Visualization and Quantification of the Cerebral Microcirculation using Contrast-enhanced Ultrasound Particle Tracking Velocimetry

Abstract

Noninvasive measurements of the regional microvascular perfusion might lead to sensitive biomarkers for the changes in intracranial hemodynamics that could guide timely surgical interventions for neonatal brain injuries. The current work utilizes a clinically available contrast enhanced ultrasound (CEUS) system and particle tracking velocimetry to perform ultrasound localization microscopy for measuring the microcirculation in piglets. A new deep learning method based on U-net is proposed for enhancing noisy raw CEUS images and detecting the microbubbles. Subsequently, the bubbles are tracked using a Kalman filter based method, which incorporates conditions of spatio-temporal consistency in flow direction and globally optimizes the assignment of bubbles to trajectories. Based on analysis of synthetic data, the U-net results demonstrate significant improvement in the processing speed and localization accuracy over a conventional blind deconvolution method. Visualization of the microvasculature is performed by superposing the bubble trajectories, enabling depiction of a complex micro-vessel network, where neighboring vessels separated by 40 µm can be distinguished. The corresponding perfusion map shows the velocity distribution in these vessels. Based on the current frame rate (44 fps), speeds in the 0.1 to 12 cm/s range can be well captured. These methods show promise as potential clinical tools for bedside measurement of cerebral microcirculation.