Another brief explanation of decoding

Here's another short video produced by UC's media people in which Jack Gallant explains in broad terms how his group's recent decoding experiment was conducted:

A good place to go next for more details is the Gallant Lab website. Read the FAQ on that page to gain a basic understanding of what the experiment was, and what it wasn't. Then go read the paper, it's written very accessibly!

Common persistent EPI artifacts: Aliasing, or wraparound

 
In Part Eleven of the series Physics for understanding fMRI artifacts (hereafter referred to as PFUFA) you saw how setting parameters in k-space determined the image field-of-view (FOV) and resolution. In that introduction I kept everything simple, and the Fourier transform from the k-space domain to the image domain worked perfectly. For instance, in one of the examples the k-space step size was doubled in one dimension, thereby neatly chopping the corresponding image domain in half with no apparent problems. At the time, perhaps you wondered where the cropped portions of sky and grass had gone from around the remaining, untouched Hawker Hurricane aeroplane. Or perhaps you didn't.

In any event, you can assume from the fact that this is a post dedicated to something called 'aliasing' that in real world MRI things aren't quite as neat and tidy. Changing the k-space step size - thereby changing the FOV - has consequences depending on the extent of the object being imaged relative to the extent of the image FOV. It's possible to set the FOV too small for the object. Alternatively, it's possible to have the FOV set to an appropriate span but position it incorrectly. (The position of the FOV relative to signal-generating regions of the sample is a settable parameter on the scanner.) Overall, what matters is where signals reside relative to the edges of the FOV.

Now, on a modern MRI scanner with fancy electronics, aliasing is a problem in one dimension only: the phase encoding dimension. (Yeah, the one with all the distortion and the N/2 ghosts. Sucks to be that dimension!) The frequency encoding dimension manages to escape the aliasing phenomenon by virtue of inline analog and digital filtering, processes that don't have a direct counterpart in the phase encoding dimension. Instead, signal that falls outside the readout dimension FOV, either because the FOV is too small or because the FOV is displaced relative to the object, is eliminated. It's therefore important to know what happens where and when as far as both image dimensions are concerned. One dimension gets chopped, the other gets aliased.

I will first cover the signal filtering in the frequency encoding dimension and then deal with aliasing in the phase encoding dimension. Finally, I'll give one example of what can happen when the FOV is set inappropriately for both dimensions simultaneously. At the end of the process you should be able to differentiate the effects with ease. (See Note 1.)


Effects in the frequency encoding dimension

Below are two sets of EPIs of the same object - a spherical phantom - that differ only in the position of the readout FOV relative to the phantom. In the top image the readout FOV is centered on the phantom, whereas in the bottom image the FOV is displaced to the left, causing the left portions of the phantom signal in each slice to be neatly, almost surgically, removed:

Readout FOV centered relative to the phantom.

Readout FOV displaced to the left of the phantom, resulting in attenuation of the signal from the left edge of each slice.

Twitter. Damn.

@practiCalfMRI

I thought I could resist, I really did. (I've been off Facebook* for more than two years!) But when Neuroskeptic took the plunge in June I started thinking that maybe I should suck it up, too. I mean, Neuroskeptic blogs ten times more frequently than I do and he still has time to tweet.

Not sure exactly how it will go. I'm going to treat it as an experiment. I can guarantee that there won't be daily tweets let alone hourly ones. I'm not going to bring anyone's cellular network to its knees. But I do come across little things related to fMRI that aren't worth a full blog post. A micro-blog ought to fit the bill, eh? We'll see...

* Okay, so technically I am still on fb. I maintain an account so that I can post comments to websites, merge with other online media, etc. Turns out it's really, really hard not to have fb unless you don't mind registering separately for every online newspaper and music service yet invented. But I never actually look at my fb page, so I apologize if you have a "friend" request suspended somewhere in cyberspace.

Understanding fMRI artifacts: "Good" coronal and sagittal data

 
Front, back, side to side

Now that you have an appreciation of "good" axial EPI time series data we should be able to zip through a review of "good" coronal and sagittal EPIs. This isn't the post to get deep into the reasons why you might want to acquire these prescriptions instead of axial or axial-oblique slices, but here's a short list (and some music) for you to be going on with:

Pros

• coronal slices tend to exhibit less dropout of frontal and temporal lobes compared to axial slices.
• coronal slices might permit a smaller field-of-view and higher spatial resolution without signal aliasing than achievable with other prescriptions, assuming your gradient performance and other pulse sequence parameters can be driven sufficiently hard.
• sagittal slices may also show some improved signal in frontal and temporal lobes compared to axial slices, but the real benefit is the unique coverage afforded. You could acquire a single hemisphere, for instance; could be useful in a handful of situations. Alternatively, if you are interested in the whole brain, including cerebellum and perhaps even brain stem, these structures are naturally included in sagittal slices.
  
