Online spatial normalization for real-time FMRI.

Real-time functional magnetic resonance imaging (rtfMRI) is a recently emerged technique that demands fast data processing within a single repetition time (TR), such as a TR of 2 seconds. Data preprocessing in rtfMRI has rarely involved spatial normalization, which can not be accomplished in a short...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Xiaofei Li, Li Yao, Qing Ye, Xiaojie Zhao
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7ba47efadb5845539289472894bdcb94
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:7ba47efadb5845539289472894bdcb94
record_format	dspace
spelling	oai:doaj.org-article:7ba47efadb5845539289472894bdcb942021-11-25T06:07:39ZOnline spatial normalization for real-time FMRI.1932-620310.1371/journal.pone.0103302https://doaj.org/article/7ba47efadb5845539289472894bdcb942014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25050799/?tool=EBIhttps://doaj.org/toc/1932-6203Real-time functional magnetic resonance imaging (rtfMRI) is a recently emerged technique that demands fast data processing within a single repetition time (TR), such as a TR of 2 seconds. Data preprocessing in rtfMRI has rarely involved spatial normalization, which can not be accomplished in a short time period. However, spatial normalization may be critical for accurate functional localization in a stereotactic space and is an essential procedure for some emerging applications of rtfMRI. In this study, we introduced an online spatial normalization method that adopts a novel affine registration (AFR) procedure based on principal axes registration (PA) and Gauss-Newton optimization (GN) using the self-adaptive β parameter, termed PA-GN(β) AFR and nonlinear registration (NLR) based on discrete cosine transform (DCT). In AFR, PA provides an appropriate initial estimate of GN to induce the rapid convergence of GN. In addition, the β parameter, which relies on the change rate of cost function, is employed to self-adaptively adjust the iteration step of GN. The accuracy and performance of PA-GN(β) AFR were confirmed using both simulation and real data and compared with the traditional AFR. The appropriate cutoff frequency of the DCT basis function in NLR was determined to balance the accuracy and calculation load of the online spatial normalization. Finally, the validity of the online spatial normalization method was further demonstrated by brain activation in the rtfMRI data.Xiaofei LiLi YaoQing YeXiaojie ZhaoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 7, p e103302 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xiaofei Li Li Yao Qing Ye Xiaojie Zhao Online spatial normalization for real-time FMRI.
description	Real-time functional magnetic resonance imaging (rtfMRI) is a recently emerged technique that demands fast data processing within a single repetition time (TR), such as a TR of 2 seconds. Data preprocessing in rtfMRI has rarely involved spatial normalization, which can not be accomplished in a short time period. However, spatial normalization may be critical for accurate functional localization in a stereotactic space and is an essential procedure for some emerging applications of rtfMRI. In this study, we introduced an online spatial normalization method that adopts a novel affine registration (AFR) procedure based on principal axes registration (PA) and Gauss-Newton optimization (GN) using the self-adaptive β parameter, termed PA-GN(β) AFR and nonlinear registration (NLR) based on discrete cosine transform (DCT). In AFR, PA provides an appropriate initial estimate of GN to induce the rapid convergence of GN. In addition, the β parameter, which relies on the change rate of cost function, is employed to self-adaptively adjust the iteration step of GN. The accuracy and performance of PA-GN(β) AFR were confirmed using both simulation and real data and compared with the traditional AFR. The appropriate cutoff frequency of the DCT basis function in NLR was determined to balance the accuracy and calculation load of the online spatial normalization. Finally, the validity of the online spatial normalization method was further demonstrated by brain activation in the rtfMRI data.
format	article
author	Xiaofei Li Li Yao Qing Ye Xiaojie Zhao
author_facet	Xiaofei Li Li Yao Qing Ye Xiaojie Zhao
author_sort	Xiaofei Li
title	Online spatial normalization for real-time FMRI.
title_short	Online spatial normalization for real-time FMRI.
title_full	Online spatial normalization for real-time FMRI.
title_fullStr	Online spatial normalization for real-time FMRI.
title_full_unstemmed	Online spatial normalization for real-time FMRI.
title_sort	online spatial normalization for real-time fmri.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/7ba47efadb5845539289472894bdcb94
work_keys_str_mv	AT xiaofeili onlinespatialnormalizationforrealtimefmri AT liyao onlinespatialnormalizationforrealtimefmri AT qingye onlinespatialnormalizationforrealtimefmri AT xiaojiezhao onlinespatialnormalizationforrealtimefmri
_version_	1718414171329003520

Online spatial normalization for real-time FMRI.

Ejemplares similares