Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network
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COMPUTED TOMOGRAPHY
Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network Isaac Shiri 1 & Azadeh Akhavanallaf 1 & Amirhossein Sanaat 1 & Yazdan Salimi 1 & Dariush Askari 2 & Zahra Mansouri 3 & Sajad P. Shayesteh 4 & Mohammad Hasanian 5 & Kiara Rezaei-Kalantari 6 & Ali Salahshour 7 & Saleh Sandoughdaran 8 & Hamid Abdollahi 9 & Hossein Arabi 1 & Habib Zaidi 1,10,11,12 Received: 9 June 2020 / Revised: 13 August 2020 / Accepted: 21 August 2020 # The Author(s) 2020
Abstract Objectives The current study aimed to design an ultra-low-dose CT examination protocol using a deep learning approach suitable for clinical diagnosis of COVID-19 patients. Methods In this study, 800, 170, and 171 pairs of ultra-low-dose and full-dose CT images were used as input/output as training, test, and external validation set, respectively, to implement the full-dose prediction technique. A residual convolutional neural network was applied to generate full-dose from ultra-low-dose CT images. The quality of predicted CT images was assessed using root mean square error (RMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Scores ranging from 1 to 5 were assigned reflecting subjective assessment of image quality and related COVID-19 features, including ground glass opacities (GGO), crazy paving (CP), consolidation (CS), nodular infiltrates (NI), bronchovascular thickening (BVT), and pleural effusion (PE). Results The radiation dose in terms of CT dose index (CTDIvol) was reduced by up to 89%. The RMSE decreased from 0.16 ± 0.05 to 0.09 ± 0.02 and from 0.16 ± 0.06 to 0.08 ± 0.02 for the predicted compared with ultra-low-dose CT images in the test and external validation set, respectively. The overall scoring assigned by radiologists showed an acceptance rate of 4.72 ± 0.57 out of 5 for reference full-dose CT images, while ultra-low-dose CT images rated 2.78 ± 0.9. The predicted CT images using the deep learning algorithm achieved a score of 4.42 ± 0.8. Conclusions The results demonstrated that the deep learning algorithm is capable of predicting standard full-dose CT images with acceptable quality for the clinical diagnosis of COVID-19 positive patients with substantial radiation dose reduction. Isaac Shiri and Azadeh Akhavanallaf contributed equally to this work. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00330-020-07225-6) contains supplementary material, which is available to authorized users. * Habib Zaidi [email protected] 1
Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland
2
Department of Radiology Technology, Shahid Beheshti University of Medical, Tehran, Iran
3
Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
4
Department of Physiology, Pharmacology and Medical Physics, Alborz University of Medical Sciences, Karaj, Iran
5
Department of Radiology, Arak University of Medical Sciences, Ara
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