Can we separate real and apparent motion in QC of fMRI data?

 

A few years ago, Jo Etzel and I got into a brief but useful investigation of the effects of apparent head motion in fMRI data collected with SMS-EPI. The shorter TR (and smaller voxels) afforded by SMS-EPI generated a spiky appearance in the six motion parameters (three translations, three rotations) produced by a rigid body realignment algorithm for motion correction, such as MCFLIRT in FSL. The apparent head motion is caused by magnetic susceptibility variations of the subject's chest as he/she breathes, leading to a change in the magnetic field across the head which, in turn, adds a varying phase to the phase-encoded axis of the EPI. This varying phase then manifests as a translation in the phase-encoded axis. It's not a real motion, it's pseudo-motion, but unfortunately it is a real image translation that adds to any real head motion. I should emphasize here that this additive apparent head motion arises in conventional multi-slice EPI, too, but it's generally only when the TR gets short, as is often the case with SMS-EPI, that the apparent head motion can be visualized easily (as a spiky, relatively high frequency fluctuation in the six motion parameter traces). In EPI sampled at a conventional TR of 2-3 sec, there are only a small handful of data points (volumes) per breath for an average breathing rate of 12-16 breaths/minute and this leads to aliasing of most of the apparent head motion frequency. It may still be possible to see the spiky respiration frequency riding on the six motion parameters, but it's not always there as it is for TR much less than 2 seconds.

Once we'd satisfied ourselves we'd understood the problem fully, I confess I let the matter drop. After all, we have tools like MCFLIRT that try to apply a correction to all sources of head motion simultaneously, whether real or apparent. But now I'm wondering if we might be able to evaluate the real and apparent motion contributions separately, with a view to devising improved QC measures that can emphasize real head motion over the apparent head motion when it comes to making decisions on things like data scrubbing. Jo has been dealing with the appropriate framewise displacement (FD) threshold to use when including or excluding individual volumes. (See also this paper.)

Let's review one of the motion traces from my second 2016 blog post on this issue:

These traces come from axial SMS-EPI with SMS factor (aka MB factor ) of 6. The x axes are in seconds, corresponding to TR = 1 sec. (The phase-encoded axis is anterior-posterior, which is the magnet Y direction.) On the left is a subject restrained with only foam, on the right the same subject's head is restrained with a printed head case. During each run the subject was asked to take a deep breath and sigh on exhale every 30 seconds or so. We clearly see the deep breath-then-sigh episodes in both traces, regardless of the type of head restraint used. Yet it is also clear the apparent head motion, which is the high frequency ripple, dominates the Y, Z and roll traces on the left plot. On the right plot, the dominant effect of apparent head motion manifests in the Y trace, with a much reduced effect in the roll axis. Already we are seeing a slight distinction between the translations and rotations for apparent head motion. It looks like apparent head motion contributes more to translations than rotations, which makes sense given the physical origin of the problem. In which case, can we assume that by extension real head motion will dominate the rotations?

For now, let's assume that the deep breath-then-exhale episodes are producing considerable real head motion, in addition to the large apparent head motion spike from exaggerated chest movement. The left plot above shows that pitch, yaw and roll all characterize the six deep breaths readily. They are also visible in Z and X, but with considerably reduced magnitude. There's no clear effect in the Y trace which is dominated by the aforementioned apparent head motion. So far so good! When the head can actually move in the foam restraint, we have clear biases towards rotations for real head motion and translations for apparent head motion. 

What about the right plots? Real head motion is far harder to achieve because of the printed head case restraint. But we assume the apparent head motion is basically the same magnitude because it's chest motion, not head motion. So we might think of this condition as being a low (or lowest) real motion condition. As with the foam restraint on the left, we again see Y translations dominated by apparent head motion. The roll axis also displays considerable apparent head motion. And as for the foam restraint, the roll and pitch axes display something that may be real or apparent head motion for each of the deep breath-then-exhale periods. We can't be sure if the head (or the entire head case, or even the entire RF coil!) was really moving during each breath, but let's assume it was. If so, then for good mechanical head restraint we have the same rough biases as for foam restraint in our motion traces: real motion dominates rotations, apparent motion manifests mostly as translations.

