摘要
The low-density imaging performance of a zone plate-based nano-resolution hard x-ray computed tomography(CT)system can be significantly improved by incorporating a grating-based Lau interferometer. Due to the diffraction, however,the acquired nano-resolution phase signal may suffer splitting problem, which impedes the direct reconstruction of phase contrast CT(nPCT) images. To overcome, a new model-driven nPCT image reconstruction algorithm is developed in this study. In it, the diffraction procedure is mathematically modeled into a matrix B, from which the projections without signal splitting can be generated invertedly. Furthermore, a penalized weighted least-square model with total variation(PWLSTV) is employed to denoise these projections, from which nPCT images with high accuracy are directly reconstructed.Numerical experiments demonstrate that this new algorithm is able to work with phase projections having any splitting distances. Moreover, results also reveal that nPCT images of higher signal-to-noise-ratio(SNR) could be reconstructed from projections having larger splitting distances. In summary, a novel model-driven nPCT image reconstruction algorithm with high accuracy and robustness is verified for the Lau interferometer-based hard x-ray nano-resolution phase contrast imaging.
作者
谭雨航
蔡学宝
杨杰成
苏婷
郑海荣
梁栋
朱佩平
葛永帅
Yuhang Tan;Xuebao Cai;Jiecheng Yang;Ting Su;Hairong Zheng;Dong Liang;Peiping Zhu;Yongshuai Ge(Research Center for Medical Artificial Intelligence,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China;Institute of Electronic Paper Displays,South China Academy of Advanced Optoelectronics,South China Normal University,Guangzhou 510006,China;Paul C Lauterbur Research Center for Biomedical Imaging,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China;Institute of High Energy Physics,Chinese Academy of Sciences,Beijing 100049,China)
基金
Project supported by the National Natural Science Foundation of China(Grant No.12027812)
the Guangdong Basic and Applied Basic Research Foundation of Guangdong Province,China(Grant No.2021A1515111031)。
