{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T02:11:47Z","timestamp":1780539107219,"version":"3.54.1"},"reference-count":45,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T00:00:00Z","timestamp":1771977600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T00:00:00Z","timestamp":1771977600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Recently, deep learning (DL)\u2010based noise reduction (DLNR) has been introduced in clinically used digital radiography (DR) systems, reporting superior performance over conventional algorithms. However, DLNR algorithms often operate as \u201cblack boxes\u201d with nonlinear behavior, making it essential to understand the impact of such processing on image quality under different imaging conditions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aimed to quantitatively evaluate the image quality of a commercial DLNR algorithm for DR referred to as intelligent noise reduction (INR). Specifically, we compared its noise reduction performance with that of a conventional rule\u2010based algorithm (conventional noise reduction, Con\u2010NR) using frequency\u2010domain metrics with detailed noise power spectrum (NPS) analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The NPS was used to assess the spatial\u2010frequency\u2010dependent behavior of both INR and Con\u2010NR across varying dose levels and different objects. In this work, we introduced a supplementary metric\u2014the NPS improvement factor (NPSIF)\u2014to quantify noise suppression across frequency ranges and facilitate direct comparison between methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The DL\u2010based algorithm achieved substantial noise reduction at low\u2010dose settings compared with the conventional method, although its advantages were less pronounced at higher dose levels. The NPSIF effectively captured frequency\u2010specific differences, thereby offering insights into the strengths and limitations of each technique.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The dose\u2010dependent performance of the DL\u2010based algorithm suggests sensitivity to the characteristics of the training data used to develop the DL model. The findings demonstrate distinct differences in the noise suppression behavior between DL\u2010based and conventional methods in DR and underscore the importance of detailed frequency\u2010domain evaluation for understanding advanced image processing. Further research is warranted to integrate noise analysis with diagnostic performance metrics to comprehensively assess clinical utility.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70521","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T22:14:06Z","timestamp":1773008046000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Quantitative evaluation of a deep learning\u2010based noise reduction algorithm in digital radiography using noise power spectrum analysis"],"prefix":"10.1002","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0140-5993","authenticated-orcid":false,"given":"Sho","family":"Maruyama","sequence":"first","affiliation":[{"name":"Department of Radiological Technology Gunma Prefectural College of Health Sciences  Maebashi Gunma Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroki","family":"Saitou","sequence":"additional","affiliation":[{"name":"Department of Medical Radiology Faculty of Medical Technology Teikyo University  Itabashi\u2010ku Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,2,25]]},"reference":[{"issue":"1","key":"e_1_2_11_2_1","first-page":"1","article-title":"Managing patient dose in digital radiology","volume":"34","author":"ICRP Publication 93","year":"2004","journal-title":"Ann ICRP"},{"issue":"1","key":"e_1_2_11_3_1","first-page":"1","article-title":"Diagnostic reference levels in medical imaging","volume":"46","author":"ICRP Publication 135","year":"2017","journal-title":"Ann ICRP"},{"key":"e_1_2_11_4_1","unstructured":"NCRP Report No. 184.Medical Radiation Exposure of Patients in the United States 2019."},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1093\/rpd\/nch562"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1177\/01466453231210646"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radi.2020.08.012"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2492071734"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1093\/rpd\/ncab003"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12194-024-00859-x"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14244"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2019.107426"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-021-07942-6"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0228609"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020202317"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2019.11.010"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.crad.2022.08.127"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.14270"},{"key":"e_1_2_11_19_1","volume-title":"Intelligent Noise Reduction: Seeing Through the Noise With Deep Learning Image Processing","author":"Johnson J","year":"2023"},{"key":"e_1_2_11_20_1","volume-title":"An Analysis of Canon Intelligent Noise Reduction Processing Applied to Pediatric Digital Radiographs","author":"Ice J","year":"2023"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00247-025-06251-0"},{"key":"e_1_2_11_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radi.2025.102958"},{"key":"e_1_2_11_23_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics15111391"},{"key":"e_1_2_11_24_1","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging6060052"},{"key":"e_1_2_11_25_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/52\/16\/010"},{"key":"e_1_2_11_26_1","doi-asserted-by":"publisher","DOI":"10.1088\/2057-1976\/abfc2f"},{"key":"e_1_2_11_27_1","unstructured":"International Electrotechnical Commission (IEC).Medical electrical equipment\u2014Characteristics of digital X\u2010ray imaging devices\u2014Part 1\u20101: Determination of the detective quantum efficiency \u2010 Detectors used in radiographic imaging. 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