Jo sees a similar distinction between real and apparent head motion in the motion parameter plots of her 2023 blog post. In her top plot, which she suggests is a low real motion condition, the apparent motion dominates Y and Z translations and the roll traces, exactly as my example above. Her second plot exhibits considerable real head motion. The apparent head motion is still visible as ripples on the Y and Z translation traces, but now it's clear the biggest changes arise in the three rotations and these changes are probably real head motion. Again, we have real motion dominating rotations while apparent motion manifests more in the translations.

Finally, let's consider Frew et al., who looked at head motion in pediatrics. Here's Figure 3 from their paper:

Using framewise displacement (FD), they show a transition from FD dominated by translations to FD dominated by rotations when considering low, medium and high (real) head motion subjects. Rotations and translations are both affected significantly in the medium movement group. Still, the trend here suggests that we might consider rotations alone as an index of real head motion if, as suggested above, apparent head motion contributes mostly to translations.

So, what might we do to separately evaluate real and apparent head motion? This is where you come in. I only have one starting idea, and that's to shift to considering FD using only rotations, rather than rotations and translations, when setting thresholds for the purposes of QC and scrubbing. Based on what I've presented here, we might be able to set a threshold for FD(rotations only) that will capture most of the real head motion and have a much reduced dependency on apparent head motion. This measure could help avoid mischaracterizing large apparent head motions as events to reject when they are inherently fixable with MCFLIRT and similar. (Real head motion produces a big spin history effect and likely introduces non-linear distortions in the images.) Whether the reverse is true - that is, whether FD(translations only) captures most of the apparent head motion and a reduced contribution from real head motion - I leave as an exercise for another day, but my suspicion is that it is not. Put another way, I think the focus should be on using the rotations to capture and evaluate real head motion. Pooling translations and rotations in measures like FD may be complicating the picture for us.

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Monitoring gradient cable temperature

While the gradient set is water-cooled, the gradient cables and gradient filters still rely upon air cooling in many scanner suites, such as mine. In the case of the gradient filters, the filter box on my Siemens Trio came with an opaque cover, which we replaced with clear plastic to allow easy inspection and temperature monitoring with an infrared (IR) thermometer:

The gradient filter box in the wall behind my Siemens Trio magnet. It's up at ceiling height, in the lowest possible stray magnetic field. The clear plastic cover is custom. The standard box is opaque white.

Siemens now has a smoke detector inside the gradient filter box, after at least one instance of the gradient filters disintegrating with excess heat. Still, a clear inspection panel is a handy thing to have.

The gradient cables between the filter box and the back of the magnet can also decay with use. If this happens, the load experienced by the gradient amplifier changes and this can affect gradient control fidelity. (More on this below.) The cables can be damaged by excess heat, and this damage leads to higher resistance which itself produces more heating. A classic feedback loop!

The Fluke 561 IR thermometer and a K type thermocouple, purchased separately.

Motion traces for the respiratory oscillations in EPI and SMS-EPI

This is a follow-up post to Respiratory oscillations in EPI and SMS-EPI. Thanks to Jo Etzel at WashU, you may view here the apparent head motion reported by the realignment algorithm in SPM12 for the experiments described in the previous post. Each time series is 200 volumes long, TR=1000 ms per volume. The realignment algorithm uses the first volume in each series as the template. The motion is plotted in the laboratory frame, where Z is the magnet bore axis (head-to-foot for a supine subject), X is left-right and Y is anterior-posterior for a supine subject.

