Envelope based nonlinear blind deconvolution approach for ultrasound imaging

The resolution of ultrasound medical images is yet an important problem despite of the researchers efforts. In this paper we presents a nonlinear blind deconvolution to eliminate the blurring effect based on the measured radio-frequency signal envelope. This algorithm is executed in two steps. Firsl...

Full description

Bibliographic Details
Main Authors: L.T. Chira, J.M. Girault, C. Rusu
Format: Article
Language:English
Published: UT Press Publishing House 2012-06-01
Series:Carpathian Journal of Electronic and Computer Engineering
Subjects:
Online Access:http://cjece.ubm.ro/vol/5-2012/8_Chira%20.pdf
id doaj-31a9beeae98545b7937d8afa4e0c8fb4
record_format Article
spelling doaj-31a9beeae98545b7937d8afa4e0c8fb42020-11-24T22:37:21ZengUT Press Publishing HouseCarpathian Journal of Electronic and Computer Engineering1844-96892343-89082012-06-01513943 Envelope based nonlinear blind deconvolution approach for ultrasound imagingL.T. Chira0J.M. Girault1C. Rusu2Faculty of Electronics, Telecommunications and Information Theory, Technical University of Cluj Napoca, Cluj Napoca, RomaniaUMRS “Imagerie et Ceveau” INSERM U930, Francois Rabelais University of Tours, Tours, FranceFaculty of Electronics, Telecommunications and Information Theory, Technical University of Cluj Napoca, Cluj Napoca, RomaniaThe resolution of ultrasound medical images is yet an important problem despite of the researchers efforts. In this paper we presents a nonlinear blind deconvolution to eliminate the blurring effect based on the measured radio-frequency signal envelope. This algorithm is executed in two steps. Firslty we make an estimation for Point Spread Function (PSF) and, secondly we use the estimated PSF to remove, iteratively their effect. The proposed algorithm is a greedy algorithm, called also matching pursuit or CLEAN. The use of this algorithm is motivated beacause theorically it avoid the so called inverse problem, which usually needs regularization to obtain an optimal solution. The results are presented using 1D simulated signals in term of visual evaluation and nMSE in comparison with the two most kwown regularisation solution methods for least square problem, Thikonov regularization or l2-norm and Total Variation or l1 norm.http://cjece.ubm.ro/vol/5-2012/8_Chira%20.pdfultrasoundresolution improvementnonlinear blind deconvolutionmatching pursuit
collection DOAJ
language English
format Article
sources DOAJ
author L.T. Chira
J.M. Girault
C. Rusu
spellingShingle L.T. Chira
J.M. Girault
C. Rusu
Envelope based nonlinear blind deconvolution approach for ultrasound imaging
Carpathian Journal of Electronic and Computer Engineering
ultrasound
resolution improvement
nonlinear blind deconvolution
matching pursuit
author_facet L.T. Chira
J.M. Girault
C. Rusu
author_sort L.T. Chira
title Envelope based nonlinear blind deconvolution approach for ultrasound imaging
title_short Envelope based nonlinear blind deconvolution approach for ultrasound imaging
title_full Envelope based nonlinear blind deconvolution approach for ultrasound imaging
title_fullStr Envelope based nonlinear blind deconvolution approach for ultrasound imaging
title_full_unstemmed Envelope based nonlinear blind deconvolution approach for ultrasound imaging
title_sort envelope based nonlinear blind deconvolution approach for ultrasound imaging
publisher UT Press Publishing House
series Carpathian Journal of Electronic and Computer Engineering
issn 1844-9689
2343-8908
publishDate 2012-06-01
description The resolution of ultrasound medical images is yet an important problem despite of the researchers efforts. In this paper we presents a nonlinear blind deconvolution to eliminate the blurring effect based on the measured radio-frequency signal envelope. This algorithm is executed in two steps. Firslty we make an estimation for Point Spread Function (PSF) and, secondly we use the estimated PSF to remove, iteratively their effect. The proposed algorithm is a greedy algorithm, called also matching pursuit or CLEAN. The use of this algorithm is motivated beacause theorically it avoid the so called inverse problem, which usually needs regularization to obtain an optimal solution. The results are presented using 1D simulated signals in term of visual evaluation and nMSE in comparison with the two most kwown regularisation solution methods for least square problem, Thikonov regularization or l2-norm and Total Variation or l1 norm.
topic ultrasound
resolution improvement
nonlinear blind deconvolution
matching pursuit
url http://cjece.ubm.ro/vol/5-2012/8_Chira%20.pdf
work_keys_str_mv AT ltchira envelopebasednonlinearblinddeconvolutionapproachforultrasoundimaging
AT jmgirault envelopebasednonlinearblinddeconvolutionapproachforultrasoundimaging
AT crusu envelopebasednonlinearblinddeconvolutionapproachforultrasoundimaging
_version_ 1725717520254500864