  Ultrasound Localization Microscopy (ULM) is an emerging technique that
employs the localization of echogenic microbubbles (MBs) to finely sample and
image the microcirculation beyond the diffraction limit of ultrasound imaging.
Conventional MB localization methods are mainly based on considering a specific
Point Spread Function (PSF) for MBs, which leads to loss of information caused
by overlapping MBs, non-stationary PSFs, and harmonic MB echoes. Therefore, it
is imperative to devise methods that can accurately localize MBs while being
resilient to MB nonlinearities and variations of MB concentrations that distort
MB PSFs. This paper proposes a transformer-based MB localization approach to
address this issue. We adopted DEtection TRansformer (DETR) arXiv:2005.12872 ,
which is an end-to-end object recognition method that detects a unique bounding
box for each of the detected objects using set-based Hungarian loss and
bipartite matching. To the authors' knowledge, this is the first time
transformers have been used for MB localization. To appraise the proposed
strategy, the pre-trained DETR network's performance has been tested for
detecting MBs using transfer learning principles. We have fine-tuned the
network on a subset of randomly selected frames of the dataset provided by the
IEEE IUS Ultra-SR challenge organizers and then tested on the rest using
cross-validation. For the simulation dataset, the paper supports the deployment
of transformer-based solutions for MB localization at high accuracy.
