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

Todd N, Josephs O, Callaghan MF, Lutti A, Weiskopf N - Neuroimage (2015)

Bottom Line: Metrics were developed to quantify the amount of motion as it occurred relative to k-space data acquisition.The PMC system did not introduce extraneous artifacts for the no motion conditions and improved the time series temporal signal-to-noise by 30% to 40% for all combinations of low/high resolution and slow/fast head movement relative to the standard acquisition with no prospective correction.The numbers of activated voxels (p<0.001, uncorrected) in both task-based experiments were comparable for the no motion cases and increased by 78% and 330%, respectively, for PMC on versus PMC off in the slow motion cases.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK. Electronic address: nicholas.todd@ucl.ac.uk.

No MeSH data available.


Example of how motion timing with respect to 3D data acquisition affects image quality. Panel A shows the motion metrics during the data acquisition of the 69th and 70th image volumes from Volunteer #2 (1.5 mm resolution, PMC off, fast motion), where the motion is occurring in the peripheral partitions for volume 69 and in the central partitions for volume 70. Panels B–E display one slice of the reconstructed volume, showing the reference image (B), the reconstructed volumes 69 and 70 (C and E), and the difference images (D and F).
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f0015: Example of how motion timing with respect to 3D data acquisition affects image quality. Panel A shows the motion metrics during the data acquisition of the 69th and 70th image volumes from Volunteer #2 (1.5 mm resolution, PMC off, fast motion), where the motion is occurring in the peripheral partitions for volume 69 and in the central partitions for volume 70. Panels B–E display one slice of the reconstructed volume, showing the reference image (B), the reconstructed volumes 69 and 70 (C and E), and the difference images (D and F).

Mentions: Fig. 3 shows an example of the importance of motion timing with respect to k-space partition acquisition that motivates the MPW metric. Fig. 3A shows the motion metrics during two consecutive image volumes acquired from Volunteer #2 (1.5 mm resolution, PMC off, fast motion). More motion occurred during the acquisition of image volume 69, but the motion mainly occurred as the peripheral k-space partitions were being acquired, whereas the motion from image volume 70 was coincident with the acquisition of the central portion of k-space. Fig. 3B shows the baseline image from this run that was used as the motion-free reference image, and respective images and difference images from volumes 69 and 70 are shown in Figs. 2C–F. It can be seen that the image error was worse for image volume 70, which supports the MPW metric being a better predictor of the artifact level than the simple integrated motion metric.


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

Todd N, Josephs O, Callaghan MF, Lutti A, Weiskopf N - Neuroimage (2015)

Example of how motion timing with respect to 3D data acquisition affects image quality. Panel A shows the motion metrics during the data acquisition of the 69th and 70th image volumes from Volunteer #2 (1.5 mm resolution, PMC off, fast motion), where the motion is occurring in the peripheral partitions for volume 69 and in the central partitions for volume 70. Panels B–E display one slice of the reconstructed volume, showing the reference image (B), the reconstructed volumes 69 and 70 (C and E), and the difference images (D and F).
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4441089&req=5

f0015: Example of how motion timing with respect to 3D data acquisition affects image quality. Panel A shows the motion metrics during the data acquisition of the 69th and 70th image volumes from Volunteer #2 (1.5 mm resolution, PMC off, fast motion), where the motion is occurring in the peripheral partitions for volume 69 and in the central partitions for volume 70. Panels B–E display one slice of the reconstructed volume, showing the reference image (B), the reconstructed volumes 69 and 70 (C and E), and the difference images (D and F).
Mentions: Fig. 3 shows an example of the importance of motion timing with respect to k-space partition acquisition that motivates the MPW metric. Fig. 3A shows the motion metrics during two consecutive image volumes acquired from Volunteer #2 (1.5 mm resolution, PMC off, fast motion). More motion occurred during the acquisition of image volume 69, but the motion mainly occurred as the peripheral k-space partitions were being acquired, whereas the motion from image volume 70 was coincident with the acquisition of the central portion of k-space. Fig. 3B shows the baseline image from this run that was used as the motion-free reference image, and respective images and difference images from volumes 69 and 70 are shown in Figs. 2C–F. It can be seen that the image error was worse for image volume 70, which supports the MPW metric being a better predictor of the artifact level than the simple integrated motion metric.

Bottom Line: Metrics were developed to quantify the amount of motion as it occurred relative to k-space data acquisition.The PMC system did not introduce extraneous artifacts for the no motion conditions and improved the time series temporal signal-to-noise by 30% to 40% for all combinations of low/high resolution and slow/fast head movement relative to the standard acquisition with no prospective correction.The numbers of activated voxels (p<0.001, uncorrected) in both task-based experiments were comparable for the no motion cases and increased by 78% and 330%, respectively, for PMC on versus PMC off in the slow motion cases.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK. Electronic address: nicholas.todd@ucl.ac.uk.

No MeSH data available.