Deep-fUS: functional ultrasound imaging of the brain using deep learning and sparse data

Abstract

Functional ultrasound (fUS) is a rapidly emerging modality that enables whole-brain imaging of neural activity in awake and mobile rodents. To achieve sufficient blood flow sensitivity in the brain microvasculature, fUS relies on long ultrasound data acquisitions at high frame rates, posing high demands on the sampling and processing hardware. Here we develop an end-to-end image reconstruction approach based on deep learning that significantly reduces the amount of data necessary while retaining the imaging performance. We trained a convolutional neural network to learn the power Doppler reconstruction function from sparse sequences of ultrasound data with a compression factor up to 95%, using high-quality images from in vivo acquisitions in rats. We tested the imaging performance in a functional neuroimaging application. We demonstrate that time series of power Doppler images can be reconstructed with sufficient accuracy to detect the small changes in cerebral blood volume (~10%) characteristic of task-evoked cortical activation, even though the network was not formally trained to reconstruct such image series. The proposed platform may facilitate the development of this neuroimaging modality in any setting where dedicated hardware is not available or in clinical scanners.