Monotone FISTA With Variable Acceleration for Compressed Sensing Magnetic Resonance Imaging
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/TCI.2018.2882681 http://hdl.handle.net/11449/184355 |
Resumo: | An improvement of the monotone fast iterative shrinkage-thresholding algorithm (MFISTA) for faster convergence is proposed in this paper. Our motivation is to reduce the reconstruction time of compressed sensing problems in magnetic resonance imaging. The proposed modification introduces an extra term, which is a multiple of the proximal-gradient step, into the so-called momentum formula used for the computation of the next iterate in MFISTA. In addition, the modified algorithm selects the next iterate as a possibly improved point obtained by any other procedure, such as an arbitrary shift, a line search, or other methods. As an example, an arbitrary-length shift in the direction from the previous iterate to the output of the proximal-gradient step is considered. The resulting algorithm accelerates MFISTA in a manner that varies with the iterative steps. Convergence analysis shows that the proposed modification provides improved theoretical convergence bounds, and that it has more flexibility in its parameters than the original MFISTA. Since such problems need to he studied in the context of functions of several complex variables, a careful extension of FISTA-like methods to complex variables is provided. |
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Repositório Institucional da UNESP |
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Monotone FISTA With Variable Acceleration for Compressed Sensing Magnetic Resonance ImagingProximal-gradient methodsFISTAcompressed sensingmagnetic resonance imagingiterative algorithmsAn improvement of the monotone fast iterative shrinkage-thresholding algorithm (MFISTA) for faster convergence is proposed in this paper. Our motivation is to reduce the reconstruction time of compressed sensing problems in magnetic resonance imaging. The proposed modification introduces an extra term, which is a multiple of the proximal-gradient step, into the so-called momentum formula used for the computation of the next iterate in MFISTA. In addition, the modified algorithm selects the next iterate as a possibly improved point obtained by any other procedure, such as an arbitrary shift, a line search, or other methods. As an example, an arbitrary-length shift in the direction from the previous iterate to the output of the proximal-gradient step is considered. The resulting algorithm accelerates MFISTA in a manner that varies with the iterative steps. Convergence analysis shows that the proposed modification provides improved theoretical convergence bounds, and that it has more flexibility in its parameters than the original MFISTA. Since such problems need to he studied in the context of functions of several complex variables, a careful extension of FISTA-like methods to complex variables is provided.NIHCenter of Advanced Imaging Innovation and Research (CAI2R)NIBIB Biomedical Technology Resource CenterFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)New York Univ, Sch Med, New York, NY 10016 USAState Univ Sao Paulo, BR-01049010 Sao Paulo, BrazilCUNY, New York, NY 10017 USAState Univ Sao Paulo, BR-01049010 Sao Paulo, BrazilNIH: R01-AR060238NIH: R01-AR067156NIH: R01-AR068966NIBIB Biomedical Technology Resource Center: NIH P41-EB017183FAPESP: 2013/07375-0FAPESP: 2016/24286-9Ieee-inst Electrical Electronics Engineers IncNew York UnivUniversidade Estadual Paulista (Unesp)CUNYZibetti, Marcelo Victor WustHelou, Elias Salomao [UNESP]Regatte, Ravinder R.Herman, Gabor T.2019-10-04T11:56:54Z2019-10-04T11:56:54Z2019-03-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article109-119http://dx.doi.org/10.1109/TCI.2018.2882681Ieee Transactions On Computational Imaging. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 5, n. 1, p. 109-119, 2019.2333-9403http://hdl.handle.net/11449/18435510.1109/TCI.2018.2882681WOS:000458778600009Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIeee Transactions On Computational Imaginginfo:eu-repo/semantics/openAccess2021-10-23T17:51:59Zoai:repositorio.unesp.br:11449/184355Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T17:51:59Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Monotone FISTA With Variable Acceleration for Compressed Sensing Magnetic Resonance Imaging |
title |
Monotone FISTA With Variable Acceleration for Compressed Sensing Magnetic Resonance Imaging |
spellingShingle |
Monotone FISTA With Variable Acceleration for Compressed Sensing Magnetic Resonance Imaging Zibetti, Marcelo Victor Wust Proximal-gradient methods FISTA compressed sensing magnetic resonance imaging iterative algorithms |
title_short |
Monotone FISTA With Variable Acceleration for Compressed Sensing Magnetic Resonance Imaging |
title_full |
Monotone FISTA With Variable Acceleration for Compressed Sensing Magnetic Resonance Imaging |
title_fullStr |
Monotone FISTA With Variable Acceleration for Compressed Sensing Magnetic Resonance Imaging |
title_full_unstemmed |
Monotone FISTA With Variable Acceleration for Compressed Sensing Magnetic Resonance Imaging |
title_sort |
Monotone FISTA With Variable Acceleration for Compressed Sensing Magnetic Resonance Imaging |
author |
Zibetti, Marcelo Victor Wust |
author_facet |
Zibetti, Marcelo Victor Wust Helou, Elias Salomao [UNESP] Regatte, Ravinder R. Herman, Gabor T. |
author_role |
author |
author2 |
Helou, Elias Salomao [UNESP] Regatte, Ravinder R. Herman, Gabor T. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
New York Univ Universidade Estadual Paulista (Unesp) CUNY |
dc.contributor.author.fl_str_mv |
Zibetti, Marcelo Victor Wust Helou, Elias Salomao [UNESP] Regatte, Ravinder R. Herman, Gabor T. |
dc.subject.por.fl_str_mv |
Proximal-gradient methods FISTA compressed sensing magnetic resonance imaging iterative algorithms |
topic |
Proximal-gradient methods FISTA compressed sensing magnetic resonance imaging iterative algorithms |
description |
An improvement of the monotone fast iterative shrinkage-thresholding algorithm (MFISTA) for faster convergence is proposed in this paper. Our motivation is to reduce the reconstruction time of compressed sensing problems in magnetic resonance imaging. The proposed modification introduces an extra term, which is a multiple of the proximal-gradient step, into the so-called momentum formula used for the computation of the next iterate in MFISTA. In addition, the modified algorithm selects the next iterate as a possibly improved point obtained by any other procedure, such as an arbitrary shift, a line search, or other methods. As an example, an arbitrary-length shift in the direction from the previous iterate to the output of the proximal-gradient step is considered. The resulting algorithm accelerates MFISTA in a manner that varies with the iterative steps. Convergence analysis shows that the proposed modification provides improved theoretical convergence bounds, and that it has more flexibility in its parameters than the original MFISTA. Since such problems need to he studied in the context of functions of several complex variables, a careful extension of FISTA-like methods to complex variables is provided. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10-04T11:56:54Z 2019-10-04T11:56:54Z 2019-03-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/TCI.2018.2882681 Ieee Transactions On Computational Imaging. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 5, n. 1, p. 109-119, 2019. 2333-9403 http://hdl.handle.net/11449/184355 10.1109/TCI.2018.2882681 WOS:000458778600009 |
url |
http://dx.doi.org/10.1109/TCI.2018.2882681 http://hdl.handle.net/11449/184355 |
identifier_str_mv |
Ieee Transactions On Computational Imaging. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 5, n. 1, p. 109-119, 2019. 2333-9403 10.1109/TCI.2018.2882681 WOS:000458778600009 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ieee Transactions On Computational Imaging |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
109-119 |
dc.publisher.none.fl_str_mv |
Ieee-inst Electrical Electronics Engineers Inc |
publisher.none.fl_str_mv |
Ieee-inst Electrical Electronics Engineers Inc |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1799964850705661952 |