Beam hardening artifact removal by the fusion of FBP and deep neural networks

Csaba Olasz; László G. Varga; Antal Nagy

doi:10.1117/12.2601071

30 June 2021 Beam hardening artifact removal by the fusion of FBP and deep neural networks

Csaba Olasz, László G. Varga, Antal Nagy

Proceedings Volume 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021); 1187817 (2021) https://doi.org/10.1117/12.2601071
Event: Thirteenth International Conference on Digital Image Processing, 2021, Singapore, Singapore

Abstract

Beam hardening caused cupping and light or dark streaks are one of the major problems in computer tomographic imaging. There are many possibilities to manage beam hardening artifacts. Deep learning is a set of techniques that proved to be useful in various fields, and it is more and more commonly used in computer tomography as well. Our goal in this paper was to reduce beam hardening artifact using deep convolutional neural networks and to develop a model, which can improve the quality of the beam hardening distorted image with the presence of high amount of electrical noise. Moreover, we investigated various ways of fusing deep neural networks with the process of computer tomographic imaging. For these purposes we produced a large heterogeneous simulated dataset. We tested the performance of four convolutional networks-based methods for correcting the distorted data at various stages of the tomographic imaging pipeline. The evaluations were based on three different error measures. The evaluations have shown that the best result can be achieved by the method which makes the reconstructions step as a part a neural network. This method was capable of removing a significant amount of distortion in case of high level of electrical noise.

Citation Download Citation

Csaba Olasz, László G. Varga, and Antal Nagy "Beam hardening artifact removal by the fusion of FBP and deep neural networks", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 1187817 (30 June 2021); https://doi.org/10.1117/12.2601071

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