Literature DB >> 22921734

Signal fluctuations in fMRI data acquired with 2D-EPI and 3D-EPI at 7 Tesla.

João Jorge1, Patrícia Figueiredo, Wietske van der Zwaag, José P Marques.   

Abstract

Segmented three-dimensional echo planar imaging (3D-EPI) provides higher image signal-to-noise ratio (SNR) than standard single-shot two-dimensional echo planar imaging (2D-EPI), but is more sensitive to physiological noise. The aim of this study was to compare physiological noise removal efficiency in single-shot 2D-EPI and segmented 3D-EPI acquired at 7 Tesla. Two approaches were investigated based either on physiological regressors (PR) derived from cardiac and respiratory phases, or on principal component analysis (PCA) using additional resting-state data. Results show that, prior to physiological noise removal, 2D-EPI data had higher temporal SNR (tSNR), while spatial SNR was higher in 3D-EPI. Blood oxygen level dependent (BOLD) sensitivity was similar for both methods. The PR-based approach allowed characterization of relative contributions from different noise sources, confirming significant increases in physiological noise from 2D to 3D prior to correction. Both physiological noise removal approaches produced significant increases in tSNR and BOLD sensitivity, and these increases were larger for 3D-EPI, resulting in higher BOLD sensitivity in the 3D-EPI than in the 2D-EPI data. The PCA-based approach was the most effective correction method, yielding higher tSNR values for 3D-EPI than for 2D-EPI postcorrection.
Copyright © 2013 Elsevier Inc. All rights reserved.

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Year:  2012        PMID: 22921734     DOI: 10.1016/j.mri.2012.07.001

Source DB:  PubMed          Journal:  Magn Reson Imaging        ISSN: 0730-725X            Impact factor:   2.546


  16 in total

1.  Physiological noise in human cerebellar fMRI.

Authors:  Wietske van der Zwaag; João Jorge; Denis Butticaz; Rolf Gruetter
Journal:  MAGMA       Date:  2015-04-18       Impact factor: 2.310

2.  Evaluation of different cerebrospinal fluid and white matter fMRI filtering strategies-Quantifying noise removal and neural signal preservation.

Authors:  Marek Bartoň; Radek Mareček; Lenka Krajčovičová; Tomáš Slavíček; Tomáš Kašpárek; Petra Zemánková; Pavel Říha; Michal Mikl
Journal:  Hum Brain Mapp       Date:  2018-11-07       Impact factor: 5.038

3.  Mapping and characterization of positive and negative BOLD responses to visual stimulation in multiple brain regions at 7T.

Authors:  João Jorge; Patrícia Figueiredo; Rolf Gruetter; Wietske van der Zwaag
Journal:  Hum Brain Mapp       Date:  2018-02-20       Impact factor: 5.038

4.  A robust deep neural network for denoising task-based fMRI data: An application to working memory and episodic memory.

Authors:  Zhengshi Yang; Xiaowei Zhuang; Karthik Sreenivasan; Virendra Mishra; Tim Curran; Dietmar Cordes
Journal:  Med Image Anal       Date:  2019-11-26       Impact factor: 8.545

5.  Techniques for blood volume fMRI with VASO: From low-resolution mapping towards sub-millimeter layer-dependent applications.

Authors:  Laurentius Huber; Dimo Ivanov; Daniel A Handwerker; Sean Marrett; Maria Guidi; Kâmil Uludağ; Peter A Bandettini; Benedikt A Poser
Journal:  Neuroimage       Date:  2016-11-18       Impact factor: 6.556

6.  Improved 7 Tesla resting-state fMRI connectivity measurements by cluster-based modeling of respiratory volume and heart rate effects.

Authors:  Joana Pinto; Sandro Nunes; Marta Bianciardi; Afonso Dias; L Miguel Silveira; Lawrence L Wald; Patrícia Figueiredo
Journal:  Neuroimage       Date:  2017-04-06       Impact factor: 6.556

Review 7.  Methods for cleaning the BOLD fMRI signal.

Authors:  César Caballero-Gaudes; Richard C Reynolds
Journal:  Neuroimage       Date:  2016-12-09       Impact factor: 6.556

Review 8.  New acquisition techniques and their prospects for the achievable resolution of fMRI.

Authors:  Saskia Bollmann; Markus Barth
Journal:  Prog Neurobiol       Date:  2020-10-23       Impact factor: 11.685

9.  A study of the electro-haemodynamic coupling using simultaneously acquired intracranial EEG and fMRI data in humans.

Authors:  T Murta; L Hu; T M Tierney; U J Chaudhary; M C Walker; D W Carmichael; P Figueiredo; L Lemieux
Journal:  Neuroimage       Date:  2016-08-03       Impact factor: 6.556

10.  Optimizing RetroICor and RetroKCor corrections for multi-shot 3D FMRI acquisitions.

Authors:  Rob H N Tijssen; Mark Jenkinson; Jonathan C W Brooks; Peter Jezzard; Karla L Miller
Journal:  Neuroimage       Date:  2013-09-07       Impact factor: 6.556

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