Mohamed A El-Brawany | Search PubMed

PubMed2002

New approach for modelling ultrasound blood backscatter signal.

The ultrasound (US) scattered signal from blood has been treated as a random signal by many investigators. However, the degree of randomness of a medium is a relative term that can change considerably with the resolution of the sensor. In this study, the backscattered signal from blood has been looked at as a chaotic signal. By this treatment, according to Taken's theorem, a single variable (e.g., amplitude of the blood-backscattered signal) can be used to reconstruct the nonlinear dynamics of the blood-scattered signal. Multilayer perceptron neural network architecture, with error back-propagation, has been formulated and used as a basis for building and testing the chaotic model of the backscattered signal. This chaotic model is used successfully as a short-term predictor of the backscattered signal from blood-mimicking fluid (BMF) flowing in a vascular flow phantom under pulsatile flow. This modelling approach can be useful, for example, in detecting blood-borne emboli.

Blood↗

PubMedDate not supplied

Microemboli detection using ultrasound backscatter.

Microemboli detection and characterisation have recently received great attention due to its clinical importance in the management of cerebrovascular disease. The new method presented in this paper is directly based on the idea that the ultrasound (US) backscattered signal from flowing blood is chaotic (El-Brawany and Nassiri 2002). The detection technique involves building a nonlinear model of the deterministic characteristics of the chaotic backscatter signal from blood, and the use of this model to look at the prediction error as a primary decision-making criterion for the microemboli detector. A complementary feature to the prediction error, namely, the degree of coherence between the US excitation pulse and the prediction error signal is used to enhance the detection process. The detector has been built using a feed-forward neural network with error back-propagation. The detection technique is tested successfully using a vascular flow phantom with solid spheres and bubbles of known sizes introduced in the flow circuit to mimic solid and gaseous emboli. Receiver operating characteristic (ROC) curve is used to assess the performance of the detection process. The total classification rate ranges from 88% to 96%.

Blood Flow Velocity↗