Quench!!!

I was persuaded by Tobias Gilk to post a video of the quench of Berkeley's old 4 T magnet, a fairly momentous event that a lot of people have enjoyed watching in private (whether they were absent or witnessed it live). The quench happened back in 2009. We didn't publicize the video at the time because we didn't want a bunch of know-nothings accusing us of wasting resources. (See the FAQ in the video comments if you want to know what happened to the magnet - we turned it into a mock scanner - and why we didn't try to recover the helium.) But there comes a time when the value to others becomes greater than the annoyance of poorly informed trolls venting their spleens on YouTube. So here it is, finally:

In case you missed seeing some of our antics in the couple of days leading up to the quench, here's that video, too:

And finally, while uploading the most recent video I tripped over another quench video from what looks and sounds like some Scandinavians: (I'm not even going to guess between Finland, Denmark, Norway, Sweden, Iceland,...)

Looks like these guys had as much fun as we did! What's really clear in their tests is the oscillation of magnetic objects between the regions of peak gradient at either end of the magnet - a couple of feet out from the faces of the magnet at either end, the magnetic field and cryostat being symmetrical. The speed of movement is sufficiently slow at 1.5 T to see things clearly, versus the crazy violent movement of objects in the 4 T field. They have better music, too.

We're arXiving! (Another post on GRAPPA.)

In another move to accelerate the development of methods for neuroimaging applications, some colleagues and I recently decided to abandon a second attempt to publish a paper in traditional journals and opted for the immediacy of arXiv instead. (Damn, it feels good to be free of reviewers claiming "What problem? I don't see why a solution is even needed?" Whatever.) We've got another paper coming out on arXiv in a few days, too, although in this case we are exploring the possibility of a simultaneous submission to IEEE Trans Med Physics since it allows such tactics, and my colleagues in "real" physics do this all the time. Whether or not the IEEE submission happens the material will be out there in the world, naked, for all to view and poke at. Isn't this how science is supposed to work? I love it!

Anyway, for today, here's the skinny on the arXiv submission from August (which I inadvertently forgot to hawk on this blog even after tweeting it):

http://arxiv.org/abs/1208.0972

(Get a PDF fo' free via the link.)

Simultaneous Reduction of Two Common Autocalibration Errors in GRAPPA EPI Time Series Data

 

D. Sheltraw, B. Inglis, V. Deshpande, M. Trumpis *

(Submitted on 5 Aug 2012)

The GRAPPA (GeneRalized Autocalibrating Partially Parallel Acquisitions) method of parallel MRI makes use of an autocalibration scan (ACS) to determine a set of synthesis coefficients to be used in the image reconstruction. For EPI time series the ACS data is usually acquired once prior to the time series. In this case the interleaved R-shot EPI trajectory, where R is the GRAPPA reduction factor, offers advantages which we justify from a theoretical and experimental perspective. Unfortunately, interleaved R-shot ACS can be corrupted due to perturbations to the signal (such as direct and indirect motion effects) occurring between the shots, and these perturbations may lead to artifacts in GRAPPA-reconstructed images. Consequently we also present a method of acquiring interleaved ACS data in a manner which can reduce the effects of inter-shot signal perturbations. This method makes use of the phase correction data, conveniently a part of many standard EPI sequences, to assess the signal perturbations between the segments of R-shot EPI ACS scans. The phase correction scans serve as navigator echoes, or more accurately a perturbation-sensitive signal, to which a root-mean-square deviation perturbation metric is applied for the determination of the best available complete ACS data set among multiple complete sets of ACS data acquired prior to the EPI time series. This best set (assumed to be that with the smallest valued perturbation metric) is used in the GRAPPA autocalibration algorithm, thereby permitting considerable improvement in both image quality and temporal signal-to-noise ratio of the subsequent EPI time series at the expense of a small increase in overall acquisition time.

* For some strange arXiv technical reason the author list is reordered from that which appears (correctly) on the PDF. C'est la vie.

Understanding fMRI artifacts: CONTENTS

An organizational post I'd been meaning to get to for a while. There are some posts to come in this series, in parentheses below. I'll update this page with links as these posts get published.

Understanding fMRI artifacts

An introduction to the post series, defining what we mean by "good" data, and general discussion on viewing and interpreting EPI artifacts in a time series.

Good data

Understanding fMRI artifacts: "Good" axial data

Includes cine loops through time series EPI and statistical images to evaluate the data.

