从超声波计算声音速度:用户不可知恢复和一个新的基准
IEEE transactions on bio-medical engineering
|October 24, 2023
概括
这项研究引入了一种使用IQ相信息的新方法,以提高超声波成像中的声速 (SoS) 恢复的稳定性. 这种方法克服了域转移的挑战,为实时组织属性分析铺平了道路.
科学领域:
- 医疗成像医学成像
- 生物医学工程 生物医学工程
- 信号处理 信号处理
背景情况:
- 医学超声波的B模式成像缺乏内部器官的定量物理特性.
- 原始超声波信号含有比B模式图像更多的信息,使组织属性映射成为可能.
- 深度学习对恢复音速 (SoS) 地图有希望,但从模拟到真实数据的域转移是一个挑战.
研究的目的:
- 为了解决超声波定量参数恢复中对系统响应的操作者依赖的影响.
- 为了提高从原始超声数据的音速 (SoS) 映射的稳定性和通用性.
- 为了克服超声成像深度学习模型中的域转移问题.
主要方法:
- 利用了智商调节超声波信号的相位信息.
- 开发了一种新的方法,可以从数据中解脱运营商依赖的系统响应.
- 引入了改进的网络拓,以提高深度学习的性能.
主要成果:
- 智商阶段信息显著改善了音速恢复的稳定性.
- 这种新的方法有效地解开了运营商依赖的系统响应.
- 通过增强的网络拓学,实现更快,更好的最新结果.
- 发布了第一个公开可用的原始超声波平面波处理的基准数据集.
结论:
- 考虑IQ信号相位信息是超声波中转移学习问题的有希望的解决方案.
- 这一进步更接近于实现实时音速 (SoS) 成像.
- 开发的方法提高了定量超声波技术的可靠性和适用性.
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