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机器学习和深度神经网络:在患者和扫描准备、造影剂和辐射剂量优化中的应用。

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

机构信息

Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.

出版信息

J Thorac Imaging. 2020 May;35 Suppl 1:S17-S20. doi: 10.1097/RTI.0000000000000482.

Abstract

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.

摘要

人工智能(AI)算法依赖于大量强大的数据以及适当的计算能力和软件的应用。人工智能在心胸影像学领域具有带来重大变革的潜力。除了图像处理,机器学习和深度学习也有可能辅助图像采集过程。人工智能应用可以通过提高图像质量改善患者护理,并且有可能通过人工智能驱动的重建算法降低辐射剂量,还可以帮助避免过度扫描。本文综述了人工智能在患者和扫描准备、对比剂和辐射剂量优化方面的最新有前途的应用。

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