Literature DB >> 18992826

Improving robustness and reliability of phase-sensitive fMRI analysis using temporal off-resonance alignment of single-echo timeseries (TOAST).

Andrew D Hahn1, Andrew S Nencka, Daniel B Rowe.   

Abstract

Echo Planar Imaging (EPI), often utilized in functional MRI (fMRI) experiments, is well known for its vulnerability to inconsistencies in the static magnetic field (B(0)). Correction for these field inhomogeneities usually involves measuring the magnetic field at a single time point, and using this static information to correct a series of images collected over the course of one or multiple experiments. However, common phenomena, such as respiration and motion, change the characteristics of the B(0) field homogeneity in a time-dependent and often unpredictable manner, rendering previous field measurements invalid. The effects of these changes are particularly large in the image phase, due to its direct and sensitive relationship to the magnetic field, and methods utilizing complex information can suffer enormously. This dependence can be exploited to estimate the temporal dynamics of the B(0) field. Use of this information to correct fMRI data can provide more effective motion correction, reduce temporal "noise," and can substantially restore statistically significant power to complex fMRI data analysis. All of the necessary information is embedded in complex EPI images, and results indicate this is a robust way to improve the quality of fMRI data, especially when used with complex analysis.

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Year:  2008        PMID: 18992826      PMCID: PMC2884970          DOI: 10.1016/j.neuroimage.2008.10.001

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


  27 in total

Review 1.  Sources of distortion in functional MRI data.

Authors:  P Jezzard; S Clare
Journal:  Hum Brain Mapp       Date:  1999       Impact factor: 5.038

2.  Geometric distortion correction in gradient-echo imaging by use of dynamic time warping.

Authors:  S A Kannengiesser; Y Wang; E M Haacke
Journal:  Magn Reson Med       Date:  1999-09       Impact factor: 4.668

3.  New approach for correcting distortions in echo planar imaging.

Authors:  Huairen Zeng; J Christopher Gatenby; Yansong Zhao; John C Gore
Journal:  Magn Reson Med       Date:  2004-12       Impact factor: 4.668

4.  A complex way to compute fMRI activation.

Authors:  Daniel B Rowe; Brent R Logan
Journal:  Neuroimage       Date:  2004-11       Impact factor: 6.556

5.  Correction for geometric distortion in echo planar images from B0 field variations.

Authors:  P Jezzard; R S Balaban
Journal:  Magn Reson Med       Date:  1995-07       Impact factor: 4.668

6.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages.

Authors:  R W Cox
Journal:  Comput Biomed Res       Date:  1996-06

7.  Correction of off resonance-related distortion in echo-planar imaging using EPI-based field maps.

Authors:  P J Reber; E C Wong; R B Buxton; L R Frank
Journal:  Magn Reson Med       Date:  1998-02       Impact factor: 4.668

8.  Simulated phase evolution rewinding (SPHERE): a technique for reducing B0 inhomogeneity effects in MR images.

Authors:  Y M Kadah; X Hu
Journal:  Magn Reson Med       Date:  1997-10       Impact factor: 4.668

9.  Measurement of the point spread function in MRI using constant time imaging.

Authors:  M D Robson; J C Gore; R T Constable
Journal:  Magn Reson Med       Date:  1997-11       Impact factor: 4.668

10.  Retrospective estimation and correction of physiological fluctuation in functional MRI.

Authors:  X Hu; T H Le; T Parrish; P Erhard
Journal:  Magn Reson Med       Date:  1995-08       Impact factor: 4.668

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  17 in total

1.  Physiologic noise regression, motion regression, and TOAST dynamic field correction in complex-valued fMRI time series.

Authors:  Andrew D Hahn; Daniel B Rowe
Journal:  Neuroimage       Date:  2011-10-07       Impact factor: 6.556

2.  Enhancing the utility of complex-valued functional magnetic resonance imaging detection of neurobiological processes through postacquisition estimation and correction of dynamic B(0) errors and motion.

Authors:  Andrew D Hahn; Andrew S Nencka; Daniel B Rowe
Journal:  Hum Brain Mapp       Date:  2011-02-08       Impact factor: 5.038

3.  Quantification of the statistical effects of spatiotemporal processing of nontask FMRI data.

Authors:  Muge Karaman; Andrew S Nencka; Iain P Bruce; Daniel B Rowe
Journal:  Brain Connect       Date:  2014-09-19

4.  Enhanced phase regression with Savitzky-Golay filtering for high-resolution BOLD fMRI.

Authors:  Robert L Barry; John C Gore
Journal:  Hum Brain Mapp       Date:  2014-01-17       Impact factor: 5.038

5.  Complex and magnitude-only preprocessing of 2D and 3D BOLD fMRI data at 7 T.

Authors:  Robert L Barry; Stephen C Strother; John C Gore
Journal:  Magn Reson Med       Date:  2011-07-11       Impact factor: 4.668

6.  The SENSE-Isomorphism Theoretical Image Voxel Estimation (SENSE-ITIVE) model for reconstruction and observing statistical properties of reconstruction operators.

Authors:  Iain P Bruce; M Muge Karaman; Daniel B Rowe
Journal:  Magn Reson Imaging       Date:  2012-05-21       Impact factor: 2.546

7.  Separation of parallel encoded complex-valued slices (SPECS) from a single complex-valued aliased coil image.

Authors:  Daniel B Rowe; Iain P Bruce; Andrew S Nencka; James S Hyde; Mary C Kociuba
Journal:  Magn Reson Imaging       Date:  2015-11-21       Impact factor: 2.546

8.  Investigation of BOLD fMRI resonance frequency shifts and quantitative susceptibility changes at 7 T.

Authors:  Marta Bianciardi; Peter van Gelderen; Jeff H Duyn
Journal:  Hum Brain Mapp       Date:  2013-07-29       Impact factor: 5.038

Review 9.  Methods for cleaning the BOLD fMRI signal.

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

10.  COMPLEX-VALUED TIME SERIES MODELING FOR IMPROVED ACTIVATION DETECTION IN FMRI STUDIES.

Authors:  Daniel W Adrian; Ranjan Maitra; Daniel B Rowe
Journal:  Ann Appl Stat       Date:  2018-09-11       Impact factor: 2.083

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