A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy
This work addresses a bottleneck in ultrasound localization microscopy for medical imaging by enabling faster acquisitions with higher microbubble concentrations, though it is incremental as it builds on existing deep learning methods.
The paper tackled the problem of reconstructing dense microvascular networks from ultrasound data with high microbubble concentrations, which conventional methods struggle with due to interference. It proposed a deep learning approach using a 3D CNN trained on simulated data, achieving higher precision (81% vs. 70%) in silico and resolving vessels as small as 10 μm in vivo.
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 $μ$m with an increase in resolution when compared against a conventional approach.