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!

Thursday, April 13, 2017

Major sources of apparent head motion in fMRI data


As I mentioned yesterday, there is a tendency when reviewing the output of a volume registration ("motion correction") algorithm to attribute all variations to real head motion. But, as was demonstrated last October, the magnetic susceptibility of the chest during breathing produces shifts in the magnetic field that vary spatially across the head, producing translations and shearing in EPI data that the volume registration algorithm can't distinguish from real head motion. Here I want to quickly review other major mechanisms by which we can get apparent head motion.

Let's start with contributions to real head motion. These include slow compression of foam designed to restrain the head, relaxation or tension of neck muscles, swallowing, fidgeting and the like. Printed head cases, bite bars and other restraint systems are of use here. Then there are body motions, including the extremities, that produce movement of the head via the neck. This is why you should instruct your subjects not to move at all during the scan. Telling a subject he shouldn't move his head is tantamount to saying that moving his feet is okay, and it's not. Subjects should move, e.g. to scratch or stretch, only when the scanner is silent.

Also included in the mechanical motion category is respiratory chest motion that couples unavoidably to the head because of that pesky neck thing. Pulsations of the brain with the cardiac cycle are another source of unavoidable direct motion in the organ of interest. The latter is real brain motion, of course.

Next, body motions (including from respiration) can produce head movement in the magnetic field via instability of the patient bed. Back in the early 2000s we had a Varian 4 T scanner. We had to construct rollers to catch and support the bed sled in the magnet bore because we had a cantilevered bed that deflected like a springboard otherwise. Every tiny movement of the subject caused the bed sled to bounce. For stability we want a strongly coupled system - subject to bed, bed to gradients/magnet - and we need to avoid any relative movement between them. I was reminded of this mechanism again recently. It's something to keep in mind as we work on respiratory instabilities because I note that my Trio has a bed cantilevered on the magnet face whereas Prisma scanners have a bed supported on the floor in front of the magnet. The latter should be a lot more stable, provided the bed has a solid foundation underneath it.

So far all the mechanisms I've considered have had a direct mechanical connection between the source of the motion and the brain. Chest motion can also affect the magnetic field via changing magnetic susceptibility from the air-filled lungs, as previously demonstrated. This is a through-space mechanism. In principle, movement of the extremities or any other part of the body (or other equipment in the bore) might also produce perturbation of the magnetic field across the head via magnetic susceptibility, but my intuition is that this would be a minor contributor to overall instability compared to the effects from the chest.

A well-known motion-like effect arises from thermal drift in the magnet. The gradients get warm with use and over time this causes drift in the magnetic field, e.g. via passive shimming iron that doesn't have the water cooling of the gradient set. Re-shimming can offset some of the effects of this mechanism between runs, but not within a run. When viewed from the perspective of your agnostic volume realignment algorithm, thermal drifts appear a lot like slow (real) head movements, e.g. as foam compresses or neck muscles relax. Re-shimming between runs helps with both, but I'm afraid it doesn't do anything within a run. De-trending is usually used to good effect here.

There are doubtless other sources of instability that can manifest as apparent head motion - anything that causes shifts in the on-resonance frequency during an EPI time series will do it - but here I've covered the main mechanisms of concern. Given robust head restraint to mitigate most of the direct head motion mechanisms (except brain pulsations), it seems that the next largest instabilities to tackle are the respiratory motion mechanisms. We have three to work on: residual direct motion through the neck, magnetic susceptibility of the chest, and the possible deflection of the patient bed.


Wednesday, April 12, 2017

"Power plots" of respiratory effects in EPI


This will be brief, a simple demonstration of the sort of features visible in a "Power plot" of an EPI time series. The goal is to emphasize that chest motion produces apparent head motion effects in typical analyses. Here the subject's head was held very firmly in the 32ch coil of my Siemens Trio using a custom printed head case. See the posts from October last year for more details. In this test the subject inhaled to near maximum and exhaled immediately, repeating the procedure every 30 seconds or so in a self-paced manner. The subject breathed normally otherwise. Critically, note that no breaths were held.


