So I'm afraid I don't have a whole lot of new information to offer on either distortion or dropout, from the perspective of diagnosing and potentially changing (improving) your experiment on the day. Other than very obvious deficiencies, as might happen if the subject has a highly conductive hair product, for example, I don't spend much time evaluating distortion or dropout by inspection. Ghosts can be a good surrogate for all that ails distortion and dropout, so I focus on those.
Where you can potentially improve the situation for distortion and dropout is with parameter selection when you are establishing your experimental protocol. Distortion and dropout will generally change with slice prescription, as we already saw in the "good data" posts. And it may be that reduction of dropout leads you to use a particular slice direction, e.g. coronal slices for improved frontal lobe signal. After that, the other common tactics to minimize dropout are to use the thinnest possible slice thickness, possibly using higher in-plane spatial resolution, and perhaps decrease TE. These are protocol/parameter questions that are covered somewhat in my user training guide/FAQ, and I will expand on those sections below. Be warned, however, that it is very difficult to provide general guidelines for all fMRI experiments. Instead, the parameter choices tend to be dictated by your primary requirements. You might select very different parameters for a study that is primarily interested in orbitofrontal cortex than you would use for a sensorimotor task. It's horses for courses.
Approaches to tackling distortion
The level of distortion in the phase encoding dimension is a function of the echo spacing, as explained in PFUFA Part Twelve. Tactics to reduce the distortion level involve making fundamental changes to the phase encoding k-space scheme, e.g. multi-shot segmented k-space, or parallel imaging methods. In each approach the essential idea is to increase the k-space step size, thereby increasing the bandwidth of the phase encoding dimension.
Distortion reduction techniques such as segmented EPI and GRAPPA are beyond the scope of this artifact series. For now I am focusing exclusively on single-shot EPI because that's what most people use. But I would note in passing that each of these distortion reduction schemes comes at a cost - usually increased motion sensitivity. I already discussed the motion sensitivity of GRAPPA in a previous post. Segmented EPI gets very little use for fMRI at 3 T these days, but at some point I may do a PFUFA post on it. Until then, Stuart Clare's PhD thesis is online and has a good description of the method as well as some of the issues.
That leaves another approach entirely: trying to fix the distortion. At this juncture the most common way to attempt a fix is with a magnetic field map. I'm not going to get into the specifics of field maps today because I see this as a peripheral issue to the theme of this post series, which is on the acquisition of the EPI data. You can find a basic introduction to the acquisition and use of a field map in my most recent user training guide/FAQ (see Note 1). Your choice to use a field map to try to correct distortion has no direct influence on what you do in the EPI; the parameters you select will produce a certain level of distortion, and your decision on whether to try to remedy the distortion is one that can be made independently. That said, I may try to do a dedicated post on distortion correction at some point, because there are some experimental considerations, such as when to acquire a field map for distortion correction, and whether more than one field map acquisition is required per scan session.
Until that dedicated post I will direct you to a short review article, and references therein, courtesy of Peter Jezzard:
"Correction of geometric distortion in fMRI data." P. Jezzard, NeuroImage Epub (2011).
I would point out the youthful Peter Jezzard who appears in Figure 1, except that the bugger looks exactly the same today!
The paper doesn't address worse than normal/typical distortion, it's about the problem and how to potentially fix it. The distortion you get in your experiment should only be a function of the parameters you've selected, and the shim (i.e. the residual magnetic susceptibility gradients). And you have already seen how to diagnose a poor shim using the N/2 ghosts.
Advanced methods to try to undo distortion are (at this juncture) beyond the scope of this series because it's not something that is likely to be a function of your skill as an experimenter. Furthermore, few of the methods are (yet?) available on commercial scanners. Do a Pubmed search for "epi distortion correction" to pull up a few dozen methods. It's interesting to note that most of the methods are aimed at fixing distortion for EPI-based diffusion imaging. This may well be because people doing tractography are more aware of the need to match to undistorted anatomical space, but it could also be because changing the pulse sequence for diffusion imaging doesn't have the same statistical implications as it does for fMRI. In fMRI we seek to make each sample - essentially, each TR period - as independent as possible. Many schemes aimed at reducing distortion cause a temporal (and possibly spatial) smoothing to be imposed on the time series. But, as I say, this is far beyond the scope of this post or this blog right now.
Approaches to tackling dropout
There are modified pulse sequences, such as Z shim methods (do a Pubmed search for "EPI z-shim") that seek to use regionally optimal refocusing gradients and then pool the results into a final, improved image. None of these sequences has gained widespread acceptance yet, I suspect because there tends to be a temporal penalty in the acquisition of different pieces of the final image, and because there is additional complexity in the data processing (even if the pulse sequences were available on commercial scanners, which most aren't). Instead, there are techniques to tease out improved performance with a series of tweaks to TE and voxel size, such as this one from the FIL:
"Optimized EPI for fMRI studies of the orbitofrontal cortex: compensation of susceptibility-induced gradients in the readout direction." Weiskopf et al., MAGMA 20(1), 39-49 (2007).
The general principles are easy to comprehend: lower TE or higher spatial resolution (smaller voxels) tend to reduce the susceptibility-induced dephasing, leading to signal recovery in some brain regions. The costs of these approaches should be relatively obvious by now, too. A shorter TE will sacrifice some BOLD sensitivity in well-shimmed brain regions - but probably not enough to be of concern - while attaining higher spatial resolution requires higher gradient performance and acquisition time, potentially limiting coverage to a portion of the brain. However, in decreasing the signal loss through higher resolution - a process that increases the effective T2* for the problem voxels - it can then be useful to increase the TE to regain optimum BOLD sensitivity. This was the conclusion of a study that investigated optimum parameters for fMRI of amygdala:
"The impact of EPI voxel size on SNR and BOLD sensitivity in the anterior medio-temporal lobe: a comparative group study of deactivation of the default mode." Robinson et al., MAGMA 21(4), 279-90 (2008).
There are several other papers concerning sets of tweaks for specific applications; the above paper covers many of them. But I'm going to quit here because this post is already rambling. Feel free to post a question on particular brain regions or applications and I'll do my best to track down prior work for you to follow.
1. I'll be releasing an updated user training guide/FAQ in the next week. The acquisition of a field map for distortion correction is one of the new sections.
As previously, the most recent version of the training guide/FAQ is available from this web page:
Locate the file attachment towards the bottom of the page. The last version was called 3T_user_training_FAQ_19April2011.doc.