title: Smoothedl0Norm Regularization for Sparse-View X-Ray CT Reconstruction.
abstract: Low-dose computed tomography (CT) reconstruction is a challenging problem in medical imaging. To complement the standard filtered back-projection (FBP) reconstruction, sparse regularization reconstruction gains more and more research attention, as it promises to reduce radiation dose, suppress artifacts, and improve noise properties. In this work, we present an iterative reconstruction approach using improved smoothedl0(SL0) norm regularization which is used to approximatel0norm by a family of continuous functions to fully exploit the sparseness of the image gradient. Due to the excellent sparse representation of the reconstruction signal, the desired tissue details are preserved in the resulting images. To evaluate the performance of the proposed SL0 regularization method, we reconstruct the simulated dataset acquired from the Shepp-Logan phantom and clinical head slice image. Additional experimental verification is also performed with two real datasets from scanned animal experiment. Compared to the referenced FBP reconstruction and the total variation (TV) regularization reconstruction, the results clearly reveal that the presented method has characteristic strengths. In particular, it improves reconstruction quality via reducing noise while preserving anatomical features.
authors.affiliation: Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
authors.forename: Ming
authors.initials: M
authors.lastname: Li
authors.affiliation: Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
authors.forename: Cheng
authors.identifier: 0000-0002-2791-8271
authors.initials: C
authors.lastname: Zhang
authors.affiliation: Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
authors.forename: Chengtao
authors.initials: C
authors.lastname: Peng
authors.affiliation: PET Center, Huashan Hospital, Fudan University, Shanghai 200235, China.
authors.forename: Yihui
authors.initials: Y
authors.lastname: Guan
authors.affiliation: Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
authors.forename: Pin
authors.initials: P
authors.lastname: Xu
authors.affiliation: Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
authors.forename: Mingshan
authors.identifier: 0000-0002-0785-7387
authors.initials: M
authors.lastname: Sun
authors.affiliation: Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
authors.forename: Jian
authors.identifier: 0000-0003-4257-331X
authors.initials: J
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D000818:Animals; D016477:Artifacts; D003198:Computer Simulation; D006257:Head / Q000000981:diagnostic imaging; D006801:Humans; D051379:Mice; D015233:Models, Statistical; D019047:Phantoms, Imaging; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D059629:Signal-To-Noise Ratio; D014057:Tomography, X-Ray Computed / Q000379:methods*