title: Microemboli detection using ultrasound backscatter.
abstract: 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%.
authors.affiliation: Department of Medical Physics and Bioengineering, St. George's Hospital, Blackshaw Road, London, UK.
authors.forename: Mohamed A
authors.initials: MA
authors.lastname: El-Brawany
authors.forename: Dariush K
authors.initials: DK
authors.lastname: Nassiri
mesh_terms: D001783:Blood Flow Velocity; D004453:Echoencephalography / Q000379:methods*; D006801:Humans; D020766:Intracranial Embolism / Q000000981:diagnostic imaging*; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D019047:Phantoms, Imaging; D012372:ROC Curve; D011846:Radio Waves; D012542:Scattering, Radiation; D012815:Signal Processing, Computer-Assisted