In the last post I said that there were five total episodes of a deep breath followed by sigh-like exhale, but actually the subject produced a breath-exhale on average every 30 seconds throughout the runs. (This was a self-paced exercise.) Thus, what you see below (and in the prior post) has a rather large degree of behavioral variability. Still, the main points I made previously are confirmed in the motion traces. I'll begin with the axial scan comparison. Here are the motion parameters for the MB=6 axial acquisition with standard foam head restraint (left) versus the custom printed restraint (right):

MB=6, axial slices. Left: foam restraint. Right: custom 3D printed headcase restraint

The effect of the custom restraint is quite clear. The deep breath-then-sigh episodes are especially apparent when using only foam restraint. Note the rather similar appearance of the high frequency oscillations, particularly apparent in the blue (Y axis) traces between the two restraint systems, suggesting that the origin of these fluctuations is B0 modulation from chest motion rather than direct mechanical motion of the head. We cannot yet be sure of this explanation, however, and I am keeping an open mind just in case there are small movements that the custom head restraint doesn't fix.

Respiratory oscillations in EPI and SMS-EPI

tl;dr   When using SMS there is a tendency to acquire smaller voxels as well as use shorter TR. There are three mechanisms contributing to the visibility of respiratory motion with SMS-EPI compared to conventional EPI. Firstly, smaller voxels exhibit higher apparent motion sensitivity than larger voxels. What was intra-voxel motion becomes inter-voxel motion, and you see/detect it. Secondly, higher in-plane resolution means greater distortion via the extended EPI readout echo train, and therefore greater sensitivity to changes in B0. Finally, shorter TR tends to enhance the fine structure in motion parameters, often revealing oscillations that were smoothed at longer TR. Hence, it's not the SMS method itself but the voxel dimensions, in-plane EPI parameters and TR that are driving the apparent sensitivity to respiration. Similar respiration sensitivity is obtained with conventional single-shot EPI as for SMS-EPI when spatial and temporal parameters are matched.

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The effects of chest motion on the main magnetic field, B0, are well-known. Even so, I was somewhat surprised when I began receiving reports of likely respiratory oscillations in simultaneous multi-slice (SMS) EPI data acquired across a number of projects, centers and scanner manufacturers. (See Note 1.) Was it simply a case of a new method getting extra attention, revealing an issue that had been present but largely overlooked in regular EPI scans? Or was the SMS scheme exhibiting a new, or exacerbated, problem?

Upper section of Fig. 4 from Power, http://dx.doi.org/10.1016/j.neuroimage.2016.08.009, showing the relationship between apparent head motion (red trace) reported from a realignment algorithm and chest motion (blue trace) recorded by a respiratory belt. See the paper for an explanation of the bottom B&W panel.

QA for fMRI, Part 3: Facility QA - what to measure, when, and why

As I mentioned in the introductory post to this series, Facility QA is likely what most people think of whenever QA is mentioned in an fMRI context. In short, it's the tests that you expect your facility technical staff to be doing to ensure that the scanner is working properly. Other tests may verify performance - I'll cover some examples in future posts on Study QA - but the idea with Facility QA is to catch and then diagnose any problems.

We can't just focus on stress tests, however. We will often need more than MRI-derived measures if we want to diagnose problems efficiently. We may need information that might be seem tangential to the actual QA testing, but these ancillary measures provide context for interpreting the test data. A simple example? The weather outside your facility. Why should you care? We'll get to that.


An outline of the process

Let's outline the steps in a comprehensive Facility QA routine and then we can get into the details:

• Select an RF coil to use for the measurements. 
• Select an appropriate phantom.
• Decide what to measure from the phantom.
• Determine what other data to record at the time of the QA testing.
• Establish a baseline.
• Make periodic QA measurements.
• Look for deviations from the baseline, and decide what sort of deviations warrant investigation.
• Establish procedures for whenever deviations from "normal" occur.
• Review the QA procedure's performance whenever events (failures, environment changes, upgrades) occur, and at least annually.

In this post I'll deal with the first six items on the list - setting up and measuring - and I'll cover analysis of the test results in subsequent posts.

