Reconstruction for plane-wave ultrasound imaging using modified U-Net-based beamformer

Leang Sim Nguon, Jungwung Seo, Kangwon Seo, Yeji Han, Suhyun Park

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


An image reconstruction method that can simultaneously provide high image quality and frame rate is necessary for diagnosis on cardiovascular imaging but is challenging for plane-wave ultrasound imaging. To overcome this challenge, an end-to-end ultrasound image reconstruction method is proposed for reconstructing a high-resolution B-mode image from radio frequency (RF) data. A modified U-Net architecture that adopts EfficientNet-B5 and U-Net as the encoder and decoder parts, respectively, is proposed as a deep learning beamformer. The training data comprise pairs of pre-beamformed RF data generated from random scatterers with random amplitudes and corresponding high-resolution target data generated from coherent plane-wave compounding (CPWC). To evaluate the performance of the proposed beamforming model, simulation and experimental data are used for various beamformers, such as delay-and-sum (DAS), CPWC, and other deep learning beamformers, including U-Net and EfficientNet-B0. Compared with single plane-wave imaging with DAS, the proposed beamforming model reduces the lateral full width at half maximum by 35% for simulation and 29.6% for experimental data and improves the contrast-to-noise ratio and peak signal-to-noise ratio, respectively, by 6.3 and 9.97 dB for simulation, 2.38 and 3.01 dB for experimental data, and 3.18 and 1.03 dB for in vivo data. Furthermore, the computational complexity of the proposed beamforming model is four times less than that of the U-Net beamformer. The study results demonstrate that the proposed ultrasound image reconstruction method employing a deep learning beamformer, trained by the RF data from scatterers, can reconstruct a high-resolution image with a high frame rate for single plane-wave ultrasound imaging.

Original languageEnglish
Article number102073
JournalComputerized Medical Imaging and Graphics
StatePublished - Jun 2022

Bibliographical note

Funding Information:
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT under grant number NRF-2020R1A2C1011889 and the Ewha Womans University Research Grant of 2021.

Publisher Copyright:
© 2022 Elsevier Ltd


  • Beamforming
  • Coherent plane-wave compounding
  • Deep learning
  • Delay-and-sum
  • Plane-wave ultrasound imaging
  • Reconstruction


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