Image Reconstruction via Variational Network for Real-Time Hand-Held Sound-Speed Imaging

TL;DR

This paper introduces a variational network-based method for real-time sound-speed imaging using ultrasound, demonstrating improved accuracy and contrast in synthetic and phantom experiments, with potential for clinical application.

Contribution

The paper presents a novel variational network architecture for sound-speed image reconstruction that generalizes well and operates in real-time, outperforming prior methods.

Findings

Achieved 23% lower reconstruction error compared to prior art.

Improved contrast by 27% for stiff and 219% for soft inclusions.

Reconstruction time of approximately 10ms enables real-time imaging.

Abstract

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.