Enhancing super-resolution ultrasound localisation through multi-frame deconvolution exploiting spatiotemporal consistency

Super-resolution ultrasound (SRUS) imaging through localisation and tracking of microbubble (MB), also known as ultrasound localisation microscopy (ULM), allows non-invasive imaging of microvasculature in vivo beyond the diffraction limit. The number of MBs localised from the acquired contrast-enhanced ultrasound (CEUS) images and the localisation accuracy 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 consistency 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, F₁ 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.