Tissue typing using ultrasound RF time series: experiments with animal tissue samples.
OBJECTIVE
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.


METHODS
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.


RESULTS
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.


CONCLUSIONS
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.
Moradi
Mehdi
M
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver Canada. moradi@ece.ubc.ca
Abolmaesumi
Purang
P
Mousavi
Parvin
P
D000465:Algorithms; D000818:Animals; D002417:Cattle; D002645:Chickens; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000000981:diagnostic imaging*; D008327:Mammography / Q000379:methods*; D018482:Muscle, Skeletal / Q000000981:diagnostic imaging*; D011846:Radio Waves; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013552:Swine; D014463:Ultrasonography / Q000379:methods*