Introducing the series
The workhorse sequence for fMRI in most labs is single-shot gradient echo echo planar imaging (EPI). As we saw in the final post of the last series, EPI is selected for fMRI because of its imaging speed (and BOLD contrast), not for its ability to produce accurate, detailed facsimiles of brain anatomy. Our need for speed means we are forced to live with several inherent artifacts associated with the sequence.
However, in addition to the "characteristic three" EPI artifacts of ghosting, distortion and dropout, when we're doing fMRI we are more concerned with changes over time than with the artifact level of an individual image. So, in this series we need to assess the sources of changes between images, even if the images themselves appear to be perfectly acceptable (albeit subject to the "characteristic three").
What's the data supposed to look like?
It would be rather difficult for you to determine when something has gone wrong during your fMRI experiment if you didn't have a solid appreciation of what the images ought to look like when things are going well. Accordingly, I'll begin this series with a review of what EPIs are supposed to look like in a time series. We'll look at typical levels of the undesirable features and assess those parts of an image that vary due to normal physiology. This is what we should expect to see, having taken all reasonable precautions with the subject set up and assuming that the entire suite of hardware (scanner and peripherals) is behaving properly.
Good axial data will be the focus of the first post in the series. (Axial oblique images will exhibit qualitatively similar features to the axial slices I'll show.) In the second post I'll show examples of good sagittal and coronal data. Artifacts may appear quite differently and with dissimilar severity merely by changing the slice prescription, so it's important to keep in mind the anisotropic nature of many EPI defects. Motion sensitivity is also different, of course. Motion that was through-plane for an axial prescription is in-plane for sagittal images, for example.
Ooh, that's bad. Is it...?
With a review of good data under our belts it will be time to look at the appearance of EPI when things go tango uniform. I will group artifacts according to their temporal behavior - either persistent or intermittent - and their origins - either from hardware, from the subject, or from operator error. You should then be able to understand and differentiate the various artifacts and be able to properly diagnose (and fix) them when it counts the most: during the data acquisition. Waiting until the subject has left the building before finding a scanner glitch is a bit like doing a blood test on a corpse. Sure, you might be able to determine that it was the swine flu that finished him off, either way he's dead. Our aim will be to do our “blood tests” while there is still a chance of administering medicine and perhaps achieving a recovery.