Computed Tomography Urography: State of the Art and Beyond

被引:15
作者
Cellina, Michaela [1 ]
Ce, Maurizio [2 ]
Rossini, Nicolo' [3 ]
Cacioppa, Laura Maria [4 ]
Ascenti, Velio [2 ]
Carrafiello, Gianpaolo [5 ]
Floridi, Chiara [4 ,6 ]
机构
[1] ASST Fatebenefratelli Sacco, Fatebenefratelli Hosp, Radiol Dept, Piazza Principessa Clotilde 3, I-20121 Milan, Italy
[2] Univ Milan, Postgrad Sch Radiodiag, Via Festa Perdo 7, I-20122 Milan, Italy
[3] Univ Politecn Marche, Dept Clin Special & Dent Sci, I-60126 Ancona, Italy
[4] Univ Politecn Marche, Dept Radiol Sci, Div Intervent Radiol, I-60126 Ancona, Italy
[5] Fdn IRCCS CaGranda, Radiol Dept, Policlin Milano Osped Maggiore, Via Francesco Sforza 35, I-20122 Milan, Italy
[6] Univ Hosp Umberto I Lancisi Salesi, Dept Radiol, Div Special & Pediat Radiol, I-60126 Ancona, Italy
关键词
CT urography; renal cancer imaging; artificial intelligence; Dual-Energy Computed Tomography; AI-based reconstruction algorithms; Computed Tomography; DUAL-ENERGY CT; MULTIDETECTOR ROW CT; RENAL-CELL CARCINOMA; UPPER URINARY-TRACT; IMAGE QUALITY; CLEAR-CELL; UROTHELIAL TUMORS; TEXTURE ANALYSIS; AIDED DETECTION; CONTRAST-MEDIUM;
D O I
10.3390/tomography9030075
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Computed Tomography Urography (CTU) is a multiphase CT examination optimized for imaging kidneys, ureters, and bladder, complemented by post-contrast excretory phase imaging. Different protocols are available for contrast administration and image acquisition and timing, with different strengths and limits, mainly related to kidney enhancement, ureters distension and opacification, and radiation exposure. The availability of new reconstruction algorithms, such as iterative and deep-learning-based reconstruction has dramatically improved the image quality and reducing radiation exposure at the same time. Dual-Energy Computed Tomography also has an important role in this type of examination, with the possibility of renal stone characterization, the availability of synthetic unenhanced phases to reduce radiation dose, and the availability of iodine maps for a better interpretation of renal masses. We also describe the new artificial intelligence applications for CTU, focusing on radiomics to predict tumor grading and patients' outcome for a personalized therapeutic approach. In this narrative review, we provide a comprehensive overview of CTU from the traditional to the newest acquisition techniques and reconstruction algorithms, and the possibility of advanced imaging interpretation to provide an up-to-date guide for radiologists who want to better comprehend this technique.
引用
收藏
页码:909 / 930
页数:22
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