{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T00:29:15Z","timestamp":1747182555774,"version":"3.40.5"},"reference-count":29,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2019,5,15]],"date-time":"2019-05-15T00:00:00Z","timestamp":1557878400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Med Imag Rad Onc"],"published-print":{"date-parts":[[2019,10]]},"abstract":"Abstract<\/jats:title>Introduction<\/jats:title>Cystic fibrosis (CF<\/jats:styled-content>) predominantly affects young adults. Accurate radiological assessment of pulmonary disease is vital for predicting exacerbations, one of the leading causes of morbidity and mortality. We evaluated the image quality of model\u2010based iterative reconstruction (MBIR<\/jats:styled-content>) ultra\u2010low\u2010dose CT<\/jats:styled-content> chest (ULD<\/jats:styled-content>\u2010CT<\/jats:styled-content>) in CF<\/jats:styled-content> evaluation.<\/jats:p><\/jats:sec>Methods<\/jats:title>We compared ULD<\/jats:styled-content>\u2010CT<\/jats:styled-content> with standard adaptive statistical iterative reconstruction (ASIR<\/jats:styled-content>) low\u2010dose CT<\/jats:styled-content> (LD<\/jats:styled-content>\u2010CT<\/jats:styled-content>). Subjective assessment of contrast and noise were performed for each study. Background noise, signal to noise ratio (SNR<\/jats:styled-content>) and contrast to noise ratio (CNR<\/jats:styled-content>) were calculated and compared between the CT<\/jats:styled-content> studies. Conspicuity of major structures was assessed. These aspects of image quality were compared to determine whether ULD<\/jats:styled-content>\u2010CT<\/jats:styled-content> was superior to LD<\/jats:styled-content>\u2010CT<\/jats:styled-content> in assessment of CF<\/jats:styled-content>.<\/jats:p><\/jats:sec>Results<\/jats:title>The ULD<\/jats:styled-content>\u2010CT<\/jats:styled-content> achieved median effective dose of 0.073 mS<\/jats:styled-content>v, comparable to one standard chest radiograph and significantly lower than the median LD<\/jats:styled-content>\u2010CT<\/jats:styled-content> dose of 1.22 mS<\/jats:styled-content>v. ULD<\/jats:styled-content>\u2010CT<\/jats:styled-content> had lower subjective contrast and higher subjective noise when compared to LD<\/jats:styled-content>\u2010CT<\/jats:styled-content>. Objectively measured background noise was lower in ULD<\/jats:styled-content>\u2010CT<\/jats:styled-content> (16.33 HU<\/jats:styled-content> vs 38.53 HU<\/jats:styled-content>, P\u00a0<\/jats:italic><\u00a0<\/jats:italic>0.0001) compared to LD<\/jats:styled-content>\u2010CT<\/jats:styled-content>. ULD<\/jats:styled-content>\u2010CT<\/jats:styled-content> had higher median CNR<\/jats:styled-content> (52.65 vs 22.09, P\u00a0<\/jats:italic><\u00a0<\/jats:italic>0.0001) and SNR<\/jats:styled-content> in lung (9.08 vs 7.29, P\u00a0<\/jats:italic>=\u00a0<\/jats:italic>0.002) compared to LD<\/jats:styled-content>\u2010CT<\/jats:styled-content>. ULD<\/jats:styled-content>\u2010CT<\/jats:styled-content> was equal to LD<\/jats:styled-content>\u2010CT<\/jats:styled-content> in identification of trachea, bronchi, pleural and pericardium. Interobserver reliability showed agreement of 80\u201392%.<\/jats:p><\/jats:sec>Conclusions<\/jats:title>The image quality of ULD<\/jats:styled-content>\u2010CT<\/jats:styled-content> is similar to LD<\/jats:styled-content>\u2010CT<\/jats:styled-content>, at 1\/16th the dose. MBIR<\/jats:styled-content> constructed ULD<\/jats:styled-content>\u2010CT<\/jats:styled-content> is an effective imaging modality for CF<\/jats:styled-content> surveillance, with potential applications in other disease settings.<\/jats:p><\/jats:sec>","DOI":"10.1111\/1754-9485.12895","type":"journal-article","created":{"date-parts":[[2019,5,15]],"date-time":"2019-05-15T10:35:34Z","timestamp":1557916534000},"page":"602-609","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Image quality comparison between model\u2010based iterative reconstruction and adaptive statistical iterative reconstruction chest computed tomography in cystic fibrosis patients"],"prefix":"10.1111","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8959-1828","authenticated-orcid":false,"given":"Sandra","family":"Lin","sequence":"first","affiliation":[{"name":"Austin Health Melbourne Victoria Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Monica","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging Monash Health Melbourne Victoria Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kenneth K","family":"Lau","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging Monash Health Melbourne Victoria Australia"},{"name":"Faculty of Medicine, Nursing and Health Sciences Monash University Melbourne Victoria Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2019,5,15]]},"reference":[{"key":"e_1_2_5_2_1","doi-asserted-by":"publisher","DOI":"10.5694\/mja11.10719"},{"key":"e_1_2_5_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/jrr\/rru019"},{"key":"e_1_2_5_4_1","doi-asserted-by":"publisher","DOI":"10.4329\/wjr.v8.i4.331"},{"key":"e_1_2_5_5_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.09.2953"},{"key":"e_1_2_5_6_1","doi-asserted-by":"publisher","DOI":"10.1097\/RLI.ob013e3181dzfeec"},{"key":"e_1_2_5_7_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.11101450"},{"key":"e_1_2_5_8_1","article-title":"The model\u2010based paradigm: a new frontier in image reconstruction","author":"Thibault J\u2010B","year":"2011","journal-title":"CT Clarity (GE Healthcare)"},{"key":"e_1_2_5_9_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.12.8986"},{"key":"e_1_2_5_10_1","first-page":"90335J","article-title":"Noise performance of statistical model based iterative reconstruction in clinical CT systems","author":"Li K","year":"2014","journal-title":"Phys Med Imag"},{"key":"e_1_2_5_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-012-2627-7"},{"key":"e_1_2_5_12_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4881148"},{"key":"e_1_2_5_13_1","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000004456"},{"key":"e_1_2_5_14_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.15132091"},{"key":"e_1_2_5_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-011-2227-y"},{"volume-title":"Practical Statistics for Medical Research","year":"2006","author":"Altman DG","key":"e_1_2_5_16_1"},{"key":"e_1_2_5_17_1","doi-asserted-by":"publisher","DOI":"10.1097\/RLI.0b013e3182562a89"},{"key":"e_1_2_5_18_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0138329"},{"volume-title":"The Essential Physics of Medical Imaging","year":"2012","author":"Bushberg JT","key":"e_1_2_5_19_1"},{"key":"e_1_2_5_20_1","doi-asserted-by":"crossref","unstructured":"DodgeCT TammEP CodyDDet\u00a0al.Performance evaluation of iterative reconstruction algorithms for achieving CT radiation dose reduction \u2014 a phantom study.2016. 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