Diffusion imagingis often included as a component of functional neuroimaging protocols these days. While fMRI examines functional changes on the timescale of seconds to minutes, diffusion imaging is able to detect changes over weeks to years. Furthermore, there may be complimentary information from the white matter connectivity obtainable from diffusion imaging – that is, from tractography - and the functional connectivity of gray matter regions that can be derived from resting state or task-based fMRI experiments.
I was recently made aware of some artifacts on diffusion-weighted EPI scans acquired on a colleagues’ scanner. When I was able to replicate the issue on my own scanner, and even make the problem worse, it was time to do a serious investigation. The origin of the problem was finally confirmed after exhaustive checks involving the assistance of several engineers and scientists from Siemens. The conclusion isn't exactly a major surprise: fat suppression for diffusion-weighted imaging of brain is often insufficient. And it seems that although the need for good fat suppression is well known amongst physics types, it’s not common knowledge in the neuroscience community. What’s more, the definition of “sufficient” may vary from experiment to experiment and it may well be that numerous centers are unaware that they may have a problem.
Let’s start out by assessing a bad example of the problem. The diffusion-weighted images you’re about to see were acquired from a typical volunteer on a Siemens TIM/Trio using a 32-channel receive-only head coil, with b=3000 s/mm2 (see Note 1), 2 mm isotropic voxels, and GRAPPA with twofold (R=2) acceleration. These are three successive axial slices:
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The blue arrows mark hypointense artifacts whereas the orange arrow picks out a hyperintense artifact. Even my knowledge of neuroanatomy is sufficient to recognize that these crescents are not brain structures. They are actually fat signals, shifted up in the image plane from the scalp tissue at the back of the head. (If you look carefully you may be able to trace the entire outline of the scalp, including fat from around the eye sockets, all displaced anterior by a fixed amount.) I’ll discuss the mechanism later on, but at this point I’ll note that the two principal concerns are the b value (of 3000 s/mm2) and the use of a 32-channel array coil. GRAPPA isn’t a prime suspect for once!
Now, part of the problem is that the intensity of the artifacts – but not their location - changes as the direction of the diffusion-weighting gradients changes. In the following video you see the diffusion-weighted images as the diffusion gradient orientation is changed through thirty-two directions (see Note 2):
The signal from white matter fibers changes as the diffusion gradient direction changes. That’s what you want to happen. But the displaced fat artifacts also change intensity with diffusion gradient direction, meaning that the artifact is erroneously encoded as regions of anisotropic diffusion. Thus, when one computes the final diffusion model, the brain regions contaminated by fat artifacts end up looking like white matter tracts. In the next figure the data shown above was fit to a simple tensor model, from which a color-coded anisotropy map can be obtained:
The white arrow picks out the false “tract” corresponding to the artifact signal crescent we saw on the raw diffusion-weighted images. I suppose it’s remotely possible that this is the iTract, a new fasciculus that has evolved to connect the subject’s ear to his smart phone, but my money is on the fat artifact explanation.
Clearly, in the above image there is no easy way to distinguish the artifact from real white matter tracts by eye, except by using your prior anatomical knowledge. And it's likely to confuse tractographic methods, too, because it has very similar geometric properties to those that tractographic methods attempt to trace. So let's take a look at the origin of the problem and then we can get into what you want: solutions.
Fat head!
That's right, you heard me. You probably already know about the subcutaneous fat (or lipid, if you prefer) that surrounds the outside of your skull. While this relatively small amount of fat helps keep your noggin warm and provides a modicum of cushioning for when you bang your head on something, it's not ideal when it comes to brain imaging. The problem is that fat protons - and there are lots of them because fat is essentially long chains of carbonswith 1-3 hydrogen atoms attached to each one - resonate at a different frequency than water protons; a difference amounting to more than 400 Hz at 3 tesla. This so-called chemical shift difference causes a systematic spatial displacement of the fat signal from the water signal. And because the phase encoding dimension of a typical EPI has spatial information amounting to around 30 Hz per pixel, you can see immediately that a systematic 400 Hz offset amounts to a shift of a dozen or more pixels in the phase encoding dimension. (See Note 3.)
Scalp fat is thus a concern for all EPI-based imaging. Fat suppression is therefore included as a standard step in a typical EPI sequence forfMRI, for example. In fMRI, insufficient fat suppression in EPI leads to larger than ideal ghosting, which can cause regions of unnecessarily high signal variance for regions of the brain overlapped by the fat ghosts, as well as the fat shift you saw above. But, as will be discussed below, the requirements for fat suppression for diffusion imaging can be even more stringent.
What can make the problemworse?
