{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T19:54:04Z","timestamp":1790452444258,"version":"4.1.0"},"reference-count":27,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"4","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>The objective of the study is to demonstrate that with the use of artificial intelligence (AI) in computed tomography (CT), radiation doses of CT kidney-ureter-bladder (KUB) and CT urogram (CTU) can be reduced to less than that of X-ray KUB and CT KUB, respectively, while maintaining the good image quality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods:<\/jats:title>\n                    <jats:p>\n                      We reviewed all CT KUBs (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      = 121) performed in September 2019 and all CTUs (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      = 74) performed in December 2019 at our institution. The dose length product (DLP) of all CT KUBs and each individual phase of CTU were recorded. DLP of each scan done with new scanner (Canon Aquilion One Genesis with AiCE [CAOG]) which uses AI and deep learning reconstruction (DLR) were compared against traditional non-AI scanner (GE OPTIMA 660 [GEO-660]). We also compared DLPs of both scanners against the United Kingdom, National Diagnostic Reference Levels (NDRL) for CT.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>One hundred and twenty-one patient\u2019s CT KUBs and 74 patient\u2019s CTUs were reviewed. For CT KUB group, the mean DLP of 81\/121 scans done using AI\/DLR scanner (CAOG) was 77.8 mGy cm (1.16 mSv), while the mean DLP of 40\/121 CT KUB done with GEO-660 was 317.1 mGy cm (4.75 mSv). For CTU group, the mean DLP for 46\/74 scans done using AI\/DLR scanner (CAOG) was 401.9 mGy cm (6 mSv), compared to mean DLP of 1352.6 mGy cm (20.2 mSv) from GEO-660.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>We propose that CT scanners using AI\/DLR method have the potential of reducing radiation doses of CT KUB and CTU to such an extent that it heralds the extinction of plain film XR KUB for follow-up of urinary tract stones. To the best of our knowledge, this is the first study comparing CT KUB and CTU doses from new scanners utilizing AI\/DLR technology with traditional scanners using hybrid iterative reconstruction technology. Moreover, we have shown that this technology can markedly reduce the cumulative radiation burden in all urological patients undergoing CT examinations, whether this is CT KUB or CTU.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/ua.ua_73_23","type":"journal-article","created":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T04:08:37Z","timestamp":1697774917000},"page":"417-423","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":7,"title":["The use of artificial intelligence and deep learning reconstruction in urological computed tomography: Dose reduction at ghost level"],"prefix":"10.4103","volume":"15","author":[{"given":"Abdul","family":"Rauf","sequence":"first","affiliation":[{"name":"Departments of Urology, Addenbrookes Hospital, Cambridge University Hospitals, Cambridge, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saqib","family":"Javed","sequence":"additional","affiliation":[{"name":"Departments of Urology, Addenbrookes Hospital, Cambridge University Hospitals, Cambridge, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bhargavi","family":"Chandrasekar","sequence":"additional","affiliation":[{"name":"Departments of Urology, Addenbrookes Hospital, Cambridge University Hospitals, Cambridge, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saiful","family":"Miah","sequence":"additional","affiliation":[{"name":"Department of Urology, Addenbrookes Hospital, Cambridge University Hospitals, Cambridge, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Margaret","family":"Lyttle","sequence":"additional","affiliation":[{"name":"Departments of Urology, Addenbrookes Hospital, Cambridge University Hospitals, Cambridge, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mamoon","family":"Siraj","sequence":"additional","affiliation":[{"name":"Departments of Urology, Addenbrookes Hospital, Cambridge University Hospitals, Cambridge, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rono","family":"Mukherjee","sequence":"additional","affiliation":[{"name":"Salford Royal NHS Foundation Trust, Stott Lane Salford M6 8HD, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher M.","family":"McLeavy","sequence":"additional","affiliation":[{"name":"Department of Radiology, Liverpool University Hospitals, NHS Foundation