Literature DB >> 10332880

Motion correction in fMRI via registration of individual slices into an anatomical volume.

B Kim1, J L Boes, P H Bland, T L Chenevert, C R Meyer.   

Abstract

An automated retrospective image registration based on mutual information is adapted to a multislice functional magnetic resonance imaging (fMRI) acquisition protocol to provide accurate motion correction. Motion correction is performed by mapping each slice to an anatomic volume data set acquired in the same fMRI session to accommodate inter-slice head motion. Accuracy of the registration parameters was assessed by registration of simulated MR data of the known truth. The widely used rigid body volume registration approach based on stacked slices from the time series data may hinder statistical accuracy by introducing inaccurate assumptions of no motion between slices for multislice fMRI data. Improved sensitivity and specificity of the fMRI signal from mapping-each-slice-to-volume method is demonstrated in comparison with a stacked-slice correction method by examining functional data from two normal volunteers. The data presented in a standard anatomical coordinate system suggest the reliability of the mapping-each-slice-to-volume method to detect the activation signals consistent between the two subjects.

Mesh:

Year:  1999        PMID: 10332880     DOI: 10.1002/(sici)1522-2594(199905)41:5<964::aid-mrm16>3.0.co;2-d

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


  27 in total

1.  Zero and first-order phase shift correction for field map estimation with dual-echo GRE using bipolar gradients.

Authors:  Desmond T B Yeo; Thomas L Chenevert; Jeffrey A Fessler; Boklye Kim
Journal:  Magn Reson Imaging       Date:  2007-04-18       Impact factor: 2.546

2.  Concurrent correction of geometric distortion and motion using the map-slice-to-volume method in echo-planar imaging.

Authors:  Desmond T B Yeo; Jeffrey A Fessler; Boklye Kim
Journal:  Magn Reson Imaging       Date:  2008-02-15       Impact factor: 2.546

3.  Integration of motion correction and physiological noise regression in fMRI.

Authors:  Tyler B Jones; Peter A Bandettini; Rasmus M Birn
Journal:  Neuroimage       Date:  2008-05-21       Impact factor: 6.556

4.  Optimizing preprocessing and analysis pipelines for single-subject fMRI. I. Standard temporal motion and physiological noise correction methods.

Authors:  Nathan W Churchill; Anita Oder; Hervé Abdi; Fred Tam; Wayne Lee; Christopher Thomas; Jon E Ween; Simon J Graham; Stephen C Strother
Journal:  Hum Brain Mapp       Date:  2011-03-31       Impact factor: 5.038

Review 5.  Mapping fetal brain development in utero using magnetic resonance imaging: the Big Bang of brain mapping.

Authors:  Colin Studholme
Journal:  Annu Rev Biomed Eng       Date:  2011-08-15       Impact factor: 9.590

6.  Self-encoded marker for optical prospective head motion correction in MRI.

Authors:  Christoph Forman; Murat Aksoy; Joachim Hornegger; Roland Bammer
Journal:  Med Image Anal       Date:  2011-06-13       Impact factor: 8.545

7.  Optimal slice timing correction and its interaction with fMRI parameters and artifacts.

Authors:  David Parker; Xueqing Liu; Qolamreza R Razlighi
Journal:  Med Image Anal       Date:  2016-08-24       Impact factor: 8.545

8.  Motion-robust diffusion compartment imaging using simultaneous multi-slice acquisition.

Authors:  Bahram Marami; Benoit Scherrer; Shadab Khan; Onur Afacan; Sanjay P Prabhu; Mustafa Sahin; Simon K Warfield; Ali Gholipour
Journal:  Magn Reson Med       Date:  2018-11-16       Impact factor: 4.668

9.  Temporal slice registration and robust diffusion-tensor reconstruction for improved fetal brain structural connectivity analysis.

Authors:  Bahram Marami; Seyed Sadegh Mohseni Salehi; Onur Afacan; Benoit Scherrer; Caitlin K Rollins; Edward Yang; Judy A Estroff; Simon K Warfield; Ali Gholipour
Journal:  Neuroimage       Date:  2017-04-19       Impact factor: 6.556

10.  SimPACE: generating simulated motion corrupted BOLD data with synthetic-navigated acquisition for the development and evaluation of SLOMOCO: a new, highly effective slicewise motion correction.

Authors:  Erik B Beall; Mark J Lowe
Journal:  Neuroimage       Date:  2014-06-24       Impact factor: 6.556

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