  Ultrasound Localization Microscopy (ULM) has recently enabled the mapping of
the cerebral vasculature in vivo with a resolution ten times smaller than the
wavelength used, down to ten microns. However, with frame rates up to 20.000
frames per second, this method requires large amount of data to be acquired,
transmitted, stored, and processed. The transfer rate is, as of today, one of
the main limiting factors of this technology. Herein, we introduce a novel
reconstruction framework to decrease this quantity of data to be acquired and
the complexity of the required hardware by randomly subsampling the channels of
a linear probe. Method performance evaluation as well as parameters
optimization were conducted in silico using the SIMUS simulation software in an
anatomically realistic phantom and then compared to in vivo acquisitions in a
rat brain after craniotomy. Results show that reducing the number of active
elements deteriorates the signal-to-noise ratio and could lead to false
microbubbles detections but has limited effect on localization accuracy. In
simulation, the false positive rate on microbubble detection deteriorates from
3.7% for 128 channels in receive and 7 steered angles to 11% for 16 channels
and 7 angles. The average localization accuracy ranges from 10.6 um and 9.93 um
for 16 channels/3 angles and 128 channels/13 angles respectively. These results
suggest that a compromise can be found between the number of channels and the
quality of the reconstructed vascular network and demonstrate feasibility of
performing ULM with a reduced number of channels in receive, paving the way for
low-cost devices enabling high-resolution vascular mapping.
