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

TL;DR

This paper introduces a multi-frame deconvolution framework that leverages spatiotemporal coherence to significantly improve microbubble localisation accuracy and super-resolution ultrasound imaging quality.

Contribution

The authors propose a novel multi-frame deconvolution approach with new regularisers, outperforming existing methods in microbubble localisation and super-resolution microvasculature imaging.

Findings

Improved localisation precision by up to 39%.

Enhanced super-resolution images with less noise and better contrast.

Outperformed existing deconvolution and cross-correlation methods.

Abstract

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.