Detection and volume estimation of embolic air in the middle cerebral artery using transcranial Doppler sonography.

BACKGROUND AND PURPOSE Cerebral embolism has been implicated in the development of cognitive and neurological deficits following bypass surgery. This study proposes methodology for estimating cerebral air embolus volume using transcranial Doppler sonography. METHODS Transcranial Doppler audio signals of air bubbles in the middle cerebral artery obtained from in vivo experiments were subjected to a fast-Fourier transform analysis. Audio segments when no air was present as well as artifact resulting from electrocautery and sensor movement were also subjected to fast-Fourier transform analysis. Spectra were compared, and frequency and power differences were noted and used for development of audio band-pass filters for isolation of frequencies associated with air emboli. In a bench model of the middle cerebral artery circulation, repetitive injections of various air volumes between 0.5 and 500 microL were made. Transcranial Doppler audio output was band-pass filtered, acquired digitally, then subjected to a fast-Fourier transform power spectrum analysis and power spectrum integration. A linear least-squares correlation was performed on the data. RESULTS Fast-Fourier transform analysis of audio segments indicated that frequencies between 250 and 500 Hz are consistently dominant in the spectrum when air emboli are present. Background frequencies appear to be below 240 Hz, and artifact resulting from sensor movement and electrocautery appears to be below 300 Hz. Data from the middle cerebral artery model filtered through a 307- to 450-Hz band-pass filter yielded a linear relation between emboli volume and the integrated value of the power spectrum near 40 microL. Detection of emboli less than 0.5 microL was inconsistent, and embolus volumes greater than 40 microL were indistinguishable from one another. CONCLUSIONS The preliminary technique described in this study may represent a starting point from which automated detection and volume estimation of cerebral emboli might be approached.