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

Kortenbout, A.J.; Costerus, Sophie; Dudink, Jeroen; de Jong, N.; de Graaff, Jurgen C.; Vos, H.J.; Bosch, Johan G.

doi:10.1016/j.ultrasmedbio.2023.12.006

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

Title

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

Author

Kortenbout, A.J. (Erasmus MC)
Costerus, Sophie (Erasmus MC)
Dudink, Jeroen (University Medical Center Utrecht)
de Jong, N. (TU Delft ImPhys/Verweij group; Erasmus MC)
de Graaff, Jurgen C. (Erasmus MC; Weill Cornell Medical College)
Vos, H.J. (TU Delft ImPhys/Verweij group; Erasmus MC)
Bosch, Johan G. (Erasmus MC)

Date

2024

Abstract

Objective: Post-operative brain injury in neonates may result from disturbed cerebral perfusion, but accurate peri-operative 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 because of 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. Methods: HFR ultrasound data from high-risk neonatal surgeries were recorded with a custom HFR mode (frame rate = 1000 Hz) 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 RIs from regular pulsed wave Doppler measurements in 10 neonates. Results: Envelope 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 (0.46 cm/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. Conclusion: The max-likelihood algorithm is a promising approach to accurate, automated cerebral blood flow monitoring with HFR imaging in neonates.

Subject

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

To reference this document use:

http://resolver.tudelft.nl/uuid:34f73c81-fc73-4583-afc7-55ab99d38ada

DOI

https://doi.org/10.1016/j.ultrasmedbio.2023.12.006

ISSN

0301-5629

Source

Ultrasound in Medicine & Biology, 50 (3), 434-444

Part of collection

Institutional Repository