Ultrafast Ultrasound Imaging as an Inverse Problem: Matrix-Free Sparse Image Reconstruction.
Détails
ID Serval
serval:BIB_E7B58839A7E1
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Ultrafast Ultrasound Imaging as an Inverse Problem: Matrix-Free Sparse Image Reconstruction.
Périodique
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
ISSN
1525-8955 (Electronic)
ISSN-L
0885-3010
Statut éditorial
Publié
Date de publication
03/2018
Peer-reviewed
Oui
Volume
65
Numéro
3
Pages
339-355
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: ppublish
Publication Status: ppublish
Résumé
Conventional ultrasound (US) image reconstruction methods rely on delay-and-sum (DAS) beamforming, which is a relatively poor solution to the image reconstruction problem. An alternative to DAS consists in using iterative techniques, which require both an accurate measurement model and a strong prior on the image under scrutiny. Toward this goal, much effort has been deployed in formulating models for US imaging, which usually require a large amount of memory to store the matrix coefficients. We present two different techniques, which take advantage of fast and matrix-free formulations derived for the measurement model and its adjoint, and rely on sparsity of US images in well-chosen models. Sparse regularization is used for enhanced image reconstruction. Compressed beamforming exploits the compressed sensing framework to restore high-quality images from fewer raw data than state-of-the-art approaches. Using simulated data and in vivo experimental acquisitions, we show that the proposed approach is three orders of magnitude faster than non-DAS state-of-the-art methods, with comparable or better image quality.
Pubmed
Web of science
Création de la notice
26/03/2018 17:47
Dernière modification de la notice
20/08/2019 17:10