QA for fMRI, Part 2: User QA

Motivation

The majority of "scanner issues" are created by routine operation, most likely through error or omission. In a busy center with harried scientists who are invariably running late there is a tendency to rush procedures and cut corners. This is where a simple QA routine - something that can be run quickly by anyone - can pay huge dividends, perhaps allowing rapid diagnosis of a problem and permitting a scan to proceed after just a few minutes' extra effort.

A few examples to get you thinking about the sorts of common problems that might be caught by a simple test of the scanner's configuration - what I call User QA. Did the scanner boot properly, or have you introduced an error by doing something before the boot process completed? You've plugged in a head coil but have you done it properly? And what about the magnetic particles that get tracked into the bore, might they have become lodged in a critical location, such as at the back of the head coil or inside one of the coil sockets? Most, if not all, of these issues should be caught with a quick test that any trained operator should be able to interpret.

User QA is, therefore, one component of a checklist that can be employed to eliminate (or permit rapid diagnosis of) some of the mistakes caused by rushing, inexperience or carelessness. At my center the User QA should be run when the scanner is first started up, prior to shut down, and whenever there is a reason to suspect the scanner might not perform as intended. It may also be used proactively by a user who wishes to demonstrate to the next user (or the facility manager!) that the scanner was left in a usable state.

QA for fMRI, Part 1: An outline of the goals

For such a short abbreviation QA sure is a huge, lumbering beast of a topic. Even the definition is complicated! It turns out that many people, myself included, invoke one term when they may mean another. Specifically, quality assurance (QA) is different from quality control (QC). This website has a side-by-side comparison if you want to try to understand the distinction. I read the definitions and I'm still lost. Anyway, I think it means that you, as an fMRIer, are primarily interested in QA whereas I, as a facility manager, am primarily interested in QC. Whatever. Let's just lump it all into the "QA" bucket and get down to practical matters. And as a practical matter you want to know that all is well when you scan, whereas I want to know what is breaking/broken and then I can get it fixed before your next scan.


The disparate aims of QA procedures

The first critical step is to know what you're doing and why you're doing it. This implies being aware of what you don't want to do. QA is always a compromise. You simply cannot measure everything at every point during the day, every day. Your bespoke solution(s) will depend on such issues as: the types of studies being conducted on your scanner, the sophistication of your scanner operators, how long your scanner has been installed, and your scanner's maintenance history. If you think of your scanner like a car then you can make some simple analogies. Aggressive or cautious drivers? Long or short journeys? Fast or slow traffic? Good or bad roads? New car with routine preventative maintenance by the vendor or used car taken to a mechanic only when it starts smoking or making a new noise?

Using someone else's data

There was quite a lot of activity yesterday in response to PLOS ONE's announcement regarding its data policy. Most of the discussion I saw concerned rights of use and credit, completeness of data (e.g. the need for stimulus scripts for task-based fMRI) and ethics (e.g. the need to get subjects' consent to permit further distribution of their fMRI data beyond the original purpose). I am leaving all of these very important issues to others. Instead, I want to pose a couple of questions to the fMRI community specifically, because they concern data quality and data quality is what I spend almost all of my time dealing with, directly or indirectly. Here goes.


1.  Under what circumstances would you agree to use someone else's data to test a hypothesis of your own?

Possible concerns: scanner field strength and manufacturer, scan parameters, operator experience, reputation of acquiring lab.

2. What form of quality control would you insist on before relying on someone else's data?

Possible QA measures: independent verification of a simple task such as a button press response encoded in the same data, realignment "motion parameters" below/within some prior limit, temporal SNR above some prior value.


