High Resolution Plane Wave Compounding Through Deep Proximal Learning

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

Abstract

Ultra-fast ultrasound imaging relies on coherent Plane Wave (PW) compounding to obtain sufficient spatial resolution, and contrast. However, the process of coherent PW compounding incurs a loss in temporal resolution. We propose a Deep Learning (DL) network that achieves high resolution PW compounding using a reduced number of PW transmits. We embed a model based signal processing algorithm in the design of the network, which leads to better performance through the exploitation of the prior information that is now available to the network. Our proposed method outperforms two benchmark networks, yielding approximately an 8.2% improvement in PSNR, over the next best network. Aiming for an additional boost in resolution, we moreover train towards images acquired using higher transmit frequencies.
Original languageEnglish
Title of host publication2020 IEEE International Ultrasonics Symposium (IUS)
PublisherInstitute of Electrical and Electronics Engineers
Number of pages4
ISBN (Electronic)978-1-7281-5448-0
ISBN (Print)978-1-7281-5449-7
DOIs
Publication statusPublished - 2020
Event2020 IEEE International Ultrasonics Symposium, IUS 2020 - Las Vegas, United States
Duration: 7 Sep 202011 Sep 2020

Conference

Conference2020 IEEE International Ultrasonics Symposium, IUS 2020
Country/TerritoryUnited States
CityLas Vegas
Period7/09/2011/09/20

Keywords

  • Machine Learning
  • Plane Wave Compounding
  • Signal Processing
  • Ultrasound

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