{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T17:17:56Z","timestamp":1791393476060,"version":"4.3.3"},"reference-count":8,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:00:00Z","timestamp":1760054400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:00:00Z","timestamp":1760054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Jpn J Radiol"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To evaluate the image quality of pediatric portable chest radiographs processed using a deep learning\u2013based noise reduction (NR) algorithm implemented in clinical radiography systems, which is designed to reduce image noise without altering radiation dose, both alone and with edge enhancement.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>This retrospective visual grading analysis included 101 pediatric patients (median age: 33\u00a0days; median weight: 2844\u00a0g) who underwent portable chest radiography. Each image was processed using four techniques: (1) standard (no processing), (2) edge enhancement only, (3) NR only, and (4) NR with edge enhancement. Image quality was assessed using five criteria: visualization of proximal bronchi, small peripheral airways, vertebrae, image noise, and overall image quality. In an anonymous, randomized review, two pediatric radiologists rated each criterion using a 5-point Likert scale. Statistical comparisons were conducted between processing methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Images processed with NR and edge enhancement (NR\u2009+\u2009\/Filter\u2009+) achieved the highest mean scores across all criteria. Structural visibility\u2014particularly of small peripheral airways, proximal bronchi, and vertebrae\u2014showed significant improvement with edge enhancement (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.0001). No significant difference in image noise was observed between NR-only and NR\u2009+\u2009\/Filter\u2009+\u2009groups (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.482).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI-based noise reduction significantly improves image quality by reducing noise. Although edge enhancement does not further suppress noise, it improves the visibility of delicate anatomical structures. This combined approach may enhance diagnostic confidence in neonatal chest radiography, particularly under low-dose conditions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11604-025-01887-2","type":"journal-article","created":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T04:15:46Z","timestamp":1760069746000},"page":"376-382","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Evaluation of image quality in pediatric portable chest radiographs using AI-based noise reduction and edge enhancement"],"prefix":"10.1007","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3352-1334","authenticated-orcid":false,"given":"Atsuko","family":"Fujikawa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shin","family":"Matsuoka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuki","family":"Saito","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shoko","family":"Arizono","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kosei","family":"Nakamura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aya","family":"Kato","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takao","family":"Tanuma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hidefumi","family":"Mimura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,10]]},"reference":[{"key":"1887_CR1","doi-asserted-by":"publisher","first-page":"103615","DOI":"10.1016\/j.micron.2024.103615","volume":"180","author":"N Nazir","year":"2024","unstructured":"Nazir N, Sarwar A, Saini BS. Recent developments in denoising medical images using deep learning: an overview of models, techniques, and challenges. Micron. 2024;180:103615.","journal-title":"Micron"},{"key":"1887_CR2","doi-asserted-by":"publisher","first-page":"103013","DOI":"10.1016\/j.inffus.2025.103013","volume":"118","author":"B Jiang","year":"2025","unstructured":"Jiang B, Li J, Lu Y, Cai Q, Song H, Lu G. Efficient image denoising using deep learning: a brief survey. Inf Fusion. 2025;118:103013.","journal-title":"Inf Fusion"},{"issue":"16","key":"1887_CR3","doi-asserted-by":"publisher","first-page":"5391","DOI":"10.3390\/s21165391","volume":"21","author":"L Wang","year":"2021","unstructured":"Wang L, Liang Y, Zhao J, Fu Y, Li Y. An advanced noise reduction and edge enhancement algorithm. Sensors. 2021;21(16):5391.","journal-title":"Sensors"},{"issue":"2","key":"1887_CR4","doi-asserted-by":"publisher","first-page":"437","DOI":"10.2214\/ajr.177.2.1770437","volume":"177","author":"HW Goo","year":"2001","unstructured":"Goo HW, Kim EA, Yoon CH, et al. Using edge enhancement to identify subtle findings on soft-copy neonatal chest radiographs. AJR Am J Roentgenol. 2001;177(2):437\u201340.","journal-title":"AJR Am J Roentgenol"},{"issue":"7","key":"1887_CR5","doi-asserted-by":"publisher","first-page":"2951","DOI":"10.1007\/s00330-017-5301-2","volume":"28","author":"MH Smet","year":"2018","unstructured":"Smet MH, Breysem L, Mussen E, Bosmans H, Marshall NW, Cockmartin L. Visual grading analysis of digital neonatal chest phantom X-ray images: impact of detector type, dose, and image processing on image quality. Eur Radiol. 2018;28(7):2951\u20139.","journal-title":"Eur Radiol"},{"issue":"3","key":"1887_CR6","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1148\/radiol.2303021331","volume":"230","author":"MK Kalra","year":"2004","unstructured":"Kalra MK, Maher MM, Toth TL, et al. Strategies for CT radiation dose optimization. Radiology. 2004;230(3):619\u201328. https:\/\/doi.org\/10.1148\/radiol.2303021331.","journal-title":"Radiology"},{"issue":"1","key":"1887_CR7","first-page":"1","volume":"46","author":"ICRP","year":"2017","unstructured":"ICRP. Diagnostic reference levels in medical imaging. ICRP publication 135. Ann ICRP. 2017;46(1):1\u2013144.","journal-title":"Ann ICRP"},{"issue":"1","key":"1887_CR8","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.pedneo.2020.10.009","volume":"62","author":"AJ Gislason-Lee","year":"2021","unstructured":"Gislason-Lee AJ. Patient X-ray exposure and ALARA in the neonatal intensive care unit: global patterns. Pediatr Neonatol. 2021;62(1):3\u201310.","journal-title":"Pediatr Neonatol"}],"container-title":["Japanese Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11604-025-01887-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11604-025-01887-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11604-025-01887-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T06:35:40Z","timestamp":1769841340000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11604-025-01887-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,10]]},"references-count":8,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["1887"],"URL":"https:\/\/doi.org\/10.1007\/s11604-025-01887-2","relation":{},"ISSN":["1867-1071","1867-108X"],"issn-type":[{"value":"1867-1071","type":"print"},{"value":"1867-108X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,10]]},"assertion":[{"value":"2 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}