If anyone has other questions related to data quality that I haven't covered with these two, please let me know and I'll update the post. Until then I'll leave you with a couple of loaded comments. I wouldn't trust anyone's data if I didn't know the scanner operator personally and I knew first-hand that they had excellent standard operating procedures, a.k.a. excellent experimental technique. Furthermore, I wouldn't trust realignment algorithm reports (so-called motion parameters) as a reliable proxy for data quality in the same way that chemicals have purity values, for instance. The use of single value decomposition - "My motion is less than 0.5 mm over the entire run!" - is especially nonsensical in my opinion, considering that the typical voxel resolution exceeds 2 mm on a side. Okay, discuss.


UPDATE 13:35 PST

Someone just alerted me to the issue of data format. Raw? Filtered? And what about custom file types? One might expect to get image domain data, perhaps limited to the magnitude images that 99.9% of folks use. So, a third question is this: What data format(s) would you consider (un)acceptable for sharing, and why?

Rare intermittent EPI artifacts: Spiking, sparking and arcing

 
Whatever you call them - spikes, sparks or arcs - the presence of unwanted electrical discharges during data acquisition can have a dramatic effect on the appearance of your EPIs and will likely result in poor or unusable data. (See Note 1.) There are many potential sources of unwanted electrical discharges - what I shall refer to as spikes for the rest of this post, regardless of the origin - in and around an MRI scanner. They can arise from within the scanner itself, or from items in the magnet room, or from items of clothing on a subject who hasn't been screened quite as thoroughly as he might have been.

Before we get to the sources, however, let's take a look at what we're talking about. Take a look at this mosaic of EPIs:

See the problem? No? Exactly! As I have mentioned several times in the past, many artifacts are best (or only) seen once the background level is brought up. Like this:

Aha! We clearly see the artifacts in this view: strange, variable patterns across entire slices.

Now, it isn't always necessary to crank the background intensity up to be able to see the effects of spikes, as we will see below. But as a general rule, the very first signs of spiking will be quite subtle and will likely be hidden away down in the noise with the N/2 ghosts and all the other crud. This is when you want to catch them, before they become intense and wreck your experiment. So, just to reinforce the point, take a look at this video and see if you can detect any anomalies in the images:

Terminology change: characterizing EPI artifacts

After considering the rest of the topics that I want to cover in the series of posts on EPI artifacts, I've decided to change the terms "static" and "dynamic" to "persistent" and "intermittent," respectively. I think the new terms better reflect the dominant temporal character of each artifact. The idea is to sort them based on whether they are likely to plague every frame of an EPI time series, or come and go.

Take external RF interference, for example. Say you fail to close properly the RF-sealed door to the magnet room and the door opens slightly during your scan, leading to RF contamination from "environmental" sources. In this instance the RF interference itself isn't likely to be static - it will vary with whatever sources of RF happen to be in your scanner environment - but it will persist (at some level) until the door is closed. With a good eye or some appropriate diagnostics it would be possible to show the persistence of the problem throughout an acquisition. Contrast this situation with a static electrical discharge somewhere within the scanner room; tiny sparks that cause a broad range of electromagnetic frequencies, including radiofrequencies. These can arise if the humidity of the magnet room air becomes too low. Depending on the source of the sparks, the humidity, etc. you might find that only one or two TRs of a time series are contaminated, or you could find the entire time series is affected. I will therefore characterize static electrical discharges as an intermittent artifact.

Pedants will spot that the first example can be modulated by the position of the magnet room door, rendering the artifact intermittent, while in the second example the propensity for static electrical discharges will persist as long as the source exists, while the humidity remains low, etc. So, yeah, in some ways the distinctions I'm making are subjective. FMRI, like life, is complicated! Still, I'm hopeful that a more practical characterization of artifacts will assist you in differentiating and diagnosing them when it matters: during your experiment. And once you're an expert you will find it easy to comprehend the nuances of temporal behavior, when my artificial distinctions will be all but irrelevant to you.

So, there you have it. I'll be going back to edit the existing posts in this series over the next couple of days. Apologies for any confusion the switch creates.