Understanding fMRI artifacts: "Good" coronal and sagittal data

Includes cine loops through time series EPI and statistical images to evaluate the data. (The notes include a description of the slice-dependent gradient switching limits that can prohibit certain slice orientations.)

Common persistent EPI artifacts

Common persistent EPI artifacts: Aliasing, or wraparound

Aliasing effects in the frequency and phase encoding dimensions.


Common persistent EPI artifacts: Gibbs artifact, or ringing

The origin of the ringing problem and demonstrations in phantom and brain data.

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

Intense stray (static) magnetic field gradients may affect cognition

Have you ever wondered whether it's appropriate to put a research subject into a dark, confined tube that makes an awful din, whereupon the subject may learn that his brain has some abnormality, and still expect the subject's brain to operate in a state representative of his normal cognition (and not that of a stressed out basket-case)? And what about the bioeffects of the high magnetic field itself, or of the rapidly switched gradients and their induced electric currents in body tissue? To date there has been scant evidence that the action of studying human cognition via an MRI scanner actually modifies that brain function in a manner that might be considered a significant issue for interpretation of fMRI results.

Putting aside the cognitive effects of a loud background noise and claustrophobia, the question remains whether the static and time-varying magnetic fields are modifying brain function in a substantial fashion. There are some well-known side effects of high magnetic fields: vertigo (see Note 1), and a metallic taste are the two phenomena tied directly to presence of, or movement through, a high magnetic field. (See Note 2.) But these effects tend to be mild and/or transitory, as a subject acclimatizes to the magnetic field, and can usually be rendered negligible by taking care not to make rapid head movements in or around the magnet.

A colleague forwarded to me yesterday a paper from a Dutch group (van Nierop et al., "Effects of magnetic stray fields from a 7 tesla MRI scanner on neurocognition: a double-blind randomized crossover study." Occup. Environ. Med. 2012 Epub) that investigates the effects of head movements in the intense stray field region of a 7 T magnet. So, first of all, some good news: if you're doing fMRI at 1.5 or 3 T and you're not in the habit of asking your subjects to thrash their heads around wildly at the mouth of the magnet or once inside the magnet bore, then so far as is known today you're in the clear. The effects reported in this paper pertain specifically to head movement in the really intense gradients that comprise the stray magnetic field around the outside of a passively shielded 7 T magnet. (The iron shield is outside the magnet, leaving considerable gradients in the vicinity of the magnet when compared to the actively shielded 1.5 and 3 T magnets most of us have nowadays.)

And with that preamble let's look at the summary of the paper:

OBJECTIVE:  This study characterises neurocognitive domains that are affected by movement-induced time-varying magnetic fields (TVMF) within a static magnetic stray field (SMF) of a 7 Tesla (T) MRI scanner.

METHODS:  Using a double-blind randomised crossover design, 31 healthy volunteers were tested in a sham (0 T), low (0.5 T) and high (1.0 T) SMF exposure condition. Standardised head movements were made before every neurocognitive task to induce TVMF.

RESULTS:  Of the six tested neurocognitive domains, we demonstrated that attention and concentration were negatively affected when exposed to TVMF within an SMF (varying from 5.0% to 21.1% per Tesla exposure, p<0.05), particular in situations were high working memory performance was required. In addition, visuospatial orientation was affected after exposure (46.7% per Tesla exposure, p=0.05).

CONCLUSION:  Neurocognitive functioning is modulated when exposed to movement-induced TVMF within an SMF of a 7 T MRI scanner. Domains that were affected include attention/concentration and visuospatial orientation. Further studies are needed to better understand the mechanisms and possible practical safety and health implications of these acute neurocognitive effects.

Okay, so let's make sure we're clear that although the test magnetic field strengths mentioned are 0.5 and 1.0 T, this refers to two heterogeneous regions of a stray magnetic field on the outside of a 7 T magnet:

i-fMRI: Prospective motion correction for fMRI?

An ideal fMRI scanner might have the ability to update some scan parameters on-the-fly, in order to reduce or eliminate the effects of subject motion. Today, this approach is commonly referred to as "prospective motion correction" because the idea is to adapt the acquisition so that (some of) the effects of motion aren't recorded in the data, in contrast to the routinely employed retrospective motion correction schemes, such as an affine registration algorithm applied during post-processing; that is, in between the acquisition and the stats/modeling, which can lead some people to refer to such steps as "pre-processing" if you have a stats/modeling-centric view of the fMRI pipeline.

