Physics-informed deep neural network reconstruction framework for propagation-based x ray phase-contrast computed tomography with sparse-view projections
Propagation-based phase contrast computed tomography (PB-PCCT) is an effective technique for three-dimensional visualization of weakly attenuating samples. However, the high radiation dose caused by the long sampling time has hindered the wider adoption of PB-PCCT. By incorporating the physical imaging model of PB-PCCT with a deep neural network, this Letter develops a physics-informed deep learning reconstruction framework for sparse-view PB-PCCT. Simulation and real experiments are performed to validate the effectiveness and capability of the proposed framework. Results show that the proposed framework obtains phaseretrieved and streaking artifacts removed PB-PCCT images from only one sparse-view measured intensity without any pretrained network and labeled data. (C) 2022 Optica Publishing Group
基金：
National Natural Science Foundation of China [82071922, 82102037, 82001813, 81671683]; Natural Science Foundation of Tian-jin City [16JCYBJC28600]; Tianjin Municipal Education Commission [2020KJ208]
第一作者单位：[1]Tianjin Med Univ, Sch Biomed Engn & Technol, Tianjin 300070, Peoples R China
通讯作者：
推荐引用方式(GB/T 7714)：
Li Fangzhi,Zhao Yuqing,Han Shuo,et al.Physics-informed deep neural network reconstruction framework for propagation-based x ray phase-contrast computed tomography with sparse-view projections[J].OPTICS LETTERS.2022,47(16):4259-4262.doi:10.1364/OL.466306.
APA：
Li, Fangzhi,Zhao, Yuqing,Han, Shuo,Ji, Dongjiang,Li, Yimin...&Hu, Chunhong.(2022).Physics-informed deep neural network reconstruction framework for propagation-based x ray phase-contrast computed tomography with sparse-view projections.OPTICS LETTERS,47,(16)
MLA：
Li, Fangzhi,et al."Physics-informed deep neural network reconstruction framework for propagation-based x ray phase-contrast computed tomography with sparse-view projections".OPTICS LETTERS 47..16(2022):4259-4262
