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Deep Learning-Based Universal Beamformer for Ultrasound Imaging

  • Shujaat Khan
  • Jaeyoung Huh
  • Jong Chul YeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11768)

Abstract

In ultrasound (US) imaging, individual channel RF measurements are back-propagated and accumulated to form an image after applying specific delays. While this time reversal is usually implemented using a hardware- or software-based delay-and-sum (DAS) beamformer, the performance of DAS decreases rapidly in situations where data acquisition is not ideal. Herein, for the first time, we demonstrate that a single data-driven adaptive beamformer designed as a deep neural network can generate high quality images robustly for various detector channel configurations and subsampling rates. The proposed deep beamformer is evaluated for two distinct acquisition schemes: focused ultrasound imaging and planewave imaging. Experimental results showed that the proposed deep beamformer exhibit significant performance gain for both focused and planar imaging schemes, in terms of contrast-to-noise ratio and structural similarity.

Keywords

Ultrasound Adaptive beamforming Deep neural network 

Supplementary material

490279_1_En_69_MOESM1_ESM.pdf (2.1 mb)
Supplementary material 1 (pdf 2132 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Bio and Brain EngineeringKorea Advanced Institute of Science and Technology (KAIST)DaejeonKorea

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