On the face of it, ameliorating motion effects by not permitting them to be recorded in the time series data is a wonderful idea. Indeed, as the subtitle to this blog attests, I am a huge fan of fixes applied during the acquisition rather than waiting until afterwards to try to post-process away unwanted effects. But this preference assumes that any method actually works, and works robustly, in everyday use. For sure there will be limitations and compromises, yet the central question is whether the benefits outweigh the costs. In the specific case of prospective motion correction, then, does a scheme (a) eliminate the need to use retrospective motion correction, and (b) does it reduce the effects of motion without bizarre failure modes that can't be predicted or circumvented easily?

A good place to begin evaluating prospective motion correction schemes - indeed, all motion correction schemes - is to first asses their vulnerabilities. It's no good if the act of fixing one part of the acquisition introduces an instability elsewhere. Failure modes should be benign. Below, I list the major hurdles for motion correction schemes to overcome, then I consider how elaborate any solutions might need to be. The goal is to decide whether - or when - prospective motion correction can be considered better than the alternative (default) approach of trying to limit all subject motion, and deal with the consequences in post-processing.


What do we mean by motion correction anyway?

As conducted today, motion correction applied during post-processing generally refers to an affine or sometimes a non-linear registration algorithm that seeks to maintain a constant anatomical content in a stack of slices throughout a time series acquisition. Prospective motion correction generally refers to the same goal: conserving the anatomical content over time. But, as is well known, there are concomitant changes in the imaging signal, and perhaps the noise, when a head moves inside the magnet. Other signal changes that are driven by motion may remain in the time series data after "correction." Indeed, depending on the cost function being used, the performance of the motion correction algorithm to maintain constant anatomy over time may be compromised by these concomitant modulations.

Now, we obviously want to try to maintain the anatomical content of a particular voxel constant through time or we have a big problem for analysis! But as a goal we should use a more restrictive definition for an ideal motion correction method: after correction we seek the elimination of all motion-driven signal (and noise) modulations. The only signal changes remaining should be neurally-driven BOLD changes (if we're using BOLD contrast, which I assume in this post) and "physiologic noise" that isn't strongly coupled to head (skull) motion. (Accounting for physiologic noise is usually treated separately. That's the assumption I'll use in this post, although at a very fine spatial scale it's clear that physiologic noise is another form of motion sensitivity.)


Motion sensitivities in fMRI experiments

A useful first task is to consider all the substantial signal changes in a time series acquisition that can be driven by subject motion. What signal changes are concomitant with changes of anatomical content as the brain moves relative to the imaging volume? How complicated is this motion sensitivity? What aspects of the signal changes will require hardware upgrades to the scanner, and/or pulse sequence modifications in order to negate them? And are these capabilities already designed into a modern scanner or will they require substantial re-design? These are the questions to keep in mind as we review the major motion sensitivities.

Methods reporting in the fMRI literature

(Thanks to Micah Allen for the original Tweet and to Craig Bennett for the Retweet.)


If you do fMRI you should read this paper by Joshua Carp asap:

"The secret lives of experiments: Methods reporting in the fMRI literature."

It's a fascinating and sometimes troubling view of fMRI as a scientific method. Doubtless there will be many reviews of this paper and hopefully a few suggestions of ways to improve our lot. I'm hoping other bloggers take a stab at it, especially on the post-processing and stats/modeling issues.

At the end the author suggests adopting some sort of new reporting structure. I concur. We have many variables for sure, but not an infinite number. With a little thought we could devise a simple, logical reporting structure that could be decoded by a reader just like a header can be interpreted from a headed file. (Dicom and numerous other file types manage it, you'd think we could do it too!)

To get things started I propose a shorthand notation for the acquisition side of the methods; this is the only part I'm intimately involved with. All we need to do is make an exhaustive list of the parameters and sequence options that can be used for fMRI, then sort them into a logical order and decide on how to encode each one. Thus, if I am doing single-shot EPI on a 3 T Siemens TIM/Trio with a 12-channel receive-only head coil, 150 volumes, two dummy scans, a TR of 2000 ms, TE of 30 ms, 30 descending 3 mm slices with 10% gap, echo spacing 0.50 ms, 22 degrees axial-coronal prescription, FOV 22.4x22.4 cm, 64x64 matrix, etc. then I might have a reporting string that looks something like this:

3T/SIEM/TRIO/VB17/12CH/TR2000/TE30/150V/2DS/30SLD/THK3/GAP0.3/ESP050/22AXCOR/FOV224x224/MAT64x64

Interleaved or ascending slices? Well, SLI or SLA, of course! 

