肾盂图
放射科
分级(工程)
医学物理学
医学
断层摄影术
图像质量
计算机科学
计算机断层摄影术
迭代重建
人工智能
图像(数学)
工程类
土木工程
作者
Michaela Cellina,Maurizio Cè,Nicolo’ Rossini,Laura Maria Cacioppa,Velio Ascenti,Gianpaolo Carrafiello,Chiara Floridi
出处
期刊:Tomography
[MDPI AG]
日期:2023-04-30
卷期号:9 (3): 909-930
被引量:3
标识
DOI:10.3390/tomography9030075
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI