Department of Electrical and Computer Engineering, University of British Columbia, Vancouver Canada.
Med Phys. 2010 Aug;37(8):4401-13. doi: 10.1118/1.3457710.
This article provides experimental evidence to show that the time series of radiofrequency (RF) ultrasound data can be used for tissue typing. It also explores the tissue typing information in RF time series. Clinical and high-frequency ultrasound are studied.
Bovine liver, pig liver, bovine muscle, and chicken breast were used in the experiments as the animal tissue types. In the proposed approach, the authors record RF echo signals backscattered from tissue, while the imaging probe and the tissue are stationary. This sequence of recorded RF data generates a time series of RF echoes for each spatial sample of the RF signal. The authors use spectral and fractal features of ultrasound RF time series averaged over a region of interest, along with feedforward neural networks for tissue typing. The experiments are repeated at ultrasound frequency of 6.6 and also 55 MHz. The effects of increasing power and frame rate are studied.
The methodology yielded an average two-class classification accuracy of 95.1% when ultrasound data were acquired at 6.6 MHz and 98.1% when data were collected with a high-frequency probe operating at 55 MHz. In four-class classification experiments, the recorded accuracies were 78.6% and 86.5% for low and high-frequency ultrasound data, respectively. A set of 12 texture features extracted from the B-mode image equivalents of the RF data yields an accuracy of only 77.5% in typing the analyzed tissues. An increase in acoustic power and the frame rate of ultrasound results in an improvement in classification results.
The results of this study demonstrate that RF time series can be used for ultrasound-based tissue typing. Further investigation of the underlying physical mechanisms is necessary.
本文提供了实验证据，表明射频（RF）超声数据的时间序列可用于组织分型。还探讨了 RF 时间序列中的组织分型信息。研究了临床和高频超声。
牛肝、猪肝、牛肌肉和鸡胸被用作动物组织类型。在提出的方法中，作者记录了从组织反向散射的 RF 回波信号，而成像探头和组织是静止的。记录的 RF 数据序列为 RF 信号的每个空间样本生成 RF 回波的时间序列。作者使用感兴趣区域内的超声 RF 时间序列的频谱和分形特征，以及前馈神经网络进行组织分型。在 6.6MHz 和 55MHz 的超声频率下重复实验。研究了增加功率和帧率的影响。
当在 6.6MHz 采集超声数据时，该方法的平均两分类分类准确率为 95.1%，当使用工作频率为 55MHz 的高频探头采集数据时，准确率为 98.1%。在四分类实验中，低频和高频超声数据的记录准确率分别为 78.6%和 86.5%。从 RF 数据的 B 模式图像等效物中提取的 12 组纹理特征在对分析组织进行分类时的准确率仅为 77.5%。增加声功率和超声帧率可提高分类结果。
本研究的结果表明，RF 时间序列可用于基于超声的组织分型。需要进一步研究潜在的物理机制。
