Sparse-view phase-contrast and attenuation-based CT reconstruction utilizing model-driven deep learning  

作　　者：Xia-Yu Tao Qi-Si Lin Zhao Wu Yong Guan Yang-Chao Tian Gang Liu 

机构地区：[1]National Synchrotron Radiation Laboratory,University of Science and Technology of China,Hefei 230009,China

出　　处：《Nuclear Science and Techniques》2025年第4期59-71,共13页核技术(英文)

基　　金：supported by the National Natural Science Foundation of China(Nos.U2032148,U2032157,11775224);USTC Research Funds of the Double First-Class Initiative(No.YD2310002008);the National Key Research and Development Program of China(No.2017YFA0402904),the Youth Innovation Promotion Association,CAS(No.2020457)。

摘　　要：Grating-based X-ray phase-contrast imaging enhances the contrast of imaged objects,particularly soft tissues.However,the radiation dose in computed tomography(CT)is generally excessive owing to the complex collection scheme.Sparse-view CT collection reduces the radiation dose,but with reduced resolution and reconstructed artifacts particularly in analytical reconstruction methods.Recently,deep learning has been employed in sparse-view CT reconstruction and achieved stateof-the-art results.Nevertheless,its low generalization performance and requirement for abundant training datasets have hindered the practical application of deep learning in phase-contrast CT.In this study,a CT model was used to generate a substantial number of simulated training datasets,thereby circumventing the need for experimental datasets.By training a network with simulated training datasets,the proposed method achieves high generalization performance in attenuationbased CT and phase-contrast CT,despite the lack of sufficient experimental datasets.In experiments utilizing only half of the CT data,our proposed method obtained an image quality comparable to that of the filtered back-projection algorithm with full-view projection.The proposed method simultaneously addresses two challenges in phase-contrast three-dimensional imaging,namely the lack of experimental datasets and the high exposure dose,through model-driven deep learning.This method significantly accelerates the practical application of phase-contrast CT.

关 键 词：Sparse-view CT Phase-contrast CT Attenuation-based CT Deep learning network Frequency loss function 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]