Machine Learning and Deep Neural Networks Applications in Patient and Scan Preparation, Contrast Medium, and Radiation Dose Optimization

被引：23

作者：

Eberhard, Matthias ^{[1
]}

Alkadhi, Hatem ^{[1
]}

机构：

[1] Univ Zurich, Univ Hosp Zurich, Inst Diagnost & Intervent Radiol, Raemistr 100, CH-8091 Zurich, Switzerland

来源：

JOURNAL OF THORACIC IMAGING | 2020年 / 35卷

关键词：

artificial intelligence; radiation dose; reconstruction algorithms; scan preparation; cardiothoracic imaging; GENERATIVE ADVERSARIAL NETWORKS; COMPUTED-TOMOGRAPHY; PROTOCOL SELECTION; ARTIFACT REDUCTION; MRI; PREDICTION; ENHANCEMENT;

D O I：

10.1097/RTI.0000000000000482

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：

Artificial intelligence (AI) algorithms are dependent on a high amount of robust data and the application of appropriate computational power and software. AI offers the potential for major changes in cardiothoracic imaging. Beyond image processing, machine learning and deep learning have the potential to support the image acquisition process. AI applications may improve patient care through superior image quality and have the potential to lower radiation dose with AI-driven reconstruction algorithms and may help avoid overscanning. This review summarizes recent promising applications of AI in patient and scan preparation as well as contrast medium and radiation dose optimization.

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收藏

页码：S17 / S20

页数：4

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