{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T23:42:45Z","timestamp":1783122165433,"version":"3.54.6"},"reference-count":47,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"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":["Medical Physics"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Large CT image databases are critical to the development and validation of protocol harmonization strategies and AI models. However, image quality (IQ) can vary significantly due to differences in technology and site practices, even for scanners from the same manufacturer, which can have considerable effects on harmonization efforts and AI algorithm development. Quantifying the 3D IQ variability represented in these databases is essential to characterize protocol heterogeneity, the training data distribution, and to assess test data overlap.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To quantify and characterize sources of 3D IQ variability in chest CT across scanner models and imaging sites.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A previously\u2010reported phantom and its automated analysis software was employed across 6 sites, 3 CT manufacturers, and 27 scanners (10 unique models) to rigorously quantify IQ metrics, including image noise, image contrast (low\u2010contrast objects: \u221226 HU, 41 HU; medium: \u221284 HU; high: 276 HU, 817 HU), the 3D MTF (axial plane and oblique direction), and the 3D NPS. The MTF was summarized using the frequency at 10% modulation \u201c\n                      <jats:italic>f<\/jats:italic>\n                      <jats:sub>10<\/jats:sub>\n                      \u201d, and NPS using average frequency \u201c\n                      <jats:italic>f<\/jats:italic>\n                      <jats:sub>avg<\/jats:sub>\n                      \u201d. Sites were instructed to use their non\u2010contrast chest CT protocols for fixed mAs phantom scanning at CTDI\n                      <jats:sub>vol,32<\/jats:sub>\n                      levels of 2.1 (low dose \u201cLD\u201d), 4.2 (MD), and 6.3 mGy (HD), and provide four reconstructions, including standard \u2018Std\u2019 and thin \u2018Thn\u2019 slice thicknesses for sharp \u201cSh\u201d and soft \u201cSo\u201d kernels. Variance component analysis (VCA) was performed on the resulting phantom series to quantify sources of variability in IQ partitioned into inter\u2010scanner model, inter\u2010site, and residual (i.e., unexplained) components.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      IQ metrics dominated by inter\u2010scanner differences (&gt;\u00a050% of total variance) included image noise for Sh\u2010Std\u2010LD\/MD, Sh\u2010Thn\u2010LD, and So\u2010Std\/Thn for all doses (51%\u201384%), high\u2010contrast (276, 817 HU) image contrast for all protocols (68%\u201393%), medium\u2010contrast image contrast for all MD\/HD protocols (64%\u201388%), low\u2010contrast (\u221226 HU) image contrast for So\u2010Std\u2010MD (65%) and Sh\u2010Std\u2010LD\/MD (64%\/51%), axial and oblique  for nearly all protocols (58%\u201388% and 56%\u201358%, respectively), axial\n                      <jats:italic>f<\/jats:italic>\n                      <jats:sub>avg<\/jats:sub>\n                      for Sh\u2010Thn across all dose levels (51%\u201365%), and oblique\n                      <jats:italic>f<\/jats:italic>\n                      <jats:sub>avg<\/jats:sub>\n                      for So\u2010Thn\u2010HD (58%) and So\u2010Thn across all dose levels (56%\u201391%). Metrics dominated by inter\u2010site differences were image noise for Sh\u2010Std\/Thn\u2010HD (57%\/58%), low\u2010contrast (41 HU) for So\u2010Std\u2010MD\/HD (77%\/54%) and Sh\u2010Std\u2010MD (64%), and axial\n                      <jats:italic>f<\/jats:italic>\n                      <jats:sub>avg<\/jats:sub>\n                      for So\u2010Thn\u2010LD\/MD (54%, 70%). Residual variance was the dominant contributor to variability in medium\u2010contrast image contrast for So\/Sh\u2010Std\u2010LD (91%\/56%), low\u2010contrast (41 HU, \u221226 HU) image contrast for So\u2010Std\u2010LD (58%\u201370%) and Sh\u2010Std\u2010LD\/HD (76%\/66%), and axial  for So\u2010Thn\u2010HD (54%).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This multisite phantom study demonstrates that substantial, systematic variability in 3D IQ exists across scanners, sites, metrics, dose levels, and reconstruction settings despite use of a routine non\u2010contrast chest CT protocol. Variability was primarily driven by scanner model differences, with additional contributions from inter\u2010site differences and residual variance for select metrics and protocols. Regardless of controlled imaging conditions that limit variability from patient anatomy, size\u2010dependent dose effects, and tube current modulation, substantial variability in 3D IQ persists. The size\u2010specific, relative trends observed in this study emphasize the importance of accounting for scanner model, site, and protocol\u2010specific heterogeneity when using large CT image databases.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70402","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T02:35:25Z","timestamp":1775097325000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Assessing variations in 3D image quality in chest CT across sites and scanners"],"prefix":"10.1002","volume":"53","author":[{"given":"Andrew M.","family":"Hernandez","sequence":"first","affiliation":[{"name":"Department of Radiology University of California Davis Health  Sacramento California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Uneri","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Johns Hopkins University  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas","family":"Bevins","sequence":"additional","affiliation":[{"name":"Department of Radiology MaineHealth Maine Medical Center Portland Maine USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kalpana","family":"Kanal","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Washington  Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Zamora","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Washington  Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Supanich","sequence":"additional","affiliation":[{"name":"Department of Radiology and Nuclear Medicine Rush University Medical Center  Chicago Illinois USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin","family":"Maloney","sequence":"additional","affiliation":[{"name":"Department of Radiology Henry Ford Health  Detroit Michigan USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahadevappa","family":"Mahesh","sequence":"additional","affiliation":[{"name":"The Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael F.","family":"McNitt\u2010Gray","sequence":"additional","affiliation":[{"name":"Department of Radiological Sciences David Geffen School of Medicine at University of California Los Angeles  