A HYBRID APPROACH BASED ON TOTAL VARIATION AND DEEP NEURAL NETWORKS FOR IMAGE RECONSTRUCTION IN LIMITED-ANGLE X-RAY TOMOGRAPHY
Keywords:
To address this, we propose a novel hybrid system that sequentially combines Total Variation (TV) regularization with a highly optimized Deep Neural Network (DNN).Abstract
Limited-angle X-ray tomography (LAT) plays an important role in non-destructive inspection in situations where full 360° data acquisition is physically limited, such as specialized medical imaging and industrial applications. However, the incompleteness of the projection data makes the underlying inverse problem highly ill-posed. Traditional analytical methods and iterative reconstruction methods based on standard models suffer from serious “missing wedge” artifacts and low numerical stability. Although stand-alone deep learning approaches have shown promising results in artifact removal, they often lack the reliability of physical information and exhibit unexpected generalization in non-distributional scanning processes.
Downloads
References
Natterer, F. (2001). The Mathematics of Computerized Tomography. Society for Industrial and Applied Mathematics (SIAM).
Hansen, P. C. (2010). Discrete Inverse Problems: Insight and Algorithms. SIAM.
Buzug, T. M. (2011). Computed Tomography: From Photon Statistics to Modern Cone-Beam CT. Springer Science & Business Media.
Sidky, E. Y., & Pan, X. (2008). Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization. Physics in Medicine & Biology, 53(17), 4777-4807.
Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011). Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine Learning, 3(1), 1-122.
Chambolle, A., & Pock, T. (2011). A first-order primal-dual algorithm for convex problems with applications to imaging. Journal of Mathematical Imaging and Vision, 40(1), 120-145.
Jin, K. H., McCann, M. T., Froustey, E., & Unser, M. (2017). Deep convolutional neural network for inverse problems in imaging. IEEE Transactions on Image Processing, 26(9), 4509-4522.
Wang, G., Ye, J. C., Mueller, K., & Fessler, J. A. (2018). Image reconstruction is a new frontier of machine learning. IEEE Transactions on Medical Imaging, 37(6), 1289-1296.
Antun, V., Renna, F., Poon, C., Adcock, B., & Hansen, A. C. (2020). On instabilities of deep learning in image reconstruction and the potential costs of AI. Proceedings of the National Academy of Sciences, 117(48), 30088-30095.
Venkatakrishnan, S. V., Bouman, C. A., & Wohlberg, B. (2013). Plug-and-play priors for model based reconstruction. In 2013 IEEE Global Conference on Signal and Information Processing (pp. 945-948). IEEE.
Adler, J., & Öktem, O. (2018). Learned primal-dual reconstruction. IEEE Transactions on Medical Imaging, 37(6), 1322-1332.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All content published in the Journal of Applied Science and Social Science (JASSS) is protected by copyright. Authors retain the copyright to their work, and grant JASSS the right to publish the work under a Creative Commons Attribution License (CC BY). This license allows others to distribute, remix, adapt, and build upon the work, even commercially, as long as they credit the author(s) for the original creation.
