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Article

Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications

1
Center for Industrial Mathematics, University of Bremen, Bibliothekstr. 5, 28359 Bremen, Germany
2
Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands
3
The Mathematical Institute, Leiden University, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
4
Computer Assisted Clinical Medicine, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany
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School of Biomedical Engineering, University of British Columbia, 2222 Health Sciences Mall, Vancouver, BC V6T 1Z3, Canada
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Leiden Institute of Advanced Computer Science, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
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Department of Biomedical Engineering, Eindhoven University of Technology, Groene Loper 3, 5612 AE Eindhoven, The Netherlands
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Yudong Zhang, Juan Manuel Gorriz and Zhengchao Dong
J. Imaging 2021, 7(3), 44; https://doi.org/10.3390/jimaging7030044
Received: 29 January 2021 / Revised: 21 February 2021 / Accepted: 22 February 2021 / Published: 2 March 2021
(This article belongs to the Special Issue Deep Learning in Medical Image Analysis)
The reconstruction of computed tomography (CT) images is an active area of research. Following the rise of deep learning methods, many data-driven models have been proposed in recent years. In this work, we present the results of a data challenge that we organized, bringing together algorithm experts from different institutes to jointly work on quantitative evaluation of several data-driven methods on two large, public datasets during a ten day sprint. We focus on two applications of CT, namely, low-dose CT and sparse-angle CT. This enables us to fairly compare different methods using standardized settings. As a general result, we observe that the deep learning-based methods are able to improve the reconstruction quality metrics in both CT applications while the top performing methods show only minor differences in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). We further discuss a number of other important criteria that should be taken into account when selecting a method, such as the availability of training data, the knowledge of the physical measurement model and the reconstruction speed. View Full-Text
Keywords: computed tomography (CT); image reconstruction; low-dose; sparse-angle; deep learning; quantitative comparison computed tomography (CT); image reconstruction; low-dose; sparse-angle; deep learning; quantitative comparison
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MDPI and ACS Style

Leuschner, J.; Schmidt, M.; Ganguly, P.S.; Andriiashen, V.; Coban, S.B.; Denker, A.; Bauer, D.; Hadjifaradji, A.; Batenburg, K.J.; Maass, P.; van Eijnatten, M. Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications. J. Imaging 2021, 7, 44. https://doi.org/10.3390/jimaging7030044

AMA Style

Leuschner J, Schmidt M, Ganguly PS, Andriiashen V, Coban SB, Denker A, Bauer D, Hadjifaradji A, Batenburg KJ, Maass P, van Eijnatten M. Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications. Journal of Imaging. 2021; 7(3):44. https://doi.org/10.3390/jimaging7030044

Chicago/Turabian Style

Leuschner, Johannes; Schmidt, Maximilian; Ganguly, Poulami S.; Andriiashen, Vladyslav; Coban, Sophia B.; Denker, Alexander; Bauer, Dominik; Hadjifaradji, Amir; Batenburg, Kees J.; Maass, Peter; van Eijnatten, Maureen. 2021. "Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications" J. Imaging 7, no. 3: 44. https://doi.org/10.3390/jimaging7030044

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