Deep-learning-based ring artifact correction for tomographic reconstruction

被引:9
|
作者
Fu, Tianyu [1 ,2 ]
Wang, Yan [1 ]
Zhang, Kai [1 ,2 ]
Zhang, Jin [1 ]
Wang, Shanfeng [1 ]
Huang, Wanxia [1 ]
Wang, Yaling [3 ,4 ]
Yao, Chunxia [1 ]
Zhou, Chenpeng [1 ,2 ]
Yuan, Qingxi [1 ]
机构
[1] Chinese Acad Sci, Beijing Synchrotron Radiat Facil, Inst High Energy Phys, Xray Opt & Technol Lab, Yuquan Rd, Beijing 010000, Peoples R China
[2] Univ Chinese Acad Sci, Yuquan Rd, Beijing 010000, Peoples R China
[3] Natl Ctr Nanosci & Technol China, CAS Key Lab Biomed Effects Nanomed & Nanosafety, Beijing 100190, Peoples R China
[4] Natl Ctr Nanosci & Technol China, CAS Ctr Excellence Nanosci, Beijing 100190, Peoples R China
基金
中国国家自然科学基金;
关键词
ring artifact correction; X-ray tomography; deep learning; residual neural network; RAY; NETWORK;
D O I
10.1107/S1600577523000917
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
X-ray tomography has been widely used in various research fields thanks to its capability of observing 3D structures with high resolution non-destructively. However, due to the nonlinearity and inconsistency of detector pixels, ring artifacts usually appear in tomographic reconstruction, which may compromise image quality and cause nonuniform bias. This study proposes a new ring artifact correction method based on the residual neural network (ResNet) for X-ray tomography. The artifact correction network uses complementary information of each wavelet coefficient and a residual mechanism of the residual block to obtain high-precision artifacts through low operation costs. In addition, a regularization term is used to accurately extract stripe artifacts in sinograms, so that the network can better preserve image details while accurately separating artifacts. When applied to simulation and experimental data, the proposed method shows a good suppression of ring artifacts. To solve the problem of insufficient training data, ResNet is trained through the transfer learning strategy, which brings advantages of robustness, versatility and low computing cost.
引用
收藏
页码:620 / 626
页数:7
相关论文
共 50 条
  • [31] Deep-Learning-Based 3-D Surface Reconstruction-A Survey
    Farshian, Anis
    Goetz, Markus
    Cavallaro, Gabriele
    Debus, Charlotte
    Niessner, Matthias
    Benediktsson, Jon Atli
    Streit, Achim
    PROCEEDINGS OF THE IEEE, 2023, 111 (11) : 1464 - 1501
  • [32] Deep-learning-based image reconstruction with limited data: generating synthetic raw data using deep learning
    Zijlstra, Frank
    While, Peter Thomas
    MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE, 2024, 37 (06): : 1059 - 1076
  • [33] Comparison of Gaussian and vortex probe beams for deep-learning-based turbulence correction
    Na Y.
    Ko D.-K.
    Optik, 2024, 308
  • [34] Deep-learning-based ghost imaging
    Meng Lyu
    Wei Wang
    Hao Wang
    Haichao Wang
    Guowei Li
    Ni Chen
    Guohai Situ
    Scientific Reports, 7
  • [35] Deep-learning-based ghost imaging
    Lyu, Meng
    Wang, Wei
    Wang, Hao
    Wang, Haichao
    Li, Guowei
    Chen, Ni
    Situ, Guohai
    SCIENTIFIC REPORTS, 2017, 7
  • [36] New technologies for X-ray Microscopy: phase correction and fully automated deep learning based tomographic reconstruction
    Andrew, Matthew
    Omlor, Lars
    Andreyev, Andriy
    Sanapala, Ravikumar
    Khoshkhoo, Mohsen Samadi
    DEVELOPMENTS IN X-RAY TOMOGRAPHY XIII, 2021, 11840
  • [37] Deep-learning-based binary hologram
    Goi, Hiroaki
    Komuro, Koshi
    Nomura, Takanori
    APPLIED OPTICS, 2020, 59 (23) : 7103 - 7108
  • [38] Deep-Learning-Based Source Reconstruction Method Using Deep Convolutional Conditional Generative Adversarial Network
    Yao, He Ming
    Jiang, Lijun
    Ng, Michael
    IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, 2024, 72 (05) : 2949 - 2960
  • [39] Deep-Learning-Based Low-Frequency Reconstruction in Full-Waveform Inversion
    Gu, Zhiyuan
    Chai, Xintao
    Yang, Taihui
    REMOTE SENSING, 2023, 15 (05)
  • [40] Deep-Learning-Based Radio Map Reconstruction for V2X Communications
    Roger, Sandra
    Brambilla, Mattia
    Tedeschini, Bernardo Camajori
    Botella-Mascarell, Carmen
    Cobos, Maximo
    Nicoli, Monica
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2024, 73 (03) : 3863 - 3871