Goal: 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) signals acquired through the monostatic SA approach. Methods : A U-net was trained using 27200 pairs of RF signals, simulated considering a monostatic SA architecture, with their corresponding delay-and-sum beamformed target images in a multistatic 128-element SA configuration. The contrast was assessed on 500 simulated test images of anechoic/hyperechoic targets. The DNN's performance in reconstructing experimental images of a phantom and different in vivo scenarios was tested too. Results : The DNN, compared to the simple monostatic SA approach used to acquire pre-beamforming signals, generated better-quality images with higher contrast and reduced noise/artifacts. Conclusions : The obtained results suggest the potential for the development of a single-channel setup, simultaneously providing good-quality images and reducing hardware complexity.

A Deep Learning Approach for Beamforming and Contrast Enhancement of Ultrasound Images in Monostatic Synthetic Aperture Imaging: A Proof-of-Concept / Bosco, Edoardo; Spairani, Edoardo; Toffali, Eleonora; Meacci, Valentino; Ramalli, Alessandro; Matrone, Giulia. - In: IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY. - ISSN 2644-1276. - ELETTRONICO. - 5:(2024), pp. 376-382. [10.1109/ojemb.2024.3401098]

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

Meacci, Valentino;Ramalli, Alessandro;Matrone, Giulia
2024

Abstract

Goal: 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) signals acquired through the monostatic SA approach. Methods : A U-net was trained using 27200 pairs of RF signals, simulated considering a monostatic SA architecture, with their corresponding delay-and-sum beamformed target images in a multistatic 128-element SA configuration. The contrast was assessed on 500 simulated test images of anechoic/hyperechoic targets. The DNN's performance in reconstructing experimental images of a phantom and different in vivo scenarios was tested too. Results : The DNN, compared to the simple monostatic SA approach used to acquire pre-beamforming signals, generated better-quality images with higher contrast and reduced noise/artifacts. Conclusions : The obtained results suggest the potential for the development of a single-channel setup, simultaneously providing good-quality images and reducing hardware complexity.
2024
5
376
382
Bosco, Edoardo; Spairani, Edoardo; Toffali, Eleonora; Meacci, Valentino; Ramalli, Alessandro; Matrone, Giulia
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1363232
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