What we see are two striking features. First, there is banding with a period of approx 30 seconds, and the bright bands correspond with apparent head movement reported as framewise displacement (FD) in the top red trace. (TR is 1700 ms.) Some of this may be real head movement, but a lot arises from chest displacements modulating the magnetic field. This is the feature I want to emphasize. We need to be aware that not all sources of frame-to-frame variation reported by a volume registration (aka motion correction) algorithm are necessarily actual head motion. Last October I showed in a series of simple demonstrations how chest motion produces shearing and translations of EPI signals in a manner consistent with perturbation of magnetic field, rather than head motion per se. It's important for you to distinguish these two phenomena because the volume registration algorithm cannot differentiate them. It does its best to match volumes no matter the source of differences.

The second feature in the plots above I'm not going to get deep into here. It's for another day. But it's pretty hard to miss the dark bands that follow tens of seconds after each bright band. Notice that the dark bands don't tend to coincide with increased FD. That is, the origin of the dark bands isn't actual or apparent head motion but something else. They come from changes in BOLD signal as the arterial CO2 changes. This is the part of the "physiologic noise" that people try to model with things like RETROICOR and RVT, or from end-tidal CO2 measurements. Here, the perturbation in BOLD signal is driven by the strange breathing task, but it's not motion or motion-like. It's real physiology in the brain.

That's all for now! More posts on this stuff in the coming weeks.



Friday, December 30, 2016

Use of split slice GRAPPA (aka Leak Block) for SMS-EPI reconstruction


Accurate separation of the simultaneously acquired slices is one of the bigger limitations of the SMS-EPI method, compared to the processing used for conventional multislice EPI. The default SMS reconstruction, as used in my two introductory posts on the SMS sequences from CMRR (MB-EPI) and MGH (Blipped CAIPI), is a slice dimension adaptation of the GeneRalized Autocalibrating Partial PArallel (GRAPPA) method that was originally applied in-plane to acceleration of the phase encoding direction. It's not essential to understand the GRAPPA method applied in-plane for the purposes of understanding this post or for SMS reconstruction more generally. But if you're curious I wrote a brief introduction to in-plane GRAPPA in 2011. That post was specifically concerned with motion sensitivity of (in-plane) GRAPPA. I'll be looking in more detail at the motion sensitivity of SMS in a future post. In this post I want to compare the standard SMS reconstruction - what is generally termed Slice GRAPPA - with an alternative known as Split Slice GRAPPA. The latter option is termed "Leak Block" in the CMRR pulse sequence, MB-EPI.


What's the concern?


CMRR's parameter nomenclature offers a strong clue to the problem. In conventional EPI reconstruction we use a 2D Fourier transform (FT) which produces some amount of ringing. We also use slices that have some degree of cross-talk to neighboring slices, arising out of the limitations of frequency selectivity. So, while we think of voxels as perfect little rectangles or cubes, in reality they are blurry beasts that spread their signal into adjoining voxels because of a non-rectangular point-spread function (PSF). The dimensions we assign a voxel are entirely nominal.

With SMS we have a broader spatial problem than just non-cubic PSF. Separation of the simultaneous slices can leave signal in an incorrect position that is quite some distance from where it is supposed to be. It's a longer length scale error than the simple PSF of a voxel. Let's suppose we acquire four 2 mm slices simultaneously, 84 total slices. In one SMS acquisition we will have four slices separated by one quarter of the total slice dimension extent of 168 mm, or about 42 mm (assuming no additional inter-slice gap). Do a quick thought experiment. Imagine that in the first slice there is a very strong activation and nothing in the other three. If there is a large residual spatial error arising from poor SMS separation then we might start seeing this activation projected 4.2, 8.4 or even 12.6 cm from where it should be! And how would we know that the distant activation sites were erroneous?

This slice leakage, as it's usually called in the literature, may be strongest for simultaneously acquired neighbors but may extend throughout the slice dimension, between simultaneously acquired slices that might be quite far apart in anatomical space. And, as the thought experiment illustrates, one might assume that distant leakage would be harder to spot than the conventional cross-talk between successively acquired slices in conventional multislice EPI, or errors arising from the PSF more generally. The PSF can usually be interpreted as a local phenomenon, with errors decreasing monotonically from a voxel. Not so with SMS slice separation, meaning there is more risk of interpreting a false positive remote from the true activation site.

