Impact of cardiopulmonary bypass on cerebrovascular autoregulation assessed by ultrafast ultrasound imaging

Cerebral blood flow dynamics during cardiac surgery in infants

BACKGROUND

In this pilot study, we investigated continuous cerebral blood flow velocity measurements to explore cerebrovascular hemodynamics in infants with congenital heart disease undergoing cardiac surgery.

METHODS

A non-invasive transfontanellar cerebral Doppler monitor (NeoDoppler) was used to monitor 15 infants (aged eight days to nine months) during cardiac surgery with cardiopulmonary bypass. Numerical and visual analyses were conducted to assess trends and events in Doppler measurements together with standard monitoring equipment. The mean flow index, calculated as the moving Pearson correlation between mean arterial pressure and time averaged velocity, was utilized to evaluate dynamic autoregulation. Two levels of impaired autoregulation were defined (Mean flow index >0.3/0.45), and percentage of time above these limits were calculated.

RESULTS

High quality recordings were achieved during 90.6% of the monitoring period. There was a significant reduction in time averaged velocity in all periods of cardiopulmonary bypass. All patients showed a high percentage of time with impaired dynamic autoregulation, with Mean flow index >0.3 and 0.45: 73.71% ± 9.06% and 65.16% ± 11.27% respectively. Additionally, the system promptly detected hemodynamic events.

CONCLUSION

Continuous transfontanellar cerebral Doppler monitoring could become an additional tool in enhancing cerebral monitoring in infants during cardiac surgery.

IMPACT

This pilot study demonstrates the feasibility of continuous transfontanellar Doppler monitoring of cerebral blood flow velocities during cardiac surgery in infants. It also demonstrates a high proportion of time with impaired cerebral autoregulation during cardiac surgery based on the Mean flow index. Continuous transfontanellar Doppler could become a useful tool to improve cerebral monitoring and provide new pathophysiological insight.

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

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.

Novel Techniques in Imaging Congenital Heart Disease: JACC Scientific Statement

Recent years have witnessed exponential growth in cardiac imaging technologies, allowing better visualization of complex cardiac anatomy and improved assessment of physiology. These advances have become increasingly important as more complex surgical and catheter-based procedures are evolving to address the needs of a growing congenital heart disease population. This state-of-the-art review presents advances in echocardiography, cardiac magnetic resonance, cardiac computed tomography, invasive angiography, 3-dimensional modeling, and digital twin technology. The paper also highlights the integration of artificial intelligence with imaging technology. While some techniques are in their infancy and need further refinement, others have found their way into clinical workflow at well-resourced centers. Studies to evaluate the clinical value and cost-effectiveness of these techniques are needed. For techniques that enhance the value of care for congenital heart disease patients, resources will need to be allocated for education and training to promote widespread implementation.