A novel technique for identification of Doppler microembolic signals based on the coincidence method - In vitro and in vivo evaluation

Background and Purpose The applicability of a novel differentiation technique in embolus detection based on the coincidence principle and using a multigate probe was evaluated in this study. Methods According to the coincidence method, high-intensity transients should only be classified as microembolic signals if they appear sequentially in the two sample volumes monitored and within a defined time window calculated from the blood velocity and the spatial distance between the insonation depths. Part A: microbubbles were introduced in a continuous flow bench model of the middle cerebral artery to evaluate the accuracy of the multigate probe in embolus detection. Part B: in the subjects and patients, the minimal and maximum time delays in the appearance of microembolic signals in the two middle cerebral artery sample volumes were calculated as 0.01 second and set at 0.1 second, respectively. The multigate probe was used to monitor (1) 5 normal volunteers in whom 1008 artifact signals were produced, (2) 2 patients undergoing aortic valve replacement surgery, and (3) 12 patients with potential cardiac or carotid embolic sources. Results In the bench model, 95.5% of microembolic signals produced by microbubbles appeared in the two sample volumes with a time delay between 0.02 and 0.05 second, while in the remaining 4.5% a shorter passage time of 0.01 second was measured. A total of 1968 high-intensity signals were recorded in subjects and patients. All but 20 of these (99%) appeared in both monitoring channels within the above time frame. To summarize, 996 (98.8%) of the 1004 artifact signals and 943 (98.1%) of the 961 microembolic signals were correctly classified. Conclusions Application of the coincidence theory to distinguish microembolic signals from artifacts provides a promising new technique with high sensitivity and specificity that could decisively improve the validity of embolus detection.