  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.
