Literature DB >> 28792894

High-Quality Plane Wave Compounding Using Convolutional Neural Networks.

Maxime Gasse, Fabien Millioz, Emmanuel Roux, Damien Garcia, Herve Liebgott, Denis Friboulet.   

Abstract

Single plane wave (PW) imaging produces ultrasound images of poor quality at high frame rates (ultrafast). High-quality PW imaging usually relies on the coherent compounding of several successive steered emissions (typically more than ten), which in turn results in a decreased frame rate. We propose a new strategy to reduce the number of emitted PWs by learning a compounding operation from data, i.e., by training a convolutional neural network to reconstruct high-quality images using a small number of transmissions. We present experimental evidence that this approach is promising, as we were able to produce high-quality images from only three PWs, competing in terms of contrast ratio and lateral resolution with the standard compounding of 31 PWs ( 10× speedup factor).

Year:  2017        PMID: 28792894     DOI: 10.1109/TUFFC.2017.2736890

Source DB:  PubMed          Journal:  IEEE Trans Ultrason Ferroelectr Freq Control        ISSN: 0885-3010            Impact factor:   2.725


  10 in total

1.  Nondestructive Detection of Targeted Microbubbles Using Dual-Mode Data and Deep Learning for Real-Time Ultrasound Molecular Imaging.

Authors:  Dongwoon Hyun; Lotfi Abou-Elkacem; Rakesh Bam; Leandra L Brickson; Carl D Herickhoff; Jeremy J Dahl
Journal:  IEEE Trans Med Imaging       Date:  2020-04-09       Impact factor: 10.048

2.  Deep Neural Networks for Ultrasound Beamforming.

Authors:  Adam C Luchies; Brett C Byram
Journal:  IEEE Trans Med Imaging       Date:  2018-02-26       Impact factor: 10.048

3.  Training Deep Network Ultrasound Beamformers With Unlabeled In Vivo Data.

Authors:  Jaime Tierney; Adam Luchies; Christopher Khan; Jennifer Baker; Daniel Brown; Brett Byram; Matthew Berger
Journal:  IEEE Trans Med Imaging       Date:  2021-12-30       Impact factor: 10.048

4.  Training improvements for ultrasound beamforming with deep neural networks.

Authors:  A C Luchies; B C Byram
Journal:  Phys Med Biol       Date:  2019-02-18       Impact factor: 4.174

5.  Evaluating Input Domain and Model Selection for Deep Network Ultrasound Beamforming.

Authors:  Jaime Tierney; Adam Luchies; Matthew Berger; Brett Byram
Journal:  IEEE Trans Ultrason Ferroelectr Freq Control       Date:  2021-06-29       Impact factor: 3.267

6.  Assessing the Robustness of Frequency-Domain Ultrasound Beamforming Using Deep Neural Networks.

Authors:  Adam C Luchies; Brett C Byram
Journal:  IEEE Trans Ultrason Ferroelectr Freq Control       Date:  2020-06-15       Impact factor: 3.267

7.  Deep learning-based reconstruction of ultrasound images from raw channel data.

Authors:  Hannah Strohm; Sven Rothlübbers; Klaus Eickel; Matthias Günther
Journal:  Int J Comput Assist Radiol Surg       Date:  2020-06-03       Impact factor: 2.924

8.  Estimation of Ultrasound Echogenicity Map from B-Mode Images Using Convolutional Neural Network.

Authors:  Che-Chou Shen; Jui-En Yang
Journal:  Sensors (Basel)       Date:  2020-08-31       Impact factor: 3.576

9.  Deep-fUS: A Deep Learning Platform for Functional Ultrasound Imaging of the Brain Using Sparse Data.

Authors:  Tommaso Di Ianni; Raag D Airan
Journal:  IEEE Trans Med Imaging       Date:  2022-06-30       Impact factor: 11.037

10.  Deep Learning to Obtain Simultaneous Image and Segmentation Outputs From a Single Input of Raw Ultrasound Channel Data.

Authors:  Arun Asokan Nair; Kendra N Washington; Trac D Tran; Austin Reiter; Muyinatu A Lediju Bell
Journal:  IEEE Trans Ultrason Ferroelectr Freq Control       Date:  2020-11-24       Impact factor: 2.725

  10 in total

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