Image Reconstruction with Variational Networks: Application to Synchrotron Radiation Imaging

Rashed EA, Kudo H, Maier A (2018)

Publication Type: Conference contribution

Publication year: 2018

Publisher: Institute of Electrical and Electronics Engineers Inc.

Conference Proceedings Title: 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Proceedings

Event location: Sydney, NSW AU

ISBN: 9781538684948

DOI: 10.1109/NSSMIC.2018.8824367

Abstract

In computed tomography, image reconstructed from limited projection data is subject to strong noise and artifacts. Reducing the number of projection views in synchrotron radiation imaging has a great benefits in decreasing the computation cost for image formation. Moreover, it also prevents the damage of biological specimen caused by the x-ray radiation exposure. In this paper, a new procedure for image reconstruction from limited projection views with the assist of deep learning approach is proposed. A deep learning approach is employed to estimate an initial guess of the tomographic image followed by a fast row-action reconstruction to guarantee the restoration of any pattern missed in the first stage. Most of previous attempts that use deep learning in image reconstruction are implemented as a post-processing techniques applied on the reconstructed image. This may lead to anomalies in the final result due to the lack of consistency between the acquired projection data and the reconstructed image. The proposed procedure solves this problem by keeping the data consistency up to the final stage of image formation. This would reduce the possibility of losing image abnormal structures due to insufficient training in deep learning. Experimental results using synchrotron radiation data demonstrate the usefulness of the proposed framework.

Authors with CRIS profile

Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

University of Tsukuba / 筑波大学 JP Japan (JP) British University in Egypt EG Egypt (EG)

How to cite

APA:

Rashed, E.A., Kudo, H., & Maier, A. (2018). Image Reconstruction with Variational Networks: Application to Synchrotron Radiation Imaging. In 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Proceedings. Sydney, NSW, AU: Institute of Electrical and Electronics Engineers Inc..

MLA:

Rashed, Essam A., Hiroyuki Kudo, and Andreas Maier. "Image Reconstruction with Variational Networks: Application to Synchrotron Radiation Imaging." Proceedings of the 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018, Sydney, NSW Institute of Electrical and Electronics Engineers Inc., 2018.

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