I have renamed the three posts entitled "Diagnosing artifacts in fMRI data: Part x" to be "Physics for understanding fMRI artifacts: Part x." I am developing new posts in the series and through post seven at least the content is all quite theoretical; I'm not actually discussing artifacts or showing data! (But don't worry, I'm limiting the content to the essential concepts required to understand and differentiate fMRI artifacts. It's not going to be an entire MRI physics course!)
Once I've concluded this background series of physics posts (there are another eight or nine posts to come) I'll start a new series that will be entitled something suitable for actual artifact recognition (with data!), along the lines of the original title of the series. Hopefully this re-categorization will allow future readers to establish suitable paths through the posts, when a strictly chronological path probably won't be the best one.
Tuesday, April 19, 2011
Saturday, April 9, 2011
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.
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.
Tuesday, March 15, 2011
Go faster MRI at Berkeley!
With apologies for the continued delay to the artifact recognition series of posts - I've been distracted with some scanner problems - I thought I'd do a quick post on a recent methodological development that's generated some buzz in the field as well as in the media. The media buzz:
ABC 7 News video
UC Berkeley news center story
And in case you want to read the actual publication, it was published at PLoS ONE in early January. The work is part of the Human Connectome Project, an NIH-funded consortium involving Washington University (St Louis), Oxford, Minnesota and Berkeley. David Feinberg is the Berkeley representative.
The implications of these methodological developments could be quite substantial, possibly allowing better interpretation of brain dynamics than is currently permitted with the typical fMRI temporal resolution of two seconds or so. Of course, there are caveats. One is that the BOLD response is still low-pass filtered. And another is that the new "go faster" method involves several separate steps, each of which tends to exacerbate head motion sensitivity. Still, it looks good on highly motivated volunteers!
ABC 7 News video
UC Berkeley news center story
And in case you want to read the actual publication, it was published at PLoS ONE in early January. The work is part of the Human Connectome Project, an NIH-funded consortium involving Washington University (St Louis), Oxford, Minnesota and Berkeley. David Feinberg is the Berkeley representative.
The implications of these methodological developments could be quite substantial, possibly allowing better interpretation of brain dynamics than is currently permitted with the typical fMRI temporal resolution of two seconds or so. Of course, there are caveats. One is that the BOLD response is still low-pass filtered. And another is that the new "go faster" method involves several separate steps, each of which tends to exacerbate head motion sensitivity. Still, it looks good on highly motivated volunteers!
Saturday, February 19, 2011
Physics for understanding fMRI artifacts: Part Three
Coffee break! Time for a few tangents
In this post we're going to do a whistle-stop tour of some background concepts that you should have seen before. None of the information in today's series of videos is essential to understanding what's coming up later, when we get to k-space, the EPI pulse sequence and artifacts, but it's interesting and useful to review. Besides, these videos are well made, entertaining and are available free so we might as well use them! So, if you have the time, go grab a coffee and spend the next hour being reminded of things you probably knew at some point in a dim and distant past. You might even learn something about scanner hardware you didn't know before.
The anatomy of a miniature scanner
Don't worry too much about following every detail in today's first video, which dissects a miniature MRI scanner. It contains the same basic components as your fMRI scanner. Below, I've given a few explanatory notes on the coils and components that are most relevant to us.
In this post we're going to do a whistle-stop tour of some background concepts that you should have seen before. None of the information in today's series of videos is essential to understanding what's coming up later, when we get to k-space, the EPI pulse sequence and artifacts, but it's interesting and useful to review. Besides, these videos are well made, entertaining and are available free so we might as well use them! So, if you have the time, go grab a coffee and spend the next hour being reminded of things you probably knew at some point in a dim and distant past. You might even learn something about scanner hardware you didn't know before.
The anatomy of a miniature scanner
Don't worry too much about following every detail in today's first video, which dissects a miniature MRI scanner. It contains the same basic components as your fMRI scanner. Below, I've given a few explanatory notes on the coils and components that are most relevant to us.
Wednesday, February 9, 2011
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.
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.
Sunday, February 6, 2011
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:
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:
Saturday, January 22, 2011
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.
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.
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