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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).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The radiation dose in terms of CT dose index (CTDI<jats:sub>vol<\/jats:sub>) was reduced by up to 89%. The RMSE decreased from 0.16 \u00b1 0.05 to 0.09 \u00b1 0.02 and from 0.16 \u00b1 0.06 to 0.08 \u00b1 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 \u00b1 0.57 out of 5 for reference full-dose CT images, while ultra-low-dose CT images rated 2.78 \u00b1 0.9. The predicted CT images using the deep learning algorithm achieved a score of 4.42 \u00b1 0.8.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>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.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Key Points<\/jats:title>\n                <jats:p><jats:italic>\u2022 Ultra-low-dose CT imaging of COVID-19 patients would result in the loss of critical information about lesion types, which could potentially affect clinical diagnosis.<\/jats:italic><\/jats:p>\n                <jats:p><jats:italic>\u2022 Deep learning\u2013based prediction of full-dose from ultra-low-dose CT images for the diagnosis of COVID-19 could reduce the radiation dose by up to 89%.<\/jats:italic><\/jats:p>\n                <jats:p><jats:italic>\u2022 Deep learning algorithms failed to recover the correct lesion structure\/density for a number of patients considered outliers, and as such, further research and development is warranted to address these limitations.<\/jats:italic><\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00330-020-07225-6","type":"journal-article","created":{"date-parts":[[2020,9,3]],"date-time":"2020-09-03T01:02:33Z","timestamp":1599094953000},"page":"1420-1431","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":84,"title":["Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network"],"prefix":"10.1007","volume":"31","author":[{"given":"Isaac","family":"Shiri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Azadeh","family":"Akhavanallaf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amirhossein","family":"Sanaat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yazdan","family":"Salimi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dariush","family":"Askari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahra","family":"Mansouri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sajad P.","family":"Shayesteh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Hasanian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kiara","family":"Rezaei-Kalantari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Salahshour","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saleh","family":"Sandoughdaran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamid","family":"Abdollahi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hossein","family":"Arabi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7559-5297","authenticated-orcid":false,"given":"Habib","family":"Zaidi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,9,3]]},"reference":[{"issue":"13","key":"7225_CR1","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1001\/jama.2020.2648","volume":"323","author":"Z Wu","year":"2020","unstructured":"Wu Z, McGoogan JM (2020) Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention. 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