Optshrink LR + S: accelerated fMRI reconstruction using non-convex optimal singular value shrinkage

This paper presents a new accelerated fMRI reconstruction method, namely, OptShrink LR + S method that reconstructs undersampled fMRI data using a linear combination of low-rank and sparse components. The low-rank component has been estimated using non-convex optimal singular value shrinkage algorit...

Descripción completa

Detalles Bibliográficos
Autores principales: Aggarwal, Priya, Shrivastava, Parth, Kabra, Tanay, Gupta, Anubha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5319953/
https://www.ncbi.nlm.nih.gov/pubmed/28074352
http://dx.doi.org/10.1007/s40708-016-0059-x
_version_ 1782509443414491136
author Aggarwal, Priya
Shrivastava, Parth
Kabra, Tanay
Gupta, Anubha
author_facet Aggarwal, Priya
Shrivastava, Parth
Kabra, Tanay
Gupta, Anubha
author_sort Aggarwal, Priya
collection PubMed
description This paper presents a new accelerated fMRI reconstruction method, namely, OptShrink LR + S method that reconstructs undersampled fMRI data using a linear combination of low-rank and sparse components. The low-rank component has been estimated using non-convex optimal singular value shrinkage algorithm, while the sparse component has been estimated using convex l (1) minimization. The performance of the proposed method is compared with the existing state-of-the-art algorithms on real fMRI dataset. The proposed OptShrink LR + S method yields good qualitative and quantitative results.
format Online
Article
Text
id pubmed-5319953
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher Springer Berlin Heidelberg
record_format MEDLINE/PubMed
spelling pubmed-53199532017-03-07 Optshrink LR + S: accelerated fMRI reconstruction using non-convex optimal singular value shrinkage Aggarwal, Priya Shrivastava, Parth Kabra, Tanay Gupta, Anubha Brain Inform Article This paper presents a new accelerated fMRI reconstruction method, namely, OptShrink LR + S method that reconstructs undersampled fMRI data using a linear combination of low-rank and sparse components. The low-rank component has been estimated using non-convex optimal singular value shrinkage algorithm, while the sparse component has been estimated using convex l (1) minimization. The performance of the proposed method is compared with the existing state-of-the-art algorithms on real fMRI dataset. The proposed OptShrink LR + S method yields good qualitative and quantitative results. Springer Berlin Heidelberg 2017-01-10 /pmc/articles/PMC5319953/ /pubmed/28074352 http://dx.doi.org/10.1007/s40708-016-0059-x Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Article
Aggarwal, Priya
Shrivastava, Parth
Kabra, Tanay
Gupta, Anubha
Optshrink LR + S: accelerated fMRI reconstruction using non-convex optimal singular value shrinkage
title Optshrink LR + S: accelerated fMRI reconstruction using non-convex optimal singular value shrinkage
title_full Optshrink LR + S: accelerated fMRI reconstruction using non-convex optimal singular value shrinkage
title_fullStr Optshrink LR + S: accelerated fMRI reconstruction using non-convex optimal singular value shrinkage
title_full_unstemmed Optshrink LR + S: accelerated fMRI reconstruction using non-convex optimal singular value shrinkage
title_short Optshrink LR + S: accelerated fMRI reconstruction using non-convex optimal singular value shrinkage
title_sort optshrink lr + s: accelerated fmri reconstruction using non-convex optimal singular value shrinkage
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5319953/
https://www.ncbi.nlm.nih.gov/pubmed/28074352
http://dx.doi.org/10.1007/s40708-016-0059-x
work_keys_str_mv AT aggarwalpriya optshrinklrsacceleratedfmrireconstructionusingnonconvexoptimalsingularvalueshrinkage
AT shrivastavaparth optshrinklrsacceleratedfmrireconstructionusingnonconvexoptimalsingularvalueshrinkage
AT kabratanay optshrinklrsacceleratedfmrireconstructionusingnonconvexoptimalsingularvalueshrinkage
AT guptaanubha optshrinklrsacceleratedfmrireconstructionusingnonconvexoptimalsingularvalueshrinkage

Ejemplares similares