{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T03:17:46Z","timestamp":1790997466737,"version":"4.1.0"},"reference-count":41,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T00:00:00Z","timestamp":1656028800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Recently, computed tomography (CT) manufacturers have developed deep\u2010learning\u2010based reconstruction algorithms to compensate for the limitations of iterative reconstruction (IR) algorithms, such as image smoothing and the spatial resolution's dependence on contrast and dose levels.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To assess the impact of an artificial intelligence deep\u2010learning reconstruction (AI\u2010DLR) algorithm on image quality and dose reduction compared with a hybrid IR algorithm in chest CT for different clinical indications.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Acquisitions on the CT American College of Radiology (ACR) 464 and CT Torso CTU\u201041 phantoms were performed at five dose levels (CTDI<jats:sub>vol<\/jats:sub>: 9.5\/7.5\/6\/2.5\/0.4\u00a0mGy) used for chest CT conditions. Raw data were reconstructed using filtered backprojection, two levels of IR (iDose<jats:sup>4<\/jats:sup> levels 4 (i4) and 7 (i7)), and five levels of AI\u2010DLR (Precise Image; Smoother, Smooth, Standard, Sharp, Sharper). Noise power spectrum (NPS), task\u2010based transfer function, and detectability index (<jats:italic>d<\/jats:italic>\u2032) were computed: <jats:italic>d<\/jats:italic>\u2032\u2010modeled detection of a soft tissue mediastinal nodule (low\u2010contrast soft tissue chest nodule within the mediastinum [LCN]), ground\u2010glass opacity (GGO), or high\u2010contrast pulmonary (HCP) lesion. The subjective image quality of chest anthropomorphic phantom images was independently evaluated by two radiologists. They assessed image noise, image smoothing, contrast between vessels and fat in the mediastinum for mediastinal images, visual border detection between bronchus and lung parenchyma for parenchymal images, and overall image quality using a commonly used four\u2010 or five\u2010point scale.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>From Standard to Smoother levels, on average, the noise magnitude decreased (for all dose levels: \u221266.3% \u00b1 0.5% for mediastinal images and \u221263.1% \u00b1 0.1% for parenchymal images), the average NPS spatial frequency decreased (for all dose levels: \u221235.3% \u00b1 2.2% for mediastinal images and \u221213.3% \u00b1 2.2% for parenchymal images), and the detectability (<jats:italic>d<\/jats:italic>\u2032) of the three lesions increased. The opposite pattern was found from Standard to Sharper levels. From Smoother to Sharper levels, the spatial resolution increased for the low\u2010contrast polyethylene insert and the opposite for the high\u2010contrast air insert. Compared to the i4 used in clinical practice, <jats:italic>d<\/jats:italic>\u2032 values were higher using Smoother (mean for all dose levels: 338.7% \u00b1 29.4%), Smooth (103.4% \u00b1 11.2%), and Standard (34.1% \u00b1 6.6%) levels for the LCN on mediastinal images and Smoother (169.5% \u00b1 53.2% for GGO and 136.9% \u00b1 1.6% for HCP) and Smooth (36.4% \u00b1 22.1% and 24.1% \u00b1 0.9%, respectively) levels for parenchymal images. Radiologists considered the images satisfactory for clinical use at these levels, but adaptation to the dose level of the protocol is required.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>With AI\u2010DLR, the smoothest levels reduced the noise and improved the detectability of chest lesions but increased the image smoothing. The opposite was found with the sharpest levels. The choice of level depends on the dose level and type of image: mediastinal or parenchymal.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.15807","type":"journal-article","created":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T15:48:47Z","timestamp":1655135327000},"page":"5052-5063","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Impact of an artificial intelligence deep\u2010learning reconstruction algorithm for CT on image quality and potential dose reduction: A phantom study"],"prefix":"10.1002","volume":"49","author":[{"given":"Jo\u00ebl","family":"Greffier","sequence":"first","affiliation":[{"name":"IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging N\u00eemes University Hospital  N\u00eemes France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Salim","family":"Si\u2010Mohamed","sequence":"additional","affiliation":[{"name":"University of Lyon INSA\u2010Lyon, University Claude Bernard Lyon 1, UJM\u2010Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206  Villeurbanne France"},{"name":"Department of Radiology, Louis Pradel Hospital Hospices Civils de Lyon  Bron France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julien","family":"Frandon","sequence":"additional","affiliation":[{"name":"IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging N\u00eemes University Hospital  N\u00eemes France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maeliss","family":"Loisy","sequence":"additional","affiliation":[{"name":"IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging N\u00eemes University Hospital  N\u00eemes France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabien","family":"de Oliveira","sequence":"additional","affiliation":[{"name":"IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging N\u00eemes University Hospital  N\u00eemes France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean Paul","family":"Beregi","sequence":"additional","affiliation":[{"name":"IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging N\u00eemes University Hospital  N\u00eemes France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Djamel","family":"Dabli","sequence":"additional","affiliation":[{"name":"IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging N\u00eemes University Hospital  N\u00eemes France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,6,24]]},"reference":[{"key":"e_1_2_11_2_1","unstructured":"BoedekerK.AiCE deep learning reconstruction: bringing the power of ultra\u2010high resolution CT to routine imaging. 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