• sagittal slices tend to make the most common type of head motion - chin to chest rotations - an in-plane phenomenon which might lead to improved motion correction in post-processing.

There are, naturally, drawbacks to coronal and sagittal slices, just as there are for axial slices. I'll mention some of these in more detail below, as we consider the individual artifacts, but here's another brief list:

Cons

• safety limits on gradient switching (to avoid peripheral nerve stimulation) tend to force the phase encoding direction to be left-right for coronal slices, rendering the EPIs strongly asymmetric. While the absolute level of distortion may actually be very similar to that present in axial slices, the disruption of left-right symmetry can be a shock to your aesthetic sensibility.
• bizarre distortion is also a "feature" of sagittal slices where, as you'll soon see, the distortion can make the frontal lobes look like a duck's bill! But, as before, the absolute level of distortion may not be significantly different to that in axial slices; it's really the unnatural appearance that shocks us. (We ought to be just as outraged at the symmetric distortions in axial slices!)
• perhaps the biggest limitation to both coronal and sagittal prescriptions is the number of slices required to cover the entire brain in the given TR. Slicing along the longest axis of the brain, as done for coronal slices, is clearly the least efficient way to do it. The efficiency of sagittal slices falls somewhere between coronal and axial. And, of course, anything that leads to more (fixed width) slices means that TR might have to get longer. It all depends on your application.

Okay then, that's the introduction over with. Let's now put aside the justification for using one prescription over another and look at what constitutes "good" data in the case of coronal and sagittal slices. The features should be immediately recognizable from what you saw in the axial data of the last post.

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.

Understanding fMRI artifacts

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.

Physics for understanding fMRI artifacts: Part Twelve

Apologies for the lengthy delay getting this post out. New academic year, teaching, talks, etc. etc. Anyway, I hope that this opus will be the final post in the background physics series for the time being. I reserve the right to append further posts down the road, but with this post I hope you will be in a position to understand the origins of artifacts in real (EPI-based) fMRI data. So, after today we'll change tacks and start reviewing what "good" data should look like. First things first though. Time to put all your k-space knowledge to good use, and review the pulse sequence that the majority of us use for fMRI.


The Echo Planar Imaging (EPI) pulse sequence

In Part Ten we looked at a pulse sequence and its corresponding k-space representation for a gradient-recalled echo (GRE) imaging method. That sequence used conventional, or spin warp, phase encoding to produce the second spatial dimension of the final image. A single row of the k-space matrix was acquired per RF excitation, with successive rows of (frequency-encoded) k-space being sampled after stepping down (or up) in the 2D k-space plane following each new RF pulse.

One feature of the spin warp imaging scheme should have been relatively obvious: it's slow. Frequency encoding along kx is fast but stepping through all the ky (the phase-encoded) values is some two orders of magnitude slower, resulting in an imaging speed from tens of seconds (low resolution) to minutes (high resolution). That's not the sort of speed we need if we are to follow blood dynamics associated with neural events.

Instead of acquiring a single row of k-space per RF excitation - a process that is always going to be limited by the recovery time to allow the spins to relax via T1 processes - we need a way to acquire multiple k-space rows per excitation, in a sort of "magnetization recycling" scheme. Ideally, we would be able to recycle the magnetization so much that we could acquire an entire stack of 2D planes (slices) in just a handful of seconds. That's what echo planar imaging (EPI) achieves.

Gradient echo EPI pulse sequence

The objective with the EPI sequence, as for the GRE (spin warp) imaging sequence we saw in Part Ten, is to completely sample the plane of 2D k-space. That objective is unchanged. All we're going to do differently is sample the k-space plane with improved temporal efficiency. Then, once we have completed the plane we can apply a 2D FT to recover the desired image. Pretty simple, eh?

As before, sampling (data readout) need only happen along the rows of the k-space matrix, i.e. along kx. So we need a way to hop between the rows quickly, spending as much time as possible reading out signals under the frequency encoding gradients, Gx, and as little time as possible getting ready to sample the next row. EPI is the original recycled pulse sequence, so I'll color the readout gradient echoes in green:

The first four (and a half) gradient echoes in a gradient echo EPI pulse sequence.

To keep things simple I've omitted slice selection and indicated a 90 degree RF excitation; this could of course be any flip angle in practice. (See Note 1.) I've also shown just the first four (and a half) gradient echoes in the echo train. The full sequence repeats as many times as there are phase-encoded rows in the k-space matrix. A typical EPI sequence for fMRI might use 64 gradient echoes, corresponding to 63 little blue triangles in the train shown in the figure above. But for the example k-space plane below, the k-space grid is 16x16 so assume for the time being that the full echo train would consist of 15 little blue triangles separating eight positive Gx gradient periods and eight negative Gx gradient periods.