Common persistent EPI artifacts: Abnormally high N/2 ghosts (2/2)

In the previous post I covered sources of persistent ghosts that arise as a result of some property of the subject, such as the orientation of the subject's head in the magnet. These are what I'm categorizing as subject-dependent effects. In this post I will review the most common sources of persistent ghosts attributable to the scanner, either from an intrinsic property that you might encounter inadvertently, or from mis-setting a parameter in your protocol. As I mentioned last time, I am restricting the discussion to factors that you have some control over as the scanner operator. Ghosts that arise because of a scanner installation error, such as poor gradient eddy current compensation or inaccurate gradient calibration, are issues for your facility physicist and/or your service engineer.

Scanner-dependent conditions:

Rotated read/phase encode axes 

GLOBAL - affects all slices to some extent.

This is an insidious problem that we could categorize as pilot error, except that it's very easily encountered without realizing it. When you set up your slice prescription you are primarily concerned with capturing all those brain regions you need for your experiment. Or you might be concerned with setting a particular slice angle relative to the brain anatomy, e.g. parallel to AC-PC. Now, if the subject's head is precisely aligned such that the read and phase encode axes of your imaging plane are matched perfectly with the gradient set axes (i.e. with the magnet's frame of reference), then for axial slices the readout dimension will be attained using pure X gradient (subject's left-right) while the phase encode dimension uses pure Y gradient (subject's anterior-posterior). (See Note 4 in the post on "Good" coronal and sagittal data for an explanation of why the gradients are established this way, for subject safety/comfort reasons.) But, if the head is twisted slightly, or you're a little sloppy with your slice positioning, then it is quite easy to have a readout gradient that is mostly X with a little bit of Y, and a phase encoding gradient that is mostly Y with a little bit of X. This in-plane rotation ought not be a problem if the X and Y gradients performed equivalently, but they're only similar and not identical. There tend to be small differences in the response time of the gradients, which means that when the scanner tries to drive the read gradient to its desired k-space trajectory, one component (say the X component) can respond faster than the other. This produces a slight mismatch between the target (ideal) k-space trajectory and the trajectory that's actually achieved by the gradients, thereby leading to a source of zigzags that will produce N/2 ghosting.

Now the good news. You've got to rotate the image plane by quite a lot before the ghosting starts to become apparent. It's common to have rotations of 1-2 degrees and these will generate almost no additional ghosting. Once the rotation gets much larger than 5 degrees (depending on the specifics of your scanner) then you might start to see additional ghosting. Below on the left is an ideal prescription, while on the right I've intentionally rotated the image plane by 8 degrees, leading to a small but noticeable increase in ghost level:

(Click to enlarge.)

Common persistent EPI artifacts: Abnormally high N/2 ghosts (1/2)

 
In this and a subsequent post I am going to cover some common situations when the N/2 ghosts can become abnormally high, i.e. higher than it is possible to achieve with comparatively small tweaks to the setup. For now I am going to restrict the discussion to temporally static, or persistent, ghosts. Furthermore, I will restrict the discussion to situations over which you can exert some control, usually through the subject setup and via EPI parameter selection. I'll cover the origins of dynamic ghosts later on in this series, once you've got a better grasp of the common persistent ghosting sources and are in a position to differentiate between a source that is intermittent and a (persistent) ghost that is being modulated by subject motion.

Before we get into the different experimental conditions that can lead to abnormally high ghosting, it is important that you are familiar with the reason why N/2 ghosts arise in EPI in the first place. So, if the following section sounds like Swahili (and you don't ordinarily speak Swahili) then I would encourage you to spend twenty minutes reviewing the section on N/2 ghosts in PFUFA Part Twelve before continuing here.

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.

Shim and gradient heating effects in fMRI experiments

Another week, another tangent. At least this one is directly related to the artifacts that I promise to get back to soon!

