Multi-Focus Ultrasound Imaging Using Generative Adversarial Networks

Ultrasound (US) beam can be focused at multiple locations to increase the lateral resolution of the resulting images. However, this improvement in resolution comes at the expense of a loss in frame rate, which is essential in many applications such as imaging moving anatomy. Herein, we propose a nov...

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Main Authors: Goudarzi, Sobhan, Asif, Amir, Rivaz, Hassan
Format: Conference Proceeding
Language:English
Subjects:
adversarial loss
focal point
frame rate
Gallium nitride
generative adversarial network
Generative adversarial networks
Generators
Phantoms
Training
Ultrasonic imaging
ultrasound imaging
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creator Goudarzi, Sobhan
Asif, Amir
Rivaz, Hassan
description Ultrasound (US) beam can be focused at multiple locations to increase the lateral resolution of the resulting images. However, this improvement in resolution comes at the expense of a loss in frame rate, which is essential in many applications such as imaging moving anatomy. Herein, we propose a novel method based on Generative Adversarial Network (GAN) for achieving multi-focus line-per-line US image without a reduction in the frame rate. Results on simulated phantoms as well as real phantom experiments show that the proposed deep learning framework is able to substantially improve the resolution without sacrificing the frame rate.
doi_str_mv 10.1109/ISBI.2019.8759216
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source IEEE Xplore All Conference Series
subjects adversarial loss
focal point
frame rate
Gallium nitride
generative adversarial network
Generative adversarial networks
Generators
Phantoms
Training
Ultrasonic imaging
ultrasound imaging
title Multi-Focus Ultrasound Imaging Using Generative Adversarial Networks
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