计算机科学
深度学习
人工智能
杂乱
信号(编程语言)
机器学习
原始数据
钥匙(锁)
模式识别(心理学)
雷达
电信
计算机安全
程序设计语言
作者
Ruud J. G. van Sloun,Regev Cohen,Yonina C. Eldar
出处
期刊:Proceedings of the IEEE
[Institute of Electrical and Electronics Engineers]
日期:2020-01-01
卷期号:108 (1): 11-29
被引量:207
标识
DOI:10.1109/jproc.2019.2932116
摘要
In this article, we consider deep learning strategies in ultrasound systems, from the front end to advanced applications. Our goal is to provide the reader with a broad understanding of the possible impact of deep learning methodologies on many aspects of ultrasound imaging. In particular, we discuss methods that lie at the interface of signal acquisition and machine learning, exploiting both data structure (e.g., sparsity in some domain) and data dimensionality (big data) already at the raw radio-frequency channel stage. As some examples, we outline efficient and effective deep learning solutions for adaptive beamforming and adaptive spectral Doppler through artificial agents, learn compressive encodings for the color Doppler, and provide a framework for structured signal recovery by learning fast approximations of iterative minimization problems, with applications to clutter suppression and super-resolution ultrasound. These emerging technologies may have a considerable impact on ultrasound imaging, showing promise across key components in the receive processing chain.
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