Trust, Liverpool, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hazem","family":"Alaaraj","sequence":"additional","affiliation":[{"name":"Radiology, Mid Cheshire Hospitals, NHS Foundation Trust, Middlewich Road, Crewe CW1 4QJ, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Hawkins","sequence":"additional","affiliation":[{"name":"Radiology, Mid Cheshire Hospitals, NHS Foundation Trust, Middlewich Road, Crewe CW1 4QJ, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2023,10,20]]},"reference":[{"key":"R1-20240828","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1016\/j.crad.2008.04.012","article-title":"Is the KUB radiograph redundant for investigating acute ureteric colic in the non-contrast enhanced computed tomography era?","volume":"63","author":"Kennish","year":"2008","journal-title":"Clin Radiol"},{"key":"R2-20240828","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1111\/iju.13032","article-title":"Role of computed tomography urography in the clinical evaluation of upper tract urothelial carcinoma","volume":"23","author":"Jinzaki","year":"2016","journal-title":"Int J Urol"},{"key":"R3-20240828","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.4065\/mcp.2010.0260","article-title":"Radiation risk from medical imaging","volume":"85","author":"Lin","year":"2010","journal-title":"Mayo Clin Proc"},{"key":"R4-20240828","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1111\/j.1553-2712.2011.01103.x","article-title":"Radiological imaging of patients with suspected urinary tract stones: National trends, diagnoses, and predictors","volume":"18","author":"Westphalen","year":"2011","journal-title":"Acad Emerg Med"},{"key":"R5-20240828","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.2214\/AJR.04.1838","article-title":"Radiation dose associated with unenhanced CT for suspected renal colic: Impact of repetitive studies","volume":"186","author":"Katz","year":"2006","journal-title":"AJR Am J Roentgenol"},{"key":"R6-20240828","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1038\/nrurol.2016.154","article-title":"An overview of kidney stone imaging techniques","volume":"13","author":"Brisbane","year":"2016","journal-title":"Nat Rev Urol"},{"key":"R7-20240828","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1111\/bju.13605","article-title":"Accuracy of ultrasonography for renal stone detection and size determination: Is it good enough for management decisions?","volume":"119","author":"Ganesan","year":"2017","journal-title":"BJU Int"},{"key":"R8-20240828","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1148\/radiol.10100297","article-title":"Volume CT dose index and dose-length product displayed during CT: What good are they?","volume":"258","author":"Huda","year":"2011","journal-title":"Radiology"},{"key":"R10-20240828","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","article-title":"Artificial intelligence in radiology","volume":"18","author":"Hosny","year":"2018","journal-title":"Nat Rev Cancer"},{"key":"R11-20240828","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.crad.2021.01.010","article-title":"The future of CT: Deep learning reconstruction","volume":"76","author":"McLeavy","year":"2021","journal-title":"Clin Radiol"},{"key":"R12-20240828","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1055\/a-1248-2556","article-title":"Deep learning CT image reconstruction in clinical practice","volume":"193","author":"Arndt","year":"2021","journal-title":"Rofo"},{"key":"R13-20240828","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1016\/j.jacr.2010.06.003","article-title":"EMI and the first CT scanner","volume":"7","author":"Alexander","year":"2010","journal-title":"J Am Coll Radiol"},{"key":"R14-20240828","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1148\/radiol.10092212","article-title":"Abdominal CT: Comparison of adaptive statistical iterative and filtered back projection reconstruction techniques","volume":"257","author":"Singh","year":"2010","journal-title":"Radiology"},{"key":"R15-20240828","doi-asserted-by":"crossref","first-page":"041906","DOI":"10.1118\/1.4867863","article-title":"Statistical model based iterative reconstruction (MBIR) in clinical CT systems: Experimental assessment of noise performance","volume":"41","author":"Li","year":"2014","journal-title":"Med Phys"},{"key":"R16-20240828","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.2214\/AJR.12.8986","article-title":"Model-based iterative reconstruction versus adaptive statistical iterative reconstruction and filtered back projection in liver 64-MDCT: Focal lesion