» Articles » PMID: 21695720

Echo-planar Imaging with Prospective Slice-by-slice Motion Correction Using Active Markers

Overview
Journal Magn Reson Med
Publisher Wiley
Specialty Radiology
Date 2011 Jun 23
PMID 21695720
Citations 22
Authors
Affiliations
Soon will be listed here.
Abstract

Head motion is a fundamental problem in functional magnetic resonance imaging and is often a limiting factor in its clinical implementation. This work presents a rigid-body motion correction strategy for echo-planar imaging sequences that uses micro radiofrequency coil "active markers" for real-time, slice-by-slice prospective correction. Before the acquisition of each echo-planar imaging-slice, a short tracking pulse-sequence measures the positions of three active markers integrated into a headband worn by the subject; the rigid-body transformation that realigns these markers to their initial positions is then fed back to dynamically update the scan-plane, maintaining it at a fixed orientation relative to the head. Using this method, prospectively-corrected echo-planar imaging time series are acquired on volunteers performing in-plane and through-plane head motions, with results demonstrating increased image stability over conventional retrospective image-realignment. The benefit of this improved image stability is assessed in a blood oxygenation level dependent functional magnetic resonance imaging application. Finally, a non-rigid-body distortion-correction algorithm is introduced to reduce the remaining signal variation.

Citing Articles

Resting State fMRI: Going Through the Motions.

Maknojia S, Churchill N, Schweizer T, Graham S Front Neurosci. 2019; 13:825.

PMID: 31456656 PMC: 6700228. DOI: 10.3389/fnins.2019.00825.


Combining Prospective Acquisition CorrEction (PACE) with retrospective correction to reduce motion artifacts in resting state fMRI data.

Lanka P, Deshpande G Brain Behav. 2019; 9(8):e01341.

PMID: 31297966 PMC: 6710196. DOI: 10.1002/brb3.1341.


Prospective motion correction improves the sensitivity of fMRI pattern decoding.

Huang P, Carlin J, Alink A, Kriegeskorte N, Henson R, Correia M Hum Brain Mapp. 2018; 39(10):4018-4031.

PMID: 29885014 PMC: 6175330. DOI: 10.1002/hbm.24228.


EEG-Informed fMRI: A Review of Data Analysis Methods.

Abreu R, Leal A, Figueiredo P Front Hum Neurosci. 2018; 12:29.

PMID: 29467634 PMC: 5808233. DOI: 10.3389/fnhum.2018.00029.


Prospective motion correction in functional MRI.

Zaitsev M, Akin B, Levan P, Knowles B Neuroimage. 2016; 154:33-42.

PMID: 27845256 PMC: 5427003. DOI: 10.1016/j.neuroimage.2016.11.014.


References
1.
Pipe J . Motion correction with PROPELLER MRI: application to head motion and free-breathing cardiac imaging. Magn Reson Med. 1999; 42(5):963-9. DOI: 10.1002/(sici)1522-2594(199911)42:5<963::aid-mrm17>3.0.co;2-l. View

2.
Ward H, Riederer S, Grimm R, Ehman R, Felmlee J, Jack Jr C . Prospective multiaxial motion correction for fMRI. Magn Reson Med. 2000; 43(3):459-69. DOI: 10.1002/(sici)1522-2594(200003)43:3<459::aid-mrm19>3.0.co;2-1. View

3.
Andersson J, Hutton C, Ashburner J, Turner R, Friston K . Modeling geometric deformations in EPI time series. Neuroimage. 2001; 13(5):903-19. DOI: 10.1006/nimg.2001.0746. View

4.
Welch E, Manduca A, Grimm R, Ward H, Jack Jr C . Spherical navigator echoes for full 3D rigid body motion measurement in MRI. Magn Reson Med. 2002; 47(1):32-41. DOI: 10.1002/mrm.10012. View

5.
Hutton C, Bork A, Josephs O, Deichmann R, Ashburner J, Turner R . Image distortion correction in fMRI: A quantitative evaluation. Neuroimage. 2002; 16(1):217-40. DOI: 10.1006/nimg.2001.1054. View