Zhang
authors: Department of Radiology, State University of New York at Stony Brook, NY 11794, USA; School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China.
authors: Jianhua
authors: J
authors: Ma
authors: Department of Radiation Oncology, University of Texas Southwestern Medical Center, TX 75390, USA.
authors: Jing
authors: J
authors: Wang
authors: Department of Radiology, State University of New York at Stony Brook, NY 11794, USA.
authors: Yan
authors: Y
authors: Liu
authors: Department of Radiology, State University of New York at Stony Brook, NY 11794, USA.
authors: Hao
authors: H
authors: Han
authors: Department of Biomedical Engineering, Fourth Military Medical University, Shaanxi 710032, China.
authors: Hongbing
authors: H
authors: Lu
authors: Department of Radiology, State University of New York at Stony Brook, NY 11794, USA.
authors: William
authors: W
authors: Moore
authors: Department of Radiology, State University of New York at Stony Brook, NY 11794, USA; Department of Biomedical Engineering, State University of New York at Stony Brook, NY 11794, USA. Electronic address: jerome.liang@sunysb.edu.
authors: Zhengrong
authors: Z
authors: Liang
mesh_terms: D000465:Algorithms; D006801:Humans; D019047:Phantoms, Imaging; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013223:Statistics as Topic; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Adaptive nonlocal means; Low-dose; Statistical image reconstruction; X-ray CT