Modified Residual Dense Network Based Super-Resolution Localization Method for High-Concentration Microbubbles

The practical limitation of ultrasound localization microscopy for clinical translation is the trade-off between microbubble concentration and data acquisition time. Recently, deep learning-based approaches have shown promising capability in microbubble localization accuracy when using a high-concentration microbubble injection to shorten acquisition time. In this study, we construct a Modified Residual Dense Network (MRDN) for high-concentration microbubble super-resolution localization. By subjecting the collected data to non-local mean filtering operations, the MRDN is used for continuous learning. This method can be well used at high concentrations of 16 $\mathbf{mm}^{\boldsymbol{-2}}$ with a high localization accuracy (localization error $\boldsymbol{:22.3\mu} \mathbf{m}$ ) and high localization reliability (Jaccard index:0.78).