A Deep Learning Framework for Spatiotemporal Ultrasound Localization
  Microscopy
Léo Milecki Jonathan Porée Hatim Belgharbi Chloé Bourquin Rafat Damseh Patrick Delafontaine-Martel Frédéric Lesage Maxime Gasse Jean Provost
Ultrasound Localization Microscopy can resolve the microvascular bed down to
a few micrometers. To achieve such performance microbubble contrast agents must
perfuse the entire microvascular network. Microbubbles are then located
individually and tracked over time to sample individual vessels, typically over
hundreds of thousands of images. To overcome the fundamental limit of
diffraction and achieve a dense reconstruction of the network, low microbubble
concentrations must be used, which lead to acquisitions lasting several
minutes. Conventional processing pipelines are currently unable to deal with
interference from multiple nearby microbubbles, further reducing achievable
concentrations. This work overcomes this problem by proposing a Deep Learning
approach to recover dense vascular networks from ultrasound acquisitions with
high microbubble concentrations. A realistic mouse brain microvascular network,
segmented from 2-photon microscopy, was used to train a three-dimensional
convolutional neural network based on a V-net architecture. Ultrasound data
sets from multiple microbubbles flowing through the microvascular network were
simulated and used as ground truth to train the 3D CNN to track microbubbles.
The 3D-CNN approach was validated in silico using a subset of the data and in
vivo on a rat brain acquisition. In silico, the CNN reconstructed vascular
networks with higher precision (81%) than a conventional ULM framework (70%).
In vivo, the CNN could resolve micro vessels as small as 10 $\mu$m with an
increase in resolution when compared against a conventional approach.