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Kinahan","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Washington  Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey H.","family":"Siewerdsen","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Johns Hopkins University  Baltimore Maryland USA"},{"name":"Department of Imaging Physics The University of Texas MD Anderson Cancer Center  Houston Texas USA"},{"name":"Institute for Data Science in Oncology The University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John M.","family":"Boone","sequence":"additional","affiliation":[{"name":"Department of Radiology University of California Davis Health  Sacramento California USA"},{"name":"Department of Biomedical Engineering University of California Davis  Davis California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpbup.2024.100146"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12652\u2010021\u201003612\u2010z"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330\u2010020\u201007668\u2010x"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2020.10.001"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330\u2010024\u201011019\u20105"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.243525"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278\u2010013\u20109622\u20107"},{"issue":"3","key":"e_1_2_8_9_1","article-title":"DeepLesion: automated mining of large\u2010scale lesion annotations and universal lesion detection with deep learning","volume":"5","author":"Yan K","year":"2018","journal-title":"J Med Imaging (Bellingham)"},{"key":"e_1_2_8_10_1","unstructured":"TsaiE SimpsonS LungreMP et\u00a0al.Data from medical imaging data resource center (MIDRC)\u2010RSNA international covid radiology database (RICORD) release 1C\u2010Chest X\u2010ray covid+(MIDRC\u2010RICORD\u20101C).The Cancer Imaging Archive 10.2021.4 2021."},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020192536"},{"issue":"6","key":"e_1_2_8_12_1","article-title":"Longitudinal assessment of demographic representativeness in the medical imaging and data resource center open data commons","volume":"10","author":"Whitney HM","year":"2023","journal-title":"J Med Imaging (Bellingham)"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.70325"},{"key":"e_1_2_8_14_1","doi-asserted-by":"crossref","unstructured":"DalyMA HoffmanJM HernandezAM et\u00a0al.Harmonizing quantitative imaging feature values in CT using image quality metrics as a basis. Paper presented at: Medical Imaging 2024: Computer\u2010Aided Diagnosis2024. doi:10.1117\/12.3006885","DOI":"10.1117\/12.3006885"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.14.13613"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14635"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4961984"},{"issue":"5","key":"e_1_2_8_18_1","article-title":"Variability in image quality and radiation dose within and across 97 medical facilities","volume":"8","author":"Smith TB","year":"2021","journal-title":"J Med Imaging (Bellingham)"},{"issue":"2","key":"e_1_2_8_19_1","first-page":"163","article-title":"An improved method of automated noise measurement system in CT images","volume":"11","author":"Anam C","year":"2021","journal-title":"J Biomed Phys Eng"},{"key":"e_1_2_8_20_1","unstructured":"PillaiPS SiewerdsenJH RypinskiT et\u00a0al.Patient\u2010specific NEQ (f): Automatic local and global determination of spatial\u2010frequency\u2010dependent SNR and task performance in clinical CT. Paper presented at: Medical Imaging 2024: Physics of Medical Imaging2024."},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0245374"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/60\/21\/8381"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.5114\/pjr.2019.82743"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1097\/RLI.0000000000000540"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330\u2010018\u20105518\u20108"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4752209"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2015.09.017"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13763"},{"key":"e_1_2_8_29_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14789"},{"key":"e_1_2_8_30_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3378673"},{"key":"e_1_2_8_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2012.07.001"},{"key":"e_1_2_8_32_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.1513158"},{"key":"e_1_2_8_33_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13952"},{"key":"e_1_2_8_34_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14820"},{"key":"e_1_2_8_35_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16983"},{"key":"e_1_2_8_36_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13131"},{"key":"e_1_2_8_37_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2017161911"},{"key":"e_1_2_8_38_1","unstructured":"Corgi phantom product guide. The phantom laboratory. Accessed 12\/1\/2024.https:\/\/www.phantomlab.com\/s\/CGI014CorgiProductGuide20250114\u2010ybdz.pdfPublished 2025"},{"issue":"3","key":"e_1_2_8_39_1","article-title":"Perceptual thresholds for differences in CT noise texture","volume":"11","author":"Oostveen LJ","year":"2024","journal-title":"J Med Imaging (Bellingham)"},{"key":"e_1_2_8_40_1","doi-asserted-by":"publisher","DOI":"10.1177\/0049124104268644"},{"key":"e_1_2_8_41_1","doi-asserted-by":"publisher","DOI":"10.21037\/tlcr.2018.05.02"},{"key":"e_1_2_8_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2025.105034"},{"key":"e_1_2_8_43_1","doi-asserted-by":"publisher","DOI":"10.12688\/f1000research.147345.1"},{"key":"e_1_2_8_44_1","doi-asserted-by":"publisher","DOI":"10.1097\/RCT.0000000000000491"},{"key":"e_1_2_8_45_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010025\u201088551\u2010x"},{"key":"e_1_2_8_46_1","doi-asserted-by":"publisher","DOI":"10.1097\/01.rct.0000163706.60009.54"},{"key":"e_1_2_8_47_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.09.2380"},{"key":"e_1_2_8_48_1","doi-asserted-by":"publisher","DOI":"10.4103\/jmp.JMP_36_21"}],"container-title":["Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.70402","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/mp.70402","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.70402","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T21:25:19Z","timestamp":1783113919000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/mp.70402"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":47,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["10.1002\/mp.70402"],"URL":"https:\/\/doi.org\/10.1002\/mp.70402","archive":["Portico"],"relation":{},"ISSN":["0094-2405","2473-4209"],"issn-type":[{"value":"0094-2405","type":"print"},{"value":"2473-4209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"2025-07-04","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-02-27","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-04-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70402"}}