使用基于实时和高精度形状估计的灵活传感器进行超声波成像
Xue Gao1, Lihong Huang1, Peng Huang1
1Department of Biomedical Engineering, Fudan University, Shanghai 200438, China.
Ultrasonics
|December 18, 2024
概括
这项研究介绍了FlexSANet,这是一种用于快速准确的灵活超声波传感器形状估计的深度学习方法. 它可以实现实时,高质量的超声波成像,这对于临床应用至关重要.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 灵活的超声波传感器需要精确的形状几何来进行有效的光束成形.
- 传统的形状估计方法在计算上昂贵,而深度学习方法缺乏准确性.
- 准确的形状估计对于高质量的超声波图像重建至关重要.
研究的目的:
- 开发一种基于深度学习的新方法,用于快速准确地估计灵活的超声波传感器的形状.
- 为了提高超声波成像中的形状估计的精度和降低计算成本.
- 使用灵活的传感器实现实时,高质量的超声波成像.
主要方法:
- 从原始无线电频率 (RF) 数据中解调相位/方位 (I/Q) 数据.
- 稀疏处理以提取关键通道信号,减少数据大小和估计时间.
- 一个空间感知形状估计网络 (FlexSANet),用于从I/Q数据到探测形状的一次性映射.
- 使用延迟和总和 (DAS) 束造型器与估计形状的超声波图像重建.
主要成果:
- FlexSANet实现了快速而准确的传感器形状估计.
- 元素位置的平均绝对误差低于1/8波长.
- 用真实形状和估计形状重建的图像之间的结构相似度指数超过了0.84 (模拟) 和0.80 (体内).
- 对CPU的估计时间低至0.12秒.
结论:
- 拟议的FlexSANet方法显著提高了柔性超声波传感器的形状估计精度和速度.
- 该方法可实现实时,高质量的超声波成像,克服现有方法的局限性.
- 低估计时间和高精度表明灵活超声波传感器在临床应用方面有很大的潜力.
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