Automatic Max-Likelihood Envelope Detection Algorithm for Quantitative High-Frame-Rate Ultrasound for Neonatal Brain Monitoring

Abstract

Background and AimPostoperative brain injury in neonates may result from disturbed cerebral perfusion, but accurate perioperative monitoring is lacking. High frame rate (HFR) cerebral ultrasound could visualize and quantify flow in all detectable vessels using spectral Doppler; however, automated quantification in small vessels is challenging due to low signal amplitude. We have developed an automatic envelope detection algorithm for HFR pulsed wave spectral Doppler signals, enabling neonatal brain quantitative parameter maps during and after surgery. MethodsHFR ultrasound data from high-risk neonatal surgeries were recorded with a custom HFR mode (framerate 1000Hz) on a Zonare ZS3 system. A pulsed wave Doppler spectrogram was calculated for each pixel containing blood flow in the image, and spectral-peak velocity was tracked using a max-likelihood estimation algorithm of signal and noise regions in the spectrogram, where the most likely cross-over point marks the blood flow velocity. The resulting peak-systolic velocity (PSV), end-diastolic velocity (EDV) and resistivity index (RI) were compared with other detection schemes, manual tracking, and RI from regular pulsed wave Doppler measurements in 10 neonates. ResultsEnvelope detection was successful in both high- and low-quality arterial and venous flow spectrograms. Our technique had the lowest root mean square error for EDV, PSV, and RI (0.46cm/s, 0.53 cm/s, and 0.15, respectively) when compared with manual tracking. There was good agreement between the clinical pulsed wave Doppler RI and HFR measurement with a mean difference of 0.07. ConclusionThe max-likelihood algorithm is a promising approach for accurate, automated cerebral blood flow monitoring with high-frame rate imaging in neonates.