优化用于超声波成像的阵列编码
Jacob Spainhour1, Korben Smart2, Stephen Becker1
1Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, United States of America.
Physics in medicine and biology
|May 30, 2024
概括
机器学习优化超声波成像序列,以获得更好的分辨率和对比度. 这种方法探索了超越传统方法的新型扫描模式,提高了B模式图像质量.
科学领域:
- 超声波成像 超声波成像
- 医学成像物理学 医学成像物理学
- 机器学习应用程序 机器学习应用程序
背景情况:
- 发射编码模型对于理解合成光圈成像中的声传输效应至关重要.
- 目前的扫描序列代表了超声波图像重建可能性的有限子集.
研究的目的:
- 利用机器学习 (ML) 开发优化的扫描序列,以获得高质量的B模式超声波图像.
- 在合成光圈成像中探索超越传统方法的新型编码序列.
主要方法:
- 在PyTorch中使用来自Field II的模拟射频 (RF) 数据开发了一个定制的ML模型.
- 该模型探测编码序列 (时间延迟,apodization权重) 以尽量减少图像质量损失的功能.
- 对于延迟和总和束形成的新型衍生配方使得计算可行性成为可能.
主要成果:
- 用ML优化编码序列,当与REFoCUS成像框架一起使用时,显著改善分辨率,视野和对比度.
- 对线材目标和模仿组织的幻影进行实验验证,证实了增强的图像质量指标.
- 与传统扫描序列相比,ML方法显示出更高的性能.
结论:
- 机器学习可以发现和优化用于合成发射孔径成像的新型扫描序列.
- 在ML模型中整合光束成型对于合成发射孔径成像任务是有价值的.
- 这项工作扩大了对可用的编码方案的理解,超越了狭窄,常用的子集.
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