Ultrasound Image Deconvolution Using Fundamental and Harmonic Images

Ultrasound (US) image restoration from radio frequency (RF) signals is generally addressed by deconvolution techniques mitigating the effect of the system point spread function (PSF). Most of the existing methods estimate the tissue reflectivity function (TRF) from the so-called fundamental US image...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on ultrasonics, ferroelectrics, and frequency control ferroelectrics, and frequency control, 2021-04, Vol.68 (4), p.993-1006
Main Authors: Hourani, Mohamad, Basarab, Adrian, Kouame, Denis, Tourneret, Jean-Yves
Format: Article
Language:English
Subjects:
Alternating direction method of multipliers (ADMM)
Artificial Intelligence
Attenuation
blind deconvolution
Computer Science
Contrast agents
Cost function
Deconvolution
Echoes
Estimation
Functional Analysis
Harmonic analysis
harmonic ultrasonic imaging
Human tissues
Image restoration
Imaging
In vivo methods and tests
Mathematical model
Mathematics
Medical Imaging
Nonlinearity
optimization
Point spread functions
Radio frequency
Radio signals
Regularization
Signal and Image Processing
tissue reflectivity restoration
Ultrasonic imaging
Ultrasound
Wave propagation
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Ultrasound (US) image restoration from radio frequency (RF) signals is generally addressed by deconvolution techniques mitigating the effect of the system point spread function (PSF). Most of the existing methods estimate the tissue reflectivity function (TRF) from the so-called fundamental US images, based on an image model assuming the linear US wave propagation. However, several human tissues or tissues with contrast agents have a nonlinear behavior when interacting with US waves leading to harmonic images. This work takes this nonlinearity into account in the context of TRF restoration, by considering both fundamental and harmonic RF signals. Starting from two observation models (for the fundamental and harmonic images), TRF estimation is expressed as the minimization of a cost function defined as the sum of two data fidelity terms and one sparsity-based regularization stabilizing the solution. The high attenuation with a depth of harmonic echoes is integrated into the direct model that relates the observed harmonic image to the TRF. The interest of the proposed method is shown through synthetic and in vivo results and compared with other restoration methods.
ISSN:0885-3010
1525-8955
DOI:10.1109/TUFFC.2020.3028166