Paper Info
Title
Image Reconstruction via Variational Network for Real-Time Hand-Held Sound-Speed 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.
Year
DOI
Venue
2018
10.1007/978-3-030-00129-2_14
Lecture Notes in Computer Science
Keywords
DocType
Volume
Deep learning,Speed-of-sound,Image reconstruction
Conference
11074
ISSN
Citations 
PageRank 
0302-9743
1
0.35
References 
Authors
4
3
Name
Order
Citations
PageRank
Valeriy Vishnevskiy1182.49
Sergio J. Sanabria283.05
Orçun Göksel3318.92