{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T12:51:07Z","timestamp":1787921467219,"version":"build-2784847793"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"1155","license":[{"start":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T00:00:00Z","timestamp":1705968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT","doi-asserted-by":"publisher","award":["NRF-2018R1A5A1060031"],"award-info":[{"award-number":["NRF-2018R1A5A1060031"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT","doi-asserted-by":"publisher","award":["2022R1A2C1091805"],"award-info":[{"award-number":["2022R1A2C1091805"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,2,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>To develop and validate a super-resolution (SR) algorithm generating clinically feasible chest radiographs from 64-fold reduced data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>An SR convolutional neural network was trained to produce original-resolution images (output) from 64-fold reduced images (input) using 128\u2009\u00d7\u2009128 patches (n\u2009=\u2009127\u2009030). For validation, 112 radiographs\u2014including those with pneumothorax (n\u2009=\u200917), nodules (n\u2009=\u200920), consolidations (n\u2009=\u200918), and ground-glass opacity (GGO; n\u2009=\u200916)\u2014were collected. Three image sets were prepared: the original images and those reconstructed using SR and conventional linear interpolation (LI) using 64-fold reduced data. The mean-squared error (MSE) was calculated to measure similarity between the reconstructed and original images, and image noise was quantified. Three thoracic radiologists evaluated the quality of each image and decided whether any abnormalities were present.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The SR-images were more similar to the original images than the LI-reconstructed images (MSE: 9269\u2009\u00b1\u20091015 vs. 9429\u2009\u00b1\u20091057; P = .02). The SR-images showed lower measured noise and scored better noise level by three radiologists than both original and LI-reconstructed images (Ps &amp;lt; .01). The radiologists\u2019 pooled sensitivity with the SR-reconstructed images was not significantly different compared with the original images for detecting pneumothorax (SR vs. original, 90.2% [46\/51] vs. 96.1% [49\/51]; P = .19), nodule (90.0% [54\/60] vs. 85.0% [51\/60]; P = .26), consolidation (100% [54\/54] vs. 96.3% [52\/54]; P = .50), and GGO (91.7% [44\/48] vs. 95.8% [46\/48]; P = .69).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>SR-reconstructed chest radiographs using 64-fold reduced data showed a lower noise level than the original images, with equivalent sensitivity for detecting major abnormalities.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Advances in knowledge<\/jats:title>\n                  <jats:p>This is the first study applying super-resolution in data reduction of chest radiographs.<\/jats:p>\n               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