{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T19:54:03Z","timestamp":1790452443519,"version":"4.1.0"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"1140","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BJR"],"published-print":{"date-parts":[[2022,12,1]]},"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p> To compare image quality and diagnostic accuracy of arterial stenosis in low-dose lower-extremity CT angiography (CTA) between adaptive statistical iterative reconstruction-V (ASIR-V) and deep learning image reconstruction (DLIR) algorithms. <\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p> 46 patients undergoing low-dose lower-extremity CTA were enrolled. Images were reconstructed using ASIR-V (blending factor of 50% (AV-50) and 100% (AV-100)) and DLIR (medium (DL-M), and high (DL-H)). CT values and standard deviation of the aorta, psoas, popliteal artery, popliteal and ankle muscles were measured. The edge-rise distance and edge-rise slope were calculated. The degrees of granularity and edge blurring were assessed using a 5-point scale. The stenosis degrees were measured on the four reconstructions, and their mean square errors against that of digital subtraction angiography were calculated and compared. <\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p> For both ASIR-V and DLIR, higher reconstruction intensity generated lower noise and higher signal-to-noise ratio and contrast-to-noise ratio values. The standard deviation values in AV-100 images were significantly lower than other reconstructions. The two DLIR image groups had higher edge-rise slope and lower edge-rise distance (DL-M:1.79 \u00b1 0.37\u2009mm and DL-H:1.82 \u00b1 0.38\u2009mm vs AV-50:1.96 \u00b1 0.39\u2009mm and AV-100:2.01 \u00b1 0.36\u2009mm, p = 0.014) than ASIR-V images. The overall image quality of DLIR was rated higher than ASIR-V (DL-M:0.83 \u00b1 0.61, DL-H:0.41 \u00b1 0.62, AV-50:1.85 \u00b1 0.60\u2009and AV-100:2.37 \u00b1 0.77, p &lt; 0.001), with DL-H having the highest overall image quality score. For stenosis measurement, DL-H had the lowest mean-square-errors compared to digital subtraction angiography among all reconstruction groups. <\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p> DLIR images had higher image quality ratings with lower image noise and sharper vessel walls in low-dose lower-extremity CTA, and DL-H provides the best overall image quality and highest accuracy in diagnosing artery stenoses. <\/jats:p><\/jats:sec><jats:sec><jats:title>Advances in knowledge:<\/jats:title><jats:p> DLIR provides high-quality images with sharper edges compared to ASIR-V during low-dose CTA of lower extremity arteries, and DLIR (high) provides the best overall image quality and highest accuracy in diagnosing artery stenoses among all reconstruction algorithms (ASIR-V and DLIR). ASIR-V with blending factor of 100% has the strongest noise reduction ability among all reconstruction algorithms (ASIR-V and DLIR); however, it generates the most blurred images. <\/jats:p><\/jats:sec>","DOI":"10.1259\/bjr.20220196","type":"journal-article","created":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T10:05:37Z","timestamp":1667815537000},"source":"Crossref","is-referenced-by-count":25,"title":["Iterative reconstruction <i>vs<\/i> deep learning image reconstruction: comparison of image quality and diagnostic accuracy of arterial stenosis in low-dose lower extremity CT angiography"],"prefix":"10.1093","volume":"95","author":[{"given":"Tingting","family":"Qu","sequence":"first","affiliation":[{"name":"Department of Radiology, the First Affiliated Hospital of Xi\u2019an Jiaotong University, Xi\u2019an, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinxia","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Radiology, the First Affiliated Hospital of Xi\u2019an Jiaotong University, Xi\u2019an, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianying","family":"Li","sequence":"additional","affiliation":[{"name":"CT Research Center, GE Healthcare China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Le","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Radiology, the First Affiliated Hospital of Xi\u2019an Jiaotong University, Xi\u2019an, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology, the First Affiliated Hospital of Xi\u2019an Jiaotong University, Xi\u2019an, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lihong","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiology, the First Affiliated Hospital of Xi\u2019an Jiaotong University, Xi\u2019an, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingtao","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Radiology, the First Affiliated Hospital of Xi\u2019an Jiaotong University, Xi\u2019an, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueni","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Radiology, the First Affiliated Hospital of Xi\u2019an Jiaotong University, Xi\u2019an, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianxin","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Radiology, the First Affiliated Hospital of Xi\u2019an Jiaotong University, Xi\u2019an, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","reference":[{"key":"b1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMcp1507631"},{"key":"b2","doi-asserted-by":"publisher","DOI":"10.1097\/RCT.0000000000000666"},{"key":"b3","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2015132766"},{"key":"b4","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20170655"},{"key":"b5","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.19.22332"},{"key":"b6","doi-asserted-by":"publisher","DOI":"10.1148\/rg.2021200196"},{"key":"b7","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-07358-8"},{"key":"b8","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2019.09.008"},{"key":"b9","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.12.8689"},{"key":"b10","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2016.12.020"},{"key":"b11","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.11110700"},{"key":"b12","doi-asserted-by":"publisher","DOI":"10.1016\/j.crad.2018.08.001"},{"issue":"7","key":"b13","doi-asserted-by":"crossref","DOI":"10.3390\/diagnostics11071209","volume":"11","author":"Keller G","year":"2021","journal-title":"Diagnostics (Basel)"},{"key":"b14","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-019-06359-6"},{"key":"b15","doi-asserted-by":"publisher","DOI":"10.1118\/1.4903899"},{"key":"b16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2015.08.007"},{"key":"b17","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14319"},{"key":"b18","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-021-07712-4"},{"key":"b19","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-019-06183-y"},{"key":"b20","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2019.11.010"},{"key":"b21","doi-asserted-by":"publisher","DOI":"10.21037\/qims-20-626"},{"key":"b22","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020202317"},{"key":"b23","doi-asserted-by":"publisher","DOI":"10.3348\/kjr.2020.0116"},{"key":"b24","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-07349-9"}],"container-title":["The British Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.birpublications.org\/doi\/pdf\/10.1259\/bjr.20220196","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T16:44:11Z","timestamp":1705077851000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bjr\/article\/7477433"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,1]]},"references-count":24,"journal-issue":{"issue":"1140","published-print":{"date-parts":[[2022,12,1]]}},"alternative-id":["10.1259\/bjr.20220196"],"URL":"https:\/\/doi.org\/10.1259\/bjr.20220196","relation":{},"ISSN":["0007-1285","1748-880X"],"issn-type":[{"value":"0007-1285","type":"print"},{"value":"1748-880X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,1]]},"article-number":"20220196"}}