Next we add in options for parallel imaging, then options for inline motion correction such as PACE, and extend the string until we have exhausted all the options that Siemens has to offer for EPI. All the information is available from the scanner, much of it is included in the data header.

But that's just the first pass. Now we consider a GE running spiral, then we consider a GE running SENSE-EPI, then a Philips running SENSE-EPI, etc. Sure, it's a teeny bit involved but I'm pretty sure it wouldn't take a whole lot of work to collect all the information used in 99% of the fMRI studies out there. Some of the stuff that could be included is rarely if ever reported, so we could actually be doing a whole lot better than even the most thorough methods sections today. Did you notice how I included the software version in my Siemens string example above? VB17? I could even include the specific type of shimming routine used, even the number and type of shim coils!

If an option is unused then it is simply included with a blank entry: /-/ And if we include a few well-positioned blanks in the sequence for future development then we can insert some options and append those we can't envisage today. With sufficient thought we could encapsulate everything that is required to replicate a study in a few lines of text in a process that should see us through the next several years. (We just review and revise the reporting structure periodically, and we naturally include a version number at the very start so that we immediately know what we're looking at!)

There, that's my contribution to the common good for today. I just made up a silly syntax by way of example. The precise separators, use of decimal points, etc. would have to be thrashed out. But if this effort has legs then count me in as a willing participant in capturing the acquisition side of this business. We clearly need to do better for a litany of reasons. One of them is purely selfish: I find it hard or impossible to evaluate many fMRI papers for wont of just a few key parameters. I think we can fix that. We really don't have an excuse not to.

i-fMRI: Introducing a new post series

My colleague, MathematiCal Neuroimaging and I have been discussing what we see as flaws or limitations in current functional MRI scanners and methods, and what the future might look like were there ways to change things. So, in part to force us to consider each limitation with more rigor, and in part to stimulate thought and even activity within the neuroimaging community towards a brighter future, we decided to start a new series of posts that we'll cross-reference on our blogs. This blog will focus on the hand-wavy, conceptual side of things while at MathematiCal Neuroimaging you'll find the formal details and the mathematics.

We have loose plans at the moment to address the following topics: magnetic field strength considerations, gradient coil design considerations, RF coil design considerations, pulse sequences, contrast mechanisms, and motion and motion correction. We're going to hit a topic based on our developing interests and the issues that our local user community brings to us, so apologies if your fave doesn't actually appear in a post for months or years to come.


"You wanna go where? I wouldn't start from here, mate."

Blogs seem like the perfect vehicle for idle speculation about a fantasy future. The issues and limitations are very real, however, so that's where we will initiate the discussions. Then, wherever possible, we will gladly speculate on potential solutions and offer our opinions on the solutions that seem apparent today. But we're not going to try to predict the future; we will invariably be wrong. That would also be beside the point. What we want to do is motivate researchers, engineers and scanner vendors to consider the manifold ways an fMRI scanner and fMRI methods might evolve.

Note that in the last paragraph I referred specifically to an "fMRI scanner." A moment's consideration, however, reveals that most of the technology used for fMRI didn't arise out of dedicated efforts to produce a functional brain imager per se. Instead, we got lucky. Scanners are designed and built as clinical devices (worldwide sales in the hundreds to thousands) and not research tools for neuroscience (worldwide sales in the tens per year for pure research applications). A typical MRI scanner has compromises due to expense, size of subjects, stray magnetic field, applicability of methods to (paid) clinical markets, etc. Other forces are at work besides the quality and utility of fMRI. And these forces can be a mixed blessing.

Thus, part of the motivation for writing this post series is to provoke consideration of alternative current technologies; hardware or methods that exist right now but for whatever reason aren't available on the scanner you use for fMRI. Perhaps there are simple changes that can benefit fMRI applications even if these changes compromise a clinical application. For some facilities, like mine, that would be an acceptable trade.


What's in a name?

On this blog I'll use the moniker i-fMRI to label these op-ed posts. You can interpret the i however you like. Mathematicians might want to consider an imaginary scanner. Engineers might want to consider an impractical scanner. (This variant happens to be my preference.) Economists and business types might think of an inflationary fMRI scanner, because it's likely that the developments we seek will only drive the cost up, not down. And you neuroscientists? Well, we hope you'll consider your ideal fMRI scanner.

(Apple, if you're reading this - too late. We already sold the i-fMRI trademark to some company in China. Sorry.)