Beauchamp:MotionCorrection
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Beauchamp Lab
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Motion Correction
Over the ~hour-long course of a typical fMRI experiment, it is impossible for the subject's head to remain in the same position, barring the use of uncomfortable restraint hardware such as bite bars. For compliant subjects, the head motion usually consists of a gradual motion as the chin sinks towards the chest or the entire head sinks into the cushioning foam.
Here is a graph showing head position and rotation in a typical fMRI experiment with a compliant subject (case CW). The x-axis represents time. Each time-point represents one EPI image, and each graph shows the amount of shift or rotation estimated to have occurred at that time point by the AFNI program 3dvoreg:
It is not possible to fully correct head motion. If data about a spatial location was not acquired, no algorithm will bring it back. However, the effect of movement can be minimized. The usual approach is to simply shift and rotate each image to match a template.The template can be either the mean of all EPI images, or a single EPI image, usually the EPI image collected closest in time to the high-resolution anatomical T1 dataset (see below). A good qualitative way to examine motion and the efficacy of motion correction is to open axial, sagittal and coronal views of the EPI dataset. Switch back and forth between the 0th timepoint and the last timepoint, using the arrows next to the "Index" label inside the main AFNI window.
Distortion Correction
EPI images are significantly distorted relative to true brain anatomy. This is a problem because activation maps created from EPI images are overlaid on T1 images (or cortical surface models created from T1 images) which reflect the true anatomy. The following images show a T1 image with outlines for the outer boundary of gray matter (blue line) and white matter (green line).
and EPI images with the boundaries overlaid.
Note that this is not a problem of subject motion: inhomogeneities in the magnetic field in the head (create by the tissue properties) creates the distortion. The best way to remove distortion is to measure the magnetic field in the scanner (creating a so-called B0 map) and use it to unwarp the EPI images. Because this is not commonly done, a second choice is to distort the EPI images so that they match the T1 image. This is done in AFNI using the 3dAllineate program. A qualitative way of judging the effectiveness is to overlay the unwarped EPI on the T1 and see how they match. A quantitative way is to analyse fMRI activation maps created from EPI data; if motion correction and distortion correction is successful, the significance of the observed activation should, in general, be higher.
