  Speed-of-sound is a biomechanical property for quantitative tissue
differentiation, with great potential as a new ultrasound-based image modality.
A conventional ultrasound array transducer can be used together with an
acoustic mirror, or so-called reflector, to reconstruct sound-speed images from
time-of-flight measurements to the reflector collected between transducer
element pairs, which constitutes a challenging problem of limited-angle
computed tomography. For this problem, we herein present a variational network
based image reconstruction architecture that is based on optimization loop
unrolling, and provide an efficient training protocol of this network
architecture on fully synthetic inclusion data. Our results indicate that the
learned model presents good generalization ability, being able to reconstruct
images with significantly different statistics compared to the training set.
Complex inclusion geometries were shown to be successfully reconstructed, also
improving over the prior-art by 23% in reconstruction error and by 10% in
contrast on synthetic data. In a phantom study, we demonstrated the detection
of multiple inclusions that were not distinguishable by prior-art
reconstruction, meanwhile improving the contrast by 27% for a stiff inclusion
and by 219% for a soft inclusion. Our reconstruction algorithm takes
approximately 10ms, enabling its use as a real-time imaging method on an
ultrasound machine, for which we are demonstrating an example preliminary setup
herein.
