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Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates

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

Anna J Kortenbout¹, Sophie Costerus², Jeroen Dudink³

¹Biomedical Engineering, Department of Cardiology, University Medical Center Rotterdam, Erasmus MC, Rotterdam, The Netherlands.

Ultrasound in Medicine & Biology

|December 24, 2023

Summary

This summary is machine-generated.

A new algorithm enhances high-frame-rate (HFR) ultrasound for precise neonatal brain blood flow monitoring. This automated technique improves quantification of cerebral perfusion, aiding in the prevention of post-operative brain injury in infants.

Keywords:

Cerebral ultrasound Envelope detection High-frame-rate ultrasound High-risk surgery Max-likelihood Monitoring Neonates Pulsed wave Doppler Spectral Doppler

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A Modified Sonographic Algorithm for Image Acquisition in Life-Threatening Emergencies in the Critically Ill Newborn

A Modified Sonographic Algorithm for Image Acquisition in Life-Threatening Emergencies in the Critically Ill Newborn

Published on: April 7, 2023

Area of Science:

Medical Imaging
Neonatal Neurology
Ultrasound Technology

Background:

Post-operative brain injury in neonates is a concern due to potential cerebral perfusion disturbances.
Accurate peri-operative monitoring of cerebral blood flow in neonates is currently lacking.
High-frame-rate (HFR) cerebral ultrasound offers potential for visualizing and quantifying flow in all detectable vessels.

Purpose of the Study:

To develop an automated envelope detection algorithm for HFR pulsed wave spectral Doppler signals.
To enable quantitative parameter mapping of neonatal brain blood flow during and after surgery.
To address the challenge of automated quantification in small vessels with low signal amplitude.

Main Methods:

Recorded HFR ultrasound data (1000 Hz frame rate) during high-risk neonatal surgeries.
Calculated pulsed wave Doppler spectrograms and tracked spectral peak velocity using a max-likelihood estimation algorithm.
Compared automated measurements of peak systolic velocity (PSV), end-diastolic velocity (EDV), and resistivity index (RI) with manual tracking and conventional Doppler in 10 neonates.

Main Results:

Successful envelope detection in both high- and low-quality arterial and venous flow spectrograms.
The developed technique demonstrated the lowest root mean square error for EDV, PSV, and RI compared to manual tracking.
Achieved good agreement between HFR measurements and clinical pulsed wave Doppler RI, with a mean difference of 0.07.

Conclusions:

The max-likelihood algorithm shows promise for accurate, automated cerebral blood flow monitoring in neonates using HFR imaging.
This automated approach can facilitate improved quantitative parameter maps for neonatal brain perfusion.
The findings support the potential of HFR ultrasound for enhanced peri-operative monitoring in high-risk neonatal surgeries.