At this point we can recognize that reducing leakage is a noble, perhaps essential, goal. As usual with MRI, however, there's a catch. Reducing leakage using the Split Slice GRAPPA reconstruction may come at the cost of increasing in-plane artifacts. The overall (total) artifact level might be higher, too. I'll go into these issues in some detail below. The goal of this post is to perform a rudimentary assessment of the artifacts and determine the circumstances when Split Slice GRAPPA might be preferred over the conventional Slice GRAPPA reconstruction. For the CMRR sequence this amounts to whether or not to enable the Leak Block option.

Thursday, October 13, 2016

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.

Friday, October 7, 2016

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.

__________________

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.

Wednesday, June 29, 2016

Starting points for SMS-EPI at 3 T: Part II


In an earlier post I presented three starting protocols for the CMRR version of SMS-EPI, referred to as the MB-EPI sequence here. I'll use italics to indicate a specific pulse sequence whereas SMS-EPI, no italics, refers to the family of simultaneous multi-slice methods. In this post I'll develop a similar set of three starting protocols for the Massachusetts General Hospital (MGH) version of SMS-EPI, called Blipped-CAIPI. I'm going to build upon the explanations of the last post so please cross reference for parameter explanations and background.

As for the previous post there are several things to bear in mind. This series is Siemens-centric, specifically Trio-centric. While many of the concepts and parameter options may apply to other platforms there will be minor differences in parameter naming conventions and, perhaps, major differences in implementation that you will need to consider before you proceed. For Siemens users, I am running aging software, syngoMR version B17A. The age of the software and the old reconstruction board on the scanner means that you can expect to see much faster reconstruction on a newer system. I hope, but cannot guarantee, that the actual image quality and artifact level won't differ massively from a Trio running VB17A to a new Prisma running VE11C. I'll keep you updated as I learn more.


Preliminaries

As before, for this post I am going to be using a 32-channel receive-only head coil. The SMS-EPI sequences can be made to work with a 12-channel coil but only in a reduced fashion because the 12-channel coil has minimal receive field heterogeneity along the magnet z axis - the struts run parallel with the magnet axis except at the coil's rear, where they converge - and generally we want to do axial slices (along z) for fMRI. I don't yet know whether SMS-EPI would work well on the 20-channel head/neck coil on a Prisma, it's something I hope to investigate in the near future. But a 64-channel head/neck coil on a Prisma will definitely work for SMS-EPI. Better or worse than a 32-channel coil on a Prisma? I have no idea yet.

The Blipped-CAIPI sequence version 2.2 was obtained through a C2P (Core Competence Partnership) with MGH. Installation was a breeze: a single executable to port to the scanner and one click, done. The development team offers an informative but brief 7-page manual which will be useful to anyone who has read the SMS-EPI literature and has a basic understanding of how SMS works. It's not a starting point for everyday neuroscience, however. The manual mentions a .edx (protocol) file as a starting point for 2, 2.5 and 3 mm resolution scans, but in the file I downloaded for VB17A the contents didn't include it. Perhaps contact MGH if you are on another software version and you'd like a .edx file rather than building your own protocol, e.g. by recreating what you see here.

Tuesday, June 28, 2016

Exploiting Tanzania


So a massive helium reserve may have been found in Tanzania's Rift Valley. Wonderful. All the western headlines this morning have put a typically western spin on it. Hurrah! We are saved! We get to go plunder a foreign place again for what we need to save our own lives! Before we get too carried away with ourselves, let's take a few seconds to think about a few things. Like, say, how many MRI scanners are there in Tanzania right now? How many Tanzanian lives will be saved? Anyone care to estimate? This scanner in Dar es Salaam makes headlines when it breaks down.



What about the wildlife in Tanzania? Will lives be saved there, too? Note the concentration of national parks and game reserves in and around the Rukwa region of Tanzania. Now, I'm not intimately familiar with how helium gas is extracted, concentrated or liquefied but I'm going to guess that some of it has to be done where the gas is found. Even if the gas doesn't just float conveniently into collection chambers instead of needing some sort of gas forcing process (We love fracking, right?) and miles and miles of pipelines, it's a fair assumption that there will be massive energy needs to liquefy it. Then the cryogenic liquid helium must be transported. So we'll need roads, maybe an airport for the suits to get in and out quickly, and perhaps a railway to move the product to a sea port. Or we could just push the gas down a long pipe to the coast where it could be liquefied, then transported abroad. This is all going to be great news for African nature, I'm sure of it!