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!

Wednesday, November 16, 2011

Understanding fMRI artifacts: "Good" axial data

Good EPI data has a number of dynamic features that are perfectly normal once a few basic properties of the sample - a person's head - are considered. The task is to differentiate these normal features from abnormal (or abnormally high) artifacts and signal changes. We'll look at axial slices first because these are the most common slice prescription for fMRI. (Axial oblique slices will exhibit much the same features as the axial data considered here.)

The data we will consider in this post were acquired with a single shot, gradient echo EPI sequence on a Siemens Trio/TIM scanner, using the 12-channel head RF coil and a pulse sequence functionally equivalent to the product sequence, ep2d_bold. (See Note 1.) Parameters were typical for whole cortex coverage (the lower portion of the cerebellum tends to get cut off): 34 slices, 3 mm slice thickness, 10% slice gap, TR=2000 ms, TE=28 ms, flip angle = 90 deg, 64x64 matrix over a 22.4 cm field-of-view yielding 3.5 mm resolution in-plane, full k-space with phase encoding oriented anterior-posterior. (See Note 2 for advanced parameters.) The entire time series was 150 volumes in duration but in the movies and statistical images that follow I've considered only the first fifty volumes. (See Note 3 if you want to download the entire raw data and/or the movies and jpeg images.)

Let's start by simply looping through the volumes with the contrast set to reveal anatomy. Play this through a couple of times to familiarize yourself with it, then read on (click the 'YouTube' icon on the video to launch an expanded version in a separate tab/window):

Other than movement of the eyes and some large blood vessels in the inferior slices, at this resolution it's difficult to determine with certainty which regions are fluctuating and which are stationary. So let's zoom in on some of the central slices and replay the cine loop:

Now we can see that there's quite a bit of brain pulsation going on. Indeed, nothing appears stationary now! However, the edges of the brain don't appear to be moving very much so we can be reasonably confident that the pulsation is due to normal physiology and not a fidgety subject.

Now let's replay the same zoom but with the images re-contrasted to make the ghosts and the background noise regions visible:

The pulsations in the brain are still just about visible, and the edges of the brain still appear to be stationary. Good. Now, though, we can clearly see the N/2 ghosts and these are stationary also. Even better! This strongly suggests that the subject movement is very low. If the subject were moving even a little bit it would tend to perturb the magnetic field homogeneity over the head (the "shim") and cause the ghost intensity to fluctuate. Furthermore, if the subject had moved a lot - say, he sneezed - in the time between the shim and the acquisition of these EPIs, we might expect the ghost level to be high but temporally stable. The ghost level is acceptably low, also indicating an absence of significant movement since the start of the session. (See Note 3 in the post, Tactical approaches to (re)shimming for instructions on triggering a shim at any point during a scan on a Siemens.)

Also in this view we see that the background regions that aren't contaminated with ghosts tend to fluctuate in a random manner, as they should. Nothing coherent pops up, it's just speckly, grainy noise. Also good.

But what about the inferior slices? The quality of the shim will be considerably worse for the frontal and temporal lobes, and the inferior surface of the brain. Then there are the eyes to consider. So let's take a look at a zoom into some of these slices:

Yup, the eyes are dancing around alright! And the muscles around the eyes generate some dynamic signal, too. Normal physiological fluctuations, especially large blood vessels (white spots) are present, but otherwise the edges of the brain appear relatively stationary.

The ghosts were already visible in the previous contrast, but let's re-contrast and highlight the background, as we did previously:

The brain's edges remain stationary by inspection - good - and the N/2 ghosts also appear to be quite consistent - also good. And finally, the background noise is relatively uniform - no obvious structure. If you're wondering what the faint horizontal streaks are that are especially pronounced in the bottom row of slices, it's a consequence of the interaction of a sharp contrast boundary and ramp-sampled k-space. I'll deal with this artifact in detail in a separate post. At this point you should content yourself with it being a normal feature of EPI.

Simple statistical images

The sort of zooming and contrasting that I've done with these movies is the sort of thing that you can (and should) do with your scanner's inline (real time) image display tool. Rather than catching up on your fiction reading while your fMRI experiment proceeds, spend the time watching the real time display to ensure that the images' appearance remains similar to what you've just seen.

To reinforce the features in the movies I want to change gears and show you how this fifty-volume data set appears in simple statistical images. Unless your scanner has real time analysis you're not going to be able to look at summary statistical images like these on-the-fly, but it is instructive to see how the fluctuations you've seen in the movies translate into statistical features. We will also be in a position to make an assessment on the likely impact on fMRI statistics because we're quantifying the background fluctuations that your experiment has to overcome to reach significance. (See the post, Comparing fMRI Protocols for detailed explanation on the usefulness of the following images a proxy for fMRI performance.)

Consider the temporal SNR (TSNR) image, which is the pixelwise mean divided by the pixelwise standard deviation of the fifty image series:

TSNR image. (Click to enlarge.)

Let's also consider the standard deviation (SDEV) image. It helps confirm what we think we're seeing in the TSNR images. (I am in the habit of using these as a pair for diagnostic purposes. It means I get to see the entire dynamic range of fluctuations.)

Standard deviation image. (Click to enlarge.)

