Can I read 2020 IEEE International Ultrasonics Symposium (IUS) - Single-Shot CNN-Based Ultrasound Imaging with Sparse Linear Arrays on EtoBox?

2020 IEEE International Ultrasonics Symposium (IUS) - Single-Shot CNN-Based Ultrasound Imaging with Sparse Linear Arrays by Perdios, Dimitris; Vonlanthen, Manuel; Martinez, Florian; Arditi, Marcel; Thiran, Jean-Philippe is a scholarly article available to read on EtoBox.

What is 2020 IEEE International Ultrasonics Symposium (IUS) - Single-Shot CNN-Based Ultrasound Imaging with Sparse Linear Arrays about?

Sparse arrays are a topic of high interest within the ultrasound (US) imaging community, because of their promising ability to reduce costs, complexity, energy consumption, and data transfer requirements of US systems, thus addressing the main challenges of 3-D and portable 2-D systems. Undersampling a transducer array usually results in a significant increase in imaging artifacts, caused primarily by higher grating lobe (GL) levels. Thus, state-of-the-art sparse arrays design strategies focus on avoiding GLs, while compromising on the resulting image resolution and uniformity. In this work, we investigated the applicability of convolutional neural network (CNN)-based image reconstruction, having recently proven its potential in reducing GL artifacts, for reconstructing images from single unfocused acquisitions using uniformly undersampled linear array configurations on receive. The proposed reconstruction method consists of first computing a low-quality estimate from the undersampled single-shot acquisitions using a delay-and-sum (DAS) algorithm, followed by applying a real-time-capable CNN, trained specifically to reduce diffraction artifacts. Experiments were conducted within a

Author

Perdios, Dimitris; Vonlanthen, Manuel; Martinez, Florian; Arditi, Marcel; Thiran, Jean-Philippe

Publisher

IEEE

Published

2020

Language

EN