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. |
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| 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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