Literature DB >> 23097342

Generalized INverse imaging (GIN): ultrafast fMRI with physiological noise correction.

Rasim Boyacioğlu1, Markus Barth.   

Abstract

An ultrafast functional magnetic resonance imaging (fMRI) technique, called generalized inverse imaging (GIN), is proposed, which combines inverse imaging with a phase constraint-leading to a less underdetermined reconstruction-and physiological noise correction. A single 3D echo planar imaging (EPI) prescan is sufficient to obtain the necessary coil sensitivity information and reference images that are used to reconstruct standard images, so that standard analysis methods are applicable. A moving dots stimulus paradigm was chosen to assess the performance of GIN. We find that the spatial localization of activation for GIN is comparable to an EPI protocol and that maximum z-scores increase significantly. The high temporal resolution of GIN (50 ms) and the acquisition of the phase information enable unaliased sampling and regression of physiological signals. Using the phase time courses obtained from the 32 channels of the receiver coils as nuisance regressors in a general linear model results in significant improvement of the functional activation, rendering the acquisition of external physiological signals unnecessary. The proposed physiological noise correction can in principle be used for other fMRI protocols, such as simultaneous multislice acquisitions, which acquire the phase information sufficiently fast and sample physiological signals unaliased.
Copyright © 2012 Wiley Periodicals, Inc.

Keywords:  3 Tesla; BOLD; GIN; brain; neuroimaging; regression; ultrafast fMRI

Mesh:

Year:  2012        PMID: 23097342     DOI: 10.1002/mrm.24528

Source DB:  PubMed          Journal:  Magn Reson Med        ISSN: 0740-3194            Impact factor:   4.668


  16 in total

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Authors:  Alexander Geißler; Florian Ph S Fischmeister; Günther Grabner; Moritz Wurnig; Jakob Rath; Thomas Foki; Eva Matt; Siegfried Trattnig; Roland Beisteiner; Simon Daniel Robinson
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7.  An Investigation of RSN Frequency Spectra Using Ultra-Fast Generalized Inverse Imaging.

Authors:  Rasim Boyacioglu; Christian F Beckmann; Markus Barth
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10.  Increasing fMRI sampling rate improves Granger causality estimates.

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Journal:  PLoS One       Date:  2014-06-26       Impact factor: 3.240

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