In this post I will review the nature and typical magnitudes of heating effects in a scanner being used for fMRI. Ever wondered why you sometimes observe discontinuities, or 'steps,' in a time series comprising the concatenation of multiple blocks of EPI data? What causes these discontinuities? Are they a problem for fMRI? And are there ways to reduce or eliminate these discontinuities at the acquisition stage? To begin with, some background.

Electrical energy in, thermal and vibrational energy out

When you run the gradients to generate images, a lot of heat is produced through vibrations (friction) of the gradient coils - the Lorentz forces that result from putting electrical current through copper wires immersed in a magnetic field - as well as through direct (resistive) electrical mechanisms. Much of that heat is removed via water cooling inside the gradient set. Water typically enters at about 20 C and may exit the scanner as high as 30 C. Modern gradient designs are pretty efficient at removing heat from the gradient coil. (I've done throwaway tests on my Siemens Trio that suggest the steady state temperature of the return cooling water is achieved after about 15 minutes of continuous scanner operation.) But - and this is the crux of this post - the heat imparted to the scanner isn't removed at precisely the same rate that it is being produced. In other words, the scanner is unlikely to be in a truly steady thermal state while you're using it.

Physics for understanding fMRI artifacts: Part Two

We continue our review of the key principles of NMR with another video courtesy of Paul Callaghan. In it, Prof Callaghan introduces the idea of bulk magnetization; the thing that you induce in your subject's brain (which is ~80% water) when you slide the subject into the magnet, and which you then manipulate to produce images.

In the video shown previously (see Part One), Prof Callaghan introduced the phenomenon of resonance and demonstrated it with a spinning wheel. In MRI the resonance frequency is governed by a simple proportionality, as given in the Larmor equation. We will use this equation later on to establish different frequencies across an object, thereby encoding spatial information and yielding, ultimately, an image of that object. We will also see how the Larmor equation is used in the k-space formalism, so make sure you have a good understanding of this deceptively simple yet intuitively valuable equation.

Physics for understanding fMRI artifacts: Part One

This is the first in a series of posts in which I will attempt to provide you with the means to diagnose the manifold artifacts that plague fMRI. These artifacts can be inherent, e.g. distortion and dropout, or the consequences of a hardware issue, e.g. RF interference or gradient spiking, or may arise from your subject, e.g. cardiac pulsatility and head movement. However the artifacts arise, the aim is simple: by providing you the means to recognize what is going wrong in your experiment you may be able to discern the root cause, then remedy the problem and salvage your data.

It's a lot like plane spotting, only less fun.

The psychologists amongst you would be able to lecture me for days on category learning. Well, that's what this is. Except that before we can differentiate between one artifact and another we must first understand how EPI is designed to work in an ideal situation. (You don't need to know how planes fly to categorize them, apparently.) And to comprehend artifacts it's important for you to have a reasonable appreciation of the underlying physics, especially the concept of k-space. Here's the loose plan:

Comparing fMRI protocols

In a December post I suggested a decision tree that can be used for deciding whether or not to adopt a new (or new to you) method or device for your next fMRI experiment. In essence it was a form of risk analysis. But it isn't only new methods that need to be evaluated carefully before you embark on an experiment. What about the plethora of parameters that characterize even the simplest combination of single-shot EPI with whatever passes for standard hardware on your scanner? RF coil selection, echo spacing, TE, slice thickness, slice gap, TR, RF flip angle... all can have profound effects on your data. In the absence of a compelling paper that strongly implicates a particular protocol for your experiment, how do you make an informed choice before you proceed?

Functional signal and physiologic noise

In an ideal world you would be able to run a pilot experiment that robustly activates all the brain regions you're interested in. This approach can work well if all of your regions of interest lie in primary cortex: responses to stimuli are typically robust, baselines are fairly easily established, and simple stimuli can often be used to assess regional responses. But many contemporary experiments don't lend themselves to extensive piloting; actually doing the entire experiment may be the only way to assess whether regions A, B and C are activated at all, let alone more or less with a particular parameter setting! Instead, we may have to focus our attention on the noise properties of the tissue.