Cowe Joanne, Evans David H
Department of Medical Physics, University Hospitals of Leicester NHS Trust, Leicester, UK.
Ultrasound Med Biol. 2006 Dec;32(12):1853-67. doi: 10.1016/j.ultrasmedbio.2006.06.019.
The transcranial Doppler (TCD) radio-frequency (RF) signal can provide additional information on events recorded during ultrasonic monitoring. Embolic signals appear as uniform and predictable shapes within the RF signal, enabling pattern recognition and image processing techniques to be used for their automated detection. This paper uses principal component analysis (PCA) to characterise the typical variation in embolic signal shape, within the RF signal, using training sets of in vitro and in vivo data. PCA techniques are then utilised to discriminate between previously unseen embolic and artifact signals. Although the results of this study show that the algorithms described in this paper do not yet have the accuracy required for their use in a clinical setting, it does demonstrate that this novel technique has the potential to be developed further.
经颅多普勒（TCD）射频（RF）信号可以为超声监测期间记录的事件提供额外信息。栓子信号在RF信号中呈现出均匀且可预测的形状，这使得模式识别和图像处理技术可用于对其进行自动检测。本文使用主成分分析（PCA），利用体外和体内数据集来表征RF信号中栓子信号形状的典型变化。然后利用PCA技术区分先前未见过的栓子信号和伪像信号。尽管本研究结果表明本文所述算法尚未具备在临床环境中使用所需的准确性，但确实证明了这种新技术有进一步发展的潜力。
