  Ultrasound Localization Microscopy (ULM) is a non-invasive technique that
allows for the imaging of micro-vessels in vivo, at depth and with a resolution
on the order of ten microns. ULM is based on the sub-resolution localization of
individual microbubbles injected in the bloodstream. Mapping the whole
angioarchitecture requires the accumulation of microbubbles trajectories from
thousands of frames, typically acquired over a few minutes. ULM acquisition
times can be reduced by increasing the microbubble concentration, but requires
more advanced algorithms to detect them individually. Several deep learning
approaches have been proposed for this task, but they remain limited to 2D
imaging, in part due to the associated large memory requirements. Herein, we
propose to use sparse tensor neural networks to reduce memory usage in 2D and
to improve the scaling of the memory requirement for the extension of deep
learning architecture to 3D. We study several approaches to efficiently convert
ultrasound data into a sparse format and study the impact of the associated
loss of information. When applied in 2D, the sparse formulation reduces the
memory requirements by a factor 2 at the cost of a small reduction of
performance when compared against dense networks. In 3D, the proposed approach
reduces memory requirements by two order of magnitude while largely
outperforming conventional ULM in high concentration settings. We show that
Sparse Tensor Neural Networks in 3D ULM allow for the same benefits as dense
deep learning based method in 2D ULM i.e. the use of higher concentration in
silico and reduced acquisition time.
