M. L. Lediju Bell
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引用次数: 0

摘要

从历史上看,有许多选项可以提高图像质量,每个选项都来自相同的原始超声传感器数据。但是,这些历史选项都不能在单个图像形成步骤中合并多个贡献。这篇特邀文章讨论了在学习声波传播的物理原理后,波束成形原始超声传感器数据的新替代方案,以提高图像质量、传输速度和特征检测。应用包括囊肿检测、基于相干的波束形成和COVID-19特征检测。总结了整个社会规范和加速超声波束形成和深度学习交叉研究的新资源(https://cubdl.jhu.edu)。本文还从光声源检测、反射伪影去除、分辨率提高等方面讨论了超声软硬件集成与光学的联系。这些创新展示了在深度学习的帮助下,在单个信号处理步骤中结合多个输出和收益的巨大潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applications of ultrasound image formation in the deep learning age
Historically, there are many options to improve image quality that are each derived from the same raw ultrasound sensor data. However, none of these historical options combine multiple contributions in a single image formation step. This invited contribution discusses novel alternatives to beamforming raw ultrasound sensor data to improve image quality, delivery speed, and feature detection after learning from the physics of sound wave propagation. Applications include cyst detection, coherence-based beamforming, and COVID-19 feature detection. A new resource for the entire community to standardize and accelerate research at the intersection of ultrasound beamforming and deep learning is summarized (https://cubdl.jhu.edu). The connection to optics with the integration of ultrasound hardware and software is also discussed from the perspective of photoacoustic source detection, reflection artifact removal, and resolution improvements. These innovations demonstrate outstanding potential to combine multiple outputs and benefits in a single signal processing step with the assistance of deep learning.
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