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A New Particle Swarm Optimization-Based Method for Phase Unwrapping of MRI Data

A new method based on discrete particle swarm optimization (dPSO) algorithm is proposed to solve the branch-cut phase unwrapping problem of MRI data. In this method, the optimal order of matching the positive residues with the negative residues is first identified by the dPSO algorithm, then the bra...

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Detalles Bibliográficos
Autores principales: He, Wei, Cheng, Yiyuan, Xia, Ling, Liu, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3467940/
https://www.ncbi.nlm.nih.gov/pubmed/23082088
http://dx.doi.org/10.1155/2012/475745
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author He, Wei
Cheng, Yiyuan
Xia, Ling
Liu, Feng
author_facet He, Wei
Cheng, Yiyuan
Xia, Ling
Liu, Feng
author_sort He, Wei
collection PubMed
description A new method based on discrete particle swarm optimization (dPSO) algorithm is proposed to solve the branch-cut phase unwrapping problem of MRI data. In this method, the optimal order of matching the positive residues with the negative residues is first identified by the dPSO algorithm, then the branch cuts are placed to join each pair of the opposite polarity residues, and in the last step phases are unwrapped by flood-fill algorithm. The performance of the proposed algorithm was tested on both simulated phase image and MRI wrapped phase data sets. The results demonstrated that, compared with conventionally used branch-cut phase unwrapping algorithms, the dPSO algorithm is rather robust and effective.
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spelling pubmed-34679402012-10-18 A New Particle Swarm Optimization-Based Method for Phase Unwrapping of MRI Data He, Wei Cheng, Yiyuan Xia, Ling Liu, Feng Comput Math Methods Med Research Article A new method based on discrete particle swarm optimization (dPSO) algorithm is proposed to solve the branch-cut phase unwrapping problem of MRI data. In this method, the optimal order of matching the positive residues with the negative residues is first identified by the dPSO algorithm, then the branch cuts are placed to join each pair of the opposite polarity residues, and in the last step phases are unwrapped by flood-fill algorithm. The performance of the proposed algorithm was tested on both simulated phase image and MRI wrapped phase data sets. The results demonstrated that, compared with conventionally used branch-cut phase unwrapping algorithms, the dPSO algorithm is rather robust and effective. Hindawi Publishing Corporation 2012 2012-10-02 /pmc/articles/PMC3467940/ /pubmed/23082088 http://dx.doi.org/10.1155/2012/475745 Text en Copyright © 2012 Wei He et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
He, Wei
Cheng, Yiyuan
Xia, Ling
Liu, Feng
A New Particle Swarm Optimization-Based Method for Phase Unwrapping of MRI Data
title A New Particle Swarm Optimization-Based Method for Phase Unwrapping of MRI Data
title_full A New Particle Swarm Optimization-Based Method for Phase Unwrapping of MRI Data
title_fullStr A New Particle Swarm Optimization-Based Method for Phase Unwrapping of MRI Data
title_full_unstemmed A New Particle Swarm Optimization-Based Method for Phase Unwrapping of MRI Data
title_short A New Particle Swarm Optimization-Based Method for Phase Unwrapping of MRI Data
title_sort new particle swarm optimization-based method for phase unwrapping of mri data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3467940/
https://www.ncbi.nlm.nih.gov/pubmed/23082088
http://dx.doi.org/10.1155/2012/475745
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