The eyes are easily the brightest feature in the SDEV image - all those saccades we saw in the movie! - and this translates into relatively low TSNR. Favorably, however, there are no intense ghosts arising from the eyes apparent in the SDEV image. If there were, we should expect to see twin stripes (or pairs of oval-shaped ghosts) in anterior-posterior planes that encompass the eyes. (Recall from physics Part Twelve that the ghosts will be located in the A-P direction with the phase encoding axis oriented A-P.) We will look at ghosts from eye movement later on in this series.

Another bright feature in the SDEV image is large blood vessels, particularly in the lower slices (towards top left in the matrix view). This is good news: one hopes the subject has a cardiac cycle! These bright spots are perfectly normal, and acceptable.

The next most intense features in the SDEV image are the edges of the brain and regions that contain CSF, the ventricles and sulci. This is consistent with cardiac-driven pulsatile flow of CSF, as well as pulsations of the brain tissue in general. We might have predicted high SDEV (and low TSNR) for CSF-filled spaces because of the contrast between gray/white matter and CSF in the original EPIs; CSF appears quite a lot brighter than tissue. Coupled with the expected pulsations, the features make sense in the statistical images.

Looking more closely at the outside edges of the brain in the SDEV image, there is also a subtle enhancement of the posterior and anterior edges compared to the left and right edges. This could be head movement - movement in the chin-to-chest axis is most easily achieved by a subject for many reasons, including (especially) swallowing - pulsatile flow of CSF in the meninges, or it could be due to a small amount of gradient heating during the acquisition, because such heating tends to look like motion in the phase encoding direction. It's difficult or impossible to tell between these three explanations with these simple statistical views. What's important here is that this sort of edge enhancement is absolutely normal and should be expected. (Realignment algorithms, as typically used in an fMRI post-processing stream, can be expected to eliminate much of the reduced TSNR in the anterior/posterior edges.)

If you can negotiate your gaze around the CSF-filled spaces you may be able to make out that gray matter regions have higher standard deviation than white matter regions, producing correspondingly lower TSNR for gray matter. This is entirely predicted by neurophysiology, because we know that GM is many times more metabolically active than WM. Blood vessel density, to support the higher metabolic rates, is higher in GM and this translates into larger signal fluctuations in our BOLD-contrasted time series. Welcome to resting-state fMRI! It's precisely this "ongoing activity" that we map with that technique.

Now we're down to the lowest of features in these "good" statistical images, so I'll include a labeled composite of the SDEV and TSNR images to help you out:

(Click to enlarge.)

Note the circles of scalp fat around the head, visible most prominently in the TSNR image. Although a fat suppression pulse is applied before each EPI slice, the suppression is imperfect and tends to leave a small residual signal. But the good news is that these fat circles appear only weakly in the SDEV image, suggesting that head movement and/or gradient heating effects were low during the acquisition. Indeed, the fat circles in the SDEV image get brighter the more superior the slice. This is consistent with small movement (head rotation) in the chin-to-chest direction, as can happen from slow compression of foam padding and/or relaxation of neck muscles. Swallowing, fidgeting and other head motion tends to produce much larger deviations, as we'll see in later posts.

Finally, ghosts again. Note that the N/2 ghosts from the majority brain signal are low intensity; down towards the background noise level (where they should be). The most intense ghosts in the TSNR image are due to the residual fat signal I just mentioned. This is expected because fat protons resonate several hundred Hz away from water protons, meaning that there will always be an accrued phase difference between fat and water signals that will manifest as increased ghosting for whichever of the two species is placed off-resonance. Convention (and common sense) suggests placing water on resonance, because that's the signal of interest in the brain tissue, and that means that the residual subcutaneous scalp fat will produce (unavoidable) N/2 ghosting. This is normal; the only way to reduce fat ghosts is to improve fat suppression, which is a separate topic for another day.

Next post: "good" coronal and sagittal EPIs.



1.  At Berkeley we use a modified version of ep2d_bold, called ep2d_neuro. In these tests there is no functional difference between the two sequences. We have our own local default pulse sequence so that we can have, if desired, thinner slices, a user-defined number of dummy scans, 10 microsec precision in TR, and some other relatively minor tweaks. If you want to replicate these tests then simply set up ep2d_bold with the same parameters as used here.

2.  Bandwidth = 2790 Hz/pixel, echo spacing = 0.47 ms. These days I don't tend to drive the echo spacing below 0.5 ms without very good reason, but for the purposes of this post - assessing normal variations in the signal and ghosts - these settings may be considered optimal. I don't want to get deep into the effects of echo spacing here, but a short version is that setting the bandwidth and echo spacing (as a pair) is an exercise in avoiding mechanical resonances and excessive ramp sampling that can enhance any fluctuations in the power feeding the gradient amplifiers. I'll deal with these issues in depth in dedicated posts. These are two of the artifacts we want to become intimately familiar with!

3.  Want the raw data from this post? You can download a zip file containing all the DICOM images here:

If you don’t already have a DICOM viewer, check out Osirix for Mac OSX (available via a link in the sidebar). ImageJ from NIH - also in the sidebar - has some nice features for ROI analysis, too.

If you want the movies and jpeg images that appear above, these are available here:

You can use the data, movies and jpegs for any educational purpose you like. No need to acknowledge the source unless you really want to, in which case please cite and the Henry H. Wheeler, Jr. Brain Imaging Center at UC Berkeley.

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