A Deep Learning Approach for Beamforming and Contrast Enhancement of Ultrasound Images in Monostatic Synthetic Aperture Imaging: A Proof-of-Concept

A Deep Learning Approach for Beamforming and Contrast Enhancement of Ultrasound Images in Monostatic Synthetic Aperture Imaging: A Proof-of-Concept

<italic>Goal:</italic> In this study, we demonstrate that a deep neural network (DNN) can be trained to reconstruct high-contrast images, resembling those produced by the multistatic Synthetic Aperture (SA) method using a 128-element array, leveraging pre-beamforming radiofrequency (RF)...

Full description

Saved in:
Bibliographic Details
Main Authors: Edoardo Bosco, Edoardo Spairani, Eleonora Toffali, Valentino Meacci, Alessandro Ramalli, Giulia Matrone
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Open Journal of Engineering in Medicine and Biology
Subjects:
Deep learning
monostatic configuration
synthetic aperture imaging
ULA-OP system
ultrasound beamforming
Online Access:https://ieeexplore.ieee.org/document/10531023/
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://ieeexplore.ieee.org/document/10531023/

Similar Items