Literature DB >> 27845256

Prospective motion correction in functional MRI.

Maxim Zaitsev1, Burak Akin2, Pierre LeVan2, Benjamin R Knowles2.   

Abstract

Due to the intrinsic low sensitivity of BOLD-fMRI long scanning is required. Subject motion during fMRI scans reduces statistical significance of the activation maps and increases the prevalence of false activations. Motion correction is therefore an essential tool for a successful fMRI data analysis. Retrospective motion correction techniques are now commonplace and are incorporated into a wide range of fMRI analysis toolboxes. These techniques are advantageous due to robustness, sequence independence and have minimal impact on the fMRI study setup. Retrospective techniques however, do not provide an accurate intra-volume correction, nor can these techniques correct for the spin-history effects. The application of prospective motion correction in fMRI appears to be effective in reducing false positives and increasing sensitivity when compared to retrospective techniques, particularly in the cases of substantial motion. Especially advantageous in this regard is the combination of prospective motion correction with dynamic distortion correction. Nevertheless, none of the recent methods are able to recover activations in presence of motion that are comparable to no-motion conditions, which motivates further research in the area of adaptive dynamic imaging.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Adaptive dynamic MRI; Function MRI; Motion correction; Prospective motion correction; Real-time motion correction; fMRI

Mesh:

Year:  2016        PMID: 27845256      PMCID: PMC5427003          DOI: 10.1016/j.neuroimage.2016.11.014

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  96 in total

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2.  Image reconstruction using a gradient impulse response model for trajectory prediction.

Authors:  S Johanna Vannesjo; Nadine N Graedel; Lars Kasper; Simon Gross; Julia Busch; Maximilian Haeberlin; Christoph Barmet; Klaas P Pruessmann
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3.  Correction of B 0-induced geometric distortion variations in prospective motion correction for 7T MRI.

Authors:  Uten Yarach; Chaiya Luengviriya; Daniel Stucht; Frank Godenschweger; Peter Schulze; Oliver Speck
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4.  Prospective correction of affine motion for arbitrary MR sequences on a clinical scanner.

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5.  Analysis of functional image analysis contest (FIAC) data with brainvoyager QX: From single-subject to cortically aligned group general linear model analysis and self-organizing group independent component analysis.

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6.  Spin saturation artifact correction using slice-to-volume registration motion estimates for fMRI time series.

Authors:  Roshni Bhagalia; Boklye Kim
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7.  A prospective approach to correct for inter-image head rotation in fMRI.

Authors:  C C Lee; R C Grimm; A Manduca; J P Felmlee; R L Ehman; S J Riederer; C R Jack
Journal:  Magn Reson Med       Date:  1998-02       Impact factor: 4.668

8.  Echo-planar imaging with prospective slice-by-slice motion correction using active markers.

Authors:  Melvyn B Ooi; Sascha Krueger; Jordan Muraskin; William J Thomas; Truman R Brown
Journal:  Magn Reson Med       Date:  2011-02-24       Impact factor: 4.668

9.  Prospective motion correction of 3D echo-planar imaging data for functional MRI using optical tracking.

Authors:  Nick Todd; Oliver Josephs; Martina F Callaghan; Antoine Lutti; Nikolaus Weiskopf
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10.  Phase informed model for motion and susceptibility.

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  30 in total

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2.  Correction of respiratory artifacts in MRI head motion estimates.

Authors:  Damien A Fair; Oscar Miranda-Dominguez; Abraham Z Snyder; Anders Perrone; Eric A Earl; Andrew N Van; Jonathan M Koller; Eric Feczko; M Dylan Tisdall; Andre van der Kouwe; Rachel L Klein; Amy E Mirro; Jacqueline M Hampton; Babatunde Adeyemo; Timothy O Laumann; Caterina Gratton; Deanna J Greene; Bradley L Schlaggar; Donald J Hagler; Richard Watts; Hugh Garavan; Deanna M Barch; Joel T Nigg; Steven E Petersen; Anders M Dale; Sarah W Feldstein-Ewing; Bonnie J Nagel; Nico U F Dosenbach
Journal:  Neuroimage       Date:  2019-11-25       Impact factor: 6.556

3.  Dynamic 2D self-phase-map Nyquist ghost correction for simultaneous multi-slice echo planar imaging.

Authors:  Uten Yarach; Yi-Hang Tung; Kawin Setsompop; Myung-Ho In; Itthi Chatnuntawech; Renat Yakupov; Frank Godenschweger; Oliver Speck
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4.  Evaluating the organizational structure and specificity of network topology within the face processing system.

Authors:  Daniel B Elbich; Peter C M Molenaar; K Suzanne Scherf
Journal:  Hum Brain Mapp       Date:  2019-02-18       Impact factor: 5.038

5.  Distinctions among real and apparent respiratory motions in human fMRI data.

Authors:  Jonathan D Power; Charles J Lynch; Benjamin M Silver; Marc J Dubin; Alex Martin; Rebecca M Jones
Journal:  Neuroimage       Date:  2019-07-22       Impact factor: 6.556

6.  Real-Time Filtering with Sparse Variations for Head Motion in Magnetic Resonance Imaging.

Authors:  Daniel S Weller; Douglas C Noll; Jeffrey A Fessler
Journal:  Signal Processing       Date:  2018-12-03       Impact factor: 4.662

7.  Unified Retrospective EEG Motion Educated Artefact Suppression for EEG-fMRI to Suppress Magnetic Field Gradient Artefacts During Motion.

Authors:  Danilo Maziero; Victor A Stenger; David W Carmichael
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8.  Prospective motion correction of fMRI: Improving the quality of resting state data affected by large head motion.

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Review 9.  Methods for cleaning the BOLD fMRI signal.

Authors:  César Caballero-Gaudes; Richard C Reynolds
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10.  Functional brain imaging in voiding dysfunction.

Authors:  Rose Khavari; Timothy B Boone
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