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Microembolus Tracking
with Power M-Mode Transcranial Doppler Ultrasound and Simultaneous Single Gate Spectrogram
MA Moehring
BACKGROUND AND
PURPOSE: After a decade since the discovery of microembolic signatures in brain
blood flow using single gate Doppler ultrasound, microemboli resulting from a variety of
pathologies and surgical interventions have been identified. However, the goal of
automatic detection of microemboli with high sensitivity and specificity has not been
realized with broad acceptance. This work summarizes initial experience with power m-mode
Doppler in constructing a platform aimed at more successful automatic detection of
microemboli.
METHODS: A 2MHz power m-mode Doppler (Spencer Technologies TCD100M)
having 33 sample gates placed at 2 mm intervals from 28 to 92mm depth was configured to
display Doppler signal power, colored red and blue for directionality, in an m-mode
format. PRF was set to 8kHz and sample volume size was 6mm. Stationary and clutter echoes
were assigned no color by rejecting autocorrelation velocities less than 200Hz. The
spectrogram for a selected gate depth was displayed simultaneously. 100 embolic signals
were collected from a 39 year old male subject with two St. Jude mechanical valves (aortic
and mitral). Emphasis was placed on aiming the probe so that the middle cerebral artery
and anterior cerebral artery flow signals were visible simultaneously on the m-mode
display.
RESULTS: Embolus "tracks" in the m-mode display showed embolic
spatial position versus time while the spectrogram showed velocity versus time at the
specified gate depth. If and when the embolus track in the m-mode display ran across the
spectrogram sample volume depth, the embolus appeared on the spectrogram. In particular,
embolic tracks were red and sloped up (motion towards the probe) or blue and sloped down
(motion away from the probe). While microemboli showed up distinctly on the m-mode
display, clutter artifacts did not.
CONCLUSIONS: These results indicate that automatic detection of
microemboli and discrimination from artifacts will be accomplishable on this platform with
greater acceptance by the medical community. This work supported by NIH grant
2R44HL57108-02.
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