Education, tips and tricks to help you conduct better fMRI experiments.
Sure, you can try to fix it during data processing, but you're usually better off fixing the acquisition!

Tuesday, February 28, 2012

Common persistent EPI artifacts: Distortion and dropout

The origins of distortion and dropout in EPI were covered in PFUFA Part Twelve, and both of these artifacts have been mentioned in passing in the previous articles concerning abnormally high ghosting. In some instances these artifacts are "co-morbid" because certain issues that cause abnormally high ghosting - such as a poor shim because of asymmetric placement of the subject's head in the magnet - are likely to increase distortion and dropout effects at the same time. Except that it can be very difficult to evaluate distortion and dropout by inspection, during an experiment. The ghosts can be used as a fairly independent "barometer" of the experiment's quality if, as is often the case, some of them fall into an image region that is otherwise noise. Not so with distortion and dropout. By definition these artifacts plague signal regions in the brain, and even an experienced operator can have a tough time determining when either issue is worse than it might otherwise be.

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.