detection, lesion conspicuity, and image noise","volume":"200","author":"Shuman","year":"2013","journal-title":"AJR Am J Roentgenol"},{"key":"R17-20240828","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1007\/s13244-013-0273-5","article-title":"Comparison of image quality between filtered back-projection and the adaptive statistical and novel model-based iterative reconstruction techniques in abdominal CT for renal calculi","volume":"4","author":"Vardhanabhuti","year":"2013","journal-title":"Insights Imaging"},{"key":"R18-20240828","doi-asserted-by":"crossref","first-page":"4016","DOI":"10.1007\/s00330-018-5988-8","article-title":"Full model-based iterative reconstruction (MBIR) in abdominal CT increases objective image quality, but decreases subjective acceptance","volume":"29","author":"Laurent","year":"2019","journal-title":"Eur Radiol"},{"key":"R19-20240828","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.1016\/j.ejrad.2016.05.001","article-title":"Comparative evaluation of newly developed model-based and commercially available hybrid-type iterative reconstruction methods and filter back projection method in terms of accuracy of computer-aided volumetry (CADv) for low-dose CT protocols in phantom study","volume":"85","author":"Ohno","year":"2016","journal-title":"Eur J Radiol"},{"key":"R20-20240828","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1007\/s00330-011-2361-6","article-title":"Effect of hybrid iterative reconstruction technique on quantitative and qualitative image analysis at 256-slice prospective gating cardiac CT","volume":"22","author":"Utsunomiya","year":"2012","journal-title":"Eur Radiol"},{"key":"R21-20240828","doi-asserted-by":"crossref","first-page":"6163","DOI":"10.1007\/s00330-019-06170-3","article-title":"Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT","volume":"29","author":"Akagi","year":"2019","journal-title":"Eur Radiol"},{"key":"R24-20240828","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.2147\/JPR.S135901","article-title":"Renal colic and childbirth pain: Female experience versus male perception","volume":"10","author":"Miah","year":"2017","journal-title":"J Pain Res"},{"key":"R25-20240828","doi-asserted-by":"crossref","first-page":"1118","DOI":"10.1259\/bjr\/62994625","article-title":"Emergency department imaging protocol for suspected acute renal colic: Re-evaluating our service","volume":"85","author":"Patatas","year":"2012","journal-title":"Br J Radiol"},{"key":"R26-20240828","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.acra.2019.09.008","article-title":"Deep learning reconstruction at CT: Phantom study of the image characteristics","volume":"27","author":"Higaki","year":"2020","journal-title":"Acad Radiol"},{"key":"R27-20240828","doi-asserted-by":"crossref","first-page":"273","DOI":"10.3233\/THC-209028","article-title":"Comparison of quantitative measurements of four manufacturer's metal artifact reduction techniques for CT imaging with a self-made acrylic phantom","volume":"28","author":"Chou","year":"2020","journal-title":"Technol Health Care"},{"key":"R28-20240828","doi-asserted-by":"crossref","first-page":"80","DOI":"10.4103\/UA.UA_25_20","article-title":"Retrograde migration of a vesicoureteric junction calculus: A potential pitfall of the noncontrast limited pelvic computerized tomography","volume":"13","author":"Miah","year":"2021","journal-title":"Urol Ann"},{"key":"R29-20240828","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1089\/end.2019.0574","article-title":"Ultra-low-dose CT: An effective follow-up imaging modality for ureterolithiasis","volume":"34","author":"Cheng","year":"2020","journal-title":"J Endourol"},{"key":"R30-20240828","first-page":"414125","article-title":"Haematuria: An imaging guide","author":"Moloney","year":"2014","journal-title":"Adv Urol 2014"}],"container-title":["Urology Annals"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ovid.com\/10.4103\/ua.ua_73_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T19:14:08Z","timestamp":1787166848000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ovid.com\/10.4103\/ua.ua_73_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10]]},"references-count":27,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.4103\/ua.ua_73_23","relation":{},"ISSN":["0974-7796","0974-7834"],"issn-type":[{"value":"0974-7796","type":"print"},{"value":"0974-7834","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10]]},"assertion":[{"value":"2023-07-21","name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-08-18","name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}}]}}