Super-Resolved Microbubble Localization in Single-Channel Ultrasound RF Signals Using Deep Learning

TL;DR

This paper introduces a deep learning-based method for super-resolution ultrasound imaging that effectively localizes microbubbles from single-channel RF signals, enabling high-resolution deep tissue imaging with reduced acquisition times.

Contribution

It presents a novel CNN-based deconvolution approach with a dual-loss function for microbubble localization in single-channel RF signals, improving super-resolution ultrasound imaging.

Findings

Achieves 0.90 precision and recall at 4% wavelength localization tolerance.

Demonstrates an order-of-magnitude axial resolution improvement.

Effective in high microbubble density and deep imaging scenarios.

Abstract

Recently, super-resolution ultrasound imaging with ultrasound localization microscopy (ULM) has received much attention. However, ULM relies on low concentrations of microbubbles in the blood vessels, ultimately resulting in long acquisition times. Here, we present an alternative super-resolution approach, based on direct deconvolution of single-channel ultrasound radio-frequency (RF) signals with a one-dimensional dilated convolutional neural network (CNN). This work focuses on low-frequency ultrasound (1.7 MHz) for deep imaging (10 cm) of a dense cloud of monodisperse microbubbles (up to 1000 microbubbles in the measurement volume, corresponding to an average echo overlap of 94%). Data are generated with a simulator that uses a large range of acoustic pressures (5-250 kPa) and captures the full, nonlinear response of resonant, lipid-coated microbubbles. The network is trained with a novel dual-loss function, which features elements of both a classification loss and a regression loss and improves the detection-localization characteristics of the output. Whereas imposing a localization tolerance of 0 yields poor detection metrics, imposing a localization tolerance corresponding to 4% of the wavelength yields a precision and recall of both 0.90. Furthermore, the detection improves with increasing acoustic pressure and deteriorates with increasing microbubble density. The potential of the presented approach to super-resolution ultrasound imaging is demonstrated with a delay-and-sum reconstruction with deconvolved element data. The resulting image shows an order-of-magnitude gain in axial resolution compared to a delay-and-sum reconstruction with unprocessed element data.