  Super-resolution ultrasound imaging through microbubble (MB) localisation and
tracking, also known as ultrasound localisation microscopy, allows non-invasive
sub-diffraction resolution imaging of microvasculature in animals and humans.
The number of MBs localised from the acquired contrast-enhanced ultrasound
(CEUS) images and the localisation precision directly influence the quality of
the resulting super-resolution microvasculature images. However, non-negligible
noise present in the CEUS images can make localising MBs challenging. To
enhance the MB localisation performance, we propose a Multi-Frame Deconvolution
(MF-Decon) framework that can exploit the spatiotemporal coherence inherent in
the CEUS data, with new spatial and temporal regularisers designed based on
total variation (TV) and regularisation by denoising (RED). Based on the
MF-Decon framework, we introduce two novel methods: MF-Decon with spatial and
temporal TVs (MF-Decon+3DTV) and MF-Decon with spatial RED and temporal TV
(MF-Decon+RED+TV). Results from in silico simulations indicate that our methods
outperform two widely used methods using deconvolution or normalised
cross-correlation across all evaluation metrics, including precision, recall,
$F_1$ score, mean and standard localisation errors. In particular, our methods
improve MB localisation precision by up to 39% and recall by up to 12%.
Super-resolution microvasculature maps generated with our methods on a publicly
available in vivo rat brain dataset show less noise, better contrast, higher
resolution and more vessel structures.
