A Robust Deep Neural Network Approach for Ultrafast Ultrasound Imaging using Single Angle Plane Wave

Mohammad Wasih, Mohamed Almekkawy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Recently, deep learning-based methods have been proposed to reconstruct high-quality images from a single plane wave ultrasound data. A major problem with these methods is that they train the underlying network indiscriminately of the plane wave angle. This poses computational problems during training, as many plane waves at different angles must be mapped to a common ground-truth or reference image. To alleviate this problem, we propose a linear data transformation technique which reduces the intra-data variance among ultrasound Radio Frequency (RF) data at different angles. We further design a convolutional neural network, denoted by 'PWNet' which is trained using the transformed data to learn pixel weights for enhancing the image quality of the single plane wave delay and sum method. The results obtained on the experimental and simulated Plane-wave Imaging Challenge in Medical UltraSound data demonstrate the accuracy of our proposed method which would be beneficial for applications requiring high-quality images reconstructed at higher frame rates.

Original languageEnglish (US)
Title of host publicationIUS 2022 - IEEE International Ultrasonics Symposium
PublisherIEEE Computer Society
ISBN (Electronic)9781665466578
DOIs
StatePublished - 2022
Event2022 IEEE International Ultrasonics Symposium, IUS 2022 - Venice, Italy
Duration: Oct 10 2022Oct 13 2022

Publication series

NameIEEE International Ultrasonics Symposium, IUS
Volume2022-October
ISSN (Print)1948-5719
ISSN (Electronic)1948-5727

Conference

Conference2022 IEEE International Ultrasonics Symposium, IUS 2022
Country/TerritoryItaly
CityVenice
Period10/10/2210/13/22

All Science Journal Classification (ASJC) codes

  • Acoustics and Ultrasonics

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