Adaptive-weighted high order TV algorithm for sparse-view CT reconstruction

作     者：Xi, Yarui Zhou, Pengwu Yu, Haijun Zhang, Tao Zhang, Lingli Qiao, Zhiwei Liu, Fenglin 

作者机构：Chongqing Univ Key Lab Optoelect Technol & Syst Minist Educ Chongqing Peoples R China Chongqing Univ Engn Res Ctr Ind Computed Tomog Nondestruct Testin Minist Educ Chongqing Peoples R China Chongqing Univ Coll Mech & Vehicle Engn Chongqing Peoples R China Chongqing Univ Arts & Sci Chongqing Key Lab Complex Data Anal & Artificial I Chongqing Peoples R China Chongqing Univ Arts & Sci Chongqing Key Lab Grp & Graph Theories & Applicat Chongqing Peoples R China Shanxi Univ Sch Comp & Informat Technol Taiyuan Shanxi Peoples R China Chongqing Univ Key Lab Optoelect Technol & Syst Minist Educ Chongqing 400044 Peoples R China 

出 版 物：《MEDICAL PHYSICS》 (医疗物理学)

年 卷 期：2023年第50卷第9期

页      面：5568-5584页

核心收录：

学科分类：1001[医学-基础医学(可授医学、理学学位)] 1009[医学-特种医学] 10[医学] 

基　　金：Natural Science Foundation of Chongqing [CSTB2022NSCQ-MSX1311] National Natural Science Foundation of China National Key Research and Development Project [2022YFF0706400] Postdoctoral Fund of Natural Science Foundation of Chongqing Municipal Science and Technology Commission [cstc2021jcyj-bsh0232] Science and Technology Research Program of Chongqing Municipal Education Commission [KJQN201901317] 

主　　题：adaptive-weighted high order total variation Chambolle-Pock algorithm compressed sensing iterative algorithm image reconstruction 

摘      要：BackgroundWith the development of low-dose computed tomography (CT), incomplete data reconstruction has been widely concerned. The total variation (TV) minimization algorithm can accurately reconstruct images from sparse or noisy data. PurposeHowever, the traditional TV algorithm ignores the direction of structures in images, leading to the loss of edge information and block artifacts when the object is not piecewise constant. Since the anisotropic information can facilitate preserving the edge and detail information in images, we aim to improve the TV algorithm in terms of reconstruction accuracy via this approach. MethodsIn this paper, we propose an adaptive-weighted high order total variation (awHOTV) algorithm. We construct the second order TV-norm using the second order gradient, adapt the anisotropic edge property between neighboring image pixels, adjust the local image-intensity gradient to keep edge information, and design the corresponding Chambolle-Pock (CP) solving algorithm. Implementing the proposed algorithm, comprehensive studies are conducted in the ideal projection data experiment where the Structural similarity (SSIM), Root Mean Square Error (RMSE), Contrast to noise ratio (CNR), and modulation transform function (MTF) curves are utilized to evaluate the quality of reconstructed images in statism, structure, spatial resolution, and contrast, respectively. In the noisy data experiment, we further use the noise power spectrum (NPS) curve to evaluate the reconstructed images and compare it with other three algorithms. ResultsWe use the 2D slice in the XCAT phantom, 2D slice in TCIA Challenge data and FORBILD phantom as simulation phantoms and use real bird data for real verification. The results show that, compared with the traditional TV and FBP algorithms, the awHOTV has better performance in terms of RMSE, SSIM, and Pearson correlation coefficient (PCC) under the projected data with different sparsity. In addition, the awHOTV algorithm is robust ag