RF-ULM: Ultrasound Localization Microscopy Learned from Radio-Frequency Wavefronts

TL;DR

This paper introduces RF-ULM, a novel ultrasound localization microscopy method that leverages RF wavefront data with deep learning to improve localization accuracy and robustness, validated through extensive in vivo experiments.

Contribution

The study presents a new deep neural network approach for direct RF wavefront localization in ultrasound imaging, enhancing resolution and bridging the gap between synthetic and real data.

Findings

RF-ULM outperforms existing methods in localization accuracy

The approach effectively handles domain shifts between datasets

In vivo results demonstrate practical applicability

Abstract

In Ultrasound Localization Microscopy (ULM), achieving high-resolution images relies on the precise localization of contrast agent particles across a series of beamformed frames. However, our study uncovers an enormous potential: The process of delay-and-sum beamforming leads to an irreversible reduction of Radio-Frequency (RF) channel data, while its implications for localization remain largely unexplored. The rich contextual information embedded within RF wavefronts, including their hyperbolic shape and phase, offers great promise for guiding Deep Neural Networks (DNNs) in challenging localization scenarios. To fully exploit this data, we propose to directly localize scatterers in RF channel data. Our approach involves a custom super-resolution DNN using learned feature channel shuffling, non-maximum suppression, and a semi-global convolutional block for reliable and accurate wavefront localization. Additionally, we introduce a geometric point transformation that facilitates seamless mapping to the B-mode coordinate space. To understand the impact of beamforming on ULM, we validate the effectiveness of our method by conducting an extensive comparison with State-Of-The-Art (SOTA) techniques. We present the inaugural in vivo results from a wavefront-localizing DNN, highlighting its real-world practicality. Our findings show that RF-ULM bridges the domain shift between synthetic and real datasets, offering a considerable advantage in terms of precision and complexity. To enable the broader research community to benefit from our findings, our code and the associated SOTA methods are made available at https://github.com/hahnec/rf-ulm.