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Deep learning reconstruction algorithm and high-concentration contrast medium: feasibility of a double-low protocol in coronary computed tomography angiography.

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Title: Deep learning reconstruction algorithm and high-concentration contrast medium: feasibility of a double-low protocol in coronary computed tomography angiography.
Authors: Caruso D; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy., De Santis D; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy., Tremamunno G; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy., Santangeli C; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy., Polidori T; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy., Bona GG; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy., Zerunian M; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy., Del Gaudio A; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy., Pugliese L; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy., Laghi A; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy. andrea.laghi@uniroma1.it.
Source: European radiology [Eur Radiol] 2025 Apr; Vol. 35 (4), pp. 2213-2221. Date of Electronic Publication: 2024 Sep 19.
Publication Type: Journal Article; Randomized Controlled Trial
Journal Info: Publisher: Springer International Country of Publication: Germany NLM ID: 9114774 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-1084 (Electronic) Linking ISSN: 09387994 NLM ISO Abbreviation: Eur Radiol Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: Deep learning reconstruction algorithm and high-concentration contrast medium: feasibility of a double-low protocol in coronary computed tomography angiography.
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  Data: <searchLink fieldCode="AU" term="%22Caruso+D%22">Caruso D</searchLink>; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy.<br /><searchLink fieldCode="AU" term="%22De+Santis+D%22">De Santis D</searchLink>; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy.<br /><searchLink fieldCode="AU" term="%22Tremamunno+G%22">Tremamunno G</searchLink>; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy.<br /><searchLink fieldCode="AU" term="%22Santangeli+C%22">Santangeli C</searchLink>; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy.<br /><searchLink fieldCode="AU" term="%22Polidori+T%22">Polidori T</searchLink>; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy.<br /><searchLink fieldCode="AU" term="%22Bona+GG%22">Bona GG</searchLink>; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy.<br /><searchLink fieldCode="AU" term="%22Zerunian+M%22">Zerunian M</searchLink>; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy.<br /><searchLink fieldCode="AU" term="%22Del+Gaudio+A%22">Del Gaudio A</searchLink>; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy.<br /><searchLink fieldCode="AU" term="%22Pugliese+L%22">Pugliese L</searchLink>; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy.<br /><searchLink fieldCode="AU" term="%22Laghi+A%22">Laghi A</searchLink>; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy. andrea.laghi@uniroma1.it.
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  Data: <searchLink fieldCode="JN" term="%229114774%22">European radiology</searchLink> [Eur Radiol] 2025 Apr; Vol. 35 (4), pp. 2213-2221. <i>Date of Electronic Publication: </i>2024 Sep 19.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Springer+International%22">Springer International </searchLink><i>Country of Publication: </i>Germany <i>NLM ID: </i>9114774 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1432-1084 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2209387994%22">09387994 </searchLink><i>NLM ISO Abbreviation: </i>Eur Radiol <i>Subsets: </i>MEDLINE
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        Value: 10.1007/s00330-024-11059-x
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              Text: 2025 Apr
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