Why might EPI-based diffusion imaging need enhanced fat suppression compared to EPI for fMRI? The simple explanation is as follows: fat doesn't diffuse very quickly compared to water in tissues. Thus, for any given diffusion weighting gradient value, the amount of signal attenuation according to S/S0 = e-bD, where S0 is the signal intensity with the diffusion gradient turned off, is much lower for fat than for water because D, the diffusion coefficient, is lower for fat than for water. When the b value starts to get high the amount of residual water signal from brain tissue drops considerably, making the residual fat signal comparable to the brain signals. If we now couple a relatively intense fat signal with the spatial displacement arising from the chemical shift… Presto! You have the problem we saw in the opening figures.
Your immediate question is, of course: “But if fat suppression is enabled as a standard step, why could I possibly have a problem?” The prosaic answer is that the efficacy of the fat suppression technique may be imperfect and may need to be adjusted depending on the specific parameters you use for acquiring your diffusion data. We need to evaluate a few aspects of the brain and fat signals in a little more detail.
As I mentioned above, the essential issue here is one of relative signal magnitude, coupled with the displacement of scalp fat signals into regions that should be occupied by brain only. If, under diffusion weighting, the residual fat signals become significant relative to the residual brain tissue water signals then we have a concern. And if, because of the chemical shift, the scalp fat signals end up parked on regions that should be occupied only by brain then we have a problem. So let’s next look at a few of the situations where the residual fat signal may become problematic:
High b values -The higher the b value the greater the residual fat signal is likely to be relative to the brain water signal.A sensitive receiver array coil - Another good way to enhance the scalp fat signal is to use a very sensitive array coil, such as a 32-channel coil. The coil also boosts signal from the deeper brain signal to be sure, but the scalp fat is inconveniently located even closer to the coil elements and therefore gets an unwelcome supercharged boost. Note, however, that the fat artifact problem may arise with any RF coil.A long TE– Using an unnecessarily long TE will tend to preserve fat signal over water signal because fat has a long T2. Try to use the shortest possible TE for the b value that you want.In-plane parallel imaging– So, yeah, I lied ever so slightly earlier on. Although they aren’t the prime suspects because GRAPPA, SENSE and their ilk don’t create the fat artifact problem, they can exacerbate it because they tend to decrease the image SNR, especially the SNR of brain regions far from the periphery where the receive coil elements are located. (This is the so-called geometry, or g, factor.) There is also an overall root-R reduction of image SNR for R-fold acceleration.
Unfortunate head positioning– Some head sizes and shapes, and some head positions relative to the imaging gradients, may make the residual fat artifact more or less of a problem for you. The position of any fat artifacts will tend to vary slightly from subject to subject. Sometimes residual fat crescents may remain outside the brain, sometimes not. (See Note 4.)
Make it go away, please
How do you know if your fat suppression scheme is sufficient, or that the imaging parameters render the experiment vulnerable to fat artifacts parking themselves on the brain? Checking your human brain data for fat artifacts may not suffice. As I've mentioned, it's often quite difficult to detect by eye artifacts that are overlaying complicated brain anatomy. The crescents from scalp fat look awfully like genuine white matter tracts a lot of the time. There is also the issue of biological variability to contend with. Just because the ten test subjects you evaluate don’t exhibit a clear problem doesn’t imply that your experimental subjects will always be problem-free. This is a situation where a phantom experiment can really help.
Note how the olive oil signal has been shifted into the region of image that should be occupied by water in the sphere. (The olive oil is actually located a couple of centimeters beneath the sphere.) And, of course, if the b value were increased beyond 1000 the residual signal ratio of oil to water would be further enhanced.
On the product diffusion imaging sequence (ep2d_diff) on my scanner there are three options to eliminate fat signals: fat suppression (a.k.a. fatsat, based on a chemical shift-selective pre-pulse that targets the fat protons), fat-selective inversion recovery (SPAIR - which uses an inversion pulse targeted at just the fat resonances), and a composite spatial-spectral pulse (usually called "water excite" because it is designed to avoid the excitation of fat rather than eliminate the signal per se). These three fat elimination schemes were compared on the same phantom setup.
Below are example diffusion-weighted image data sets obtained from the oil-under-sphere phantom acquired with (from left to right) fatsat, SPAIR and water excite:
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| Left to right: Fatsat, SPAIR and water excite fat suppression options on a product diffusion imaging sequence. |
None of the fat elimination options on the product diffusion sequence was able to eliminate the fat signal. There was a clear oil artifact – a bright crescent in this particular instance - shifted into each image.
At which point I was out of options with the standard (product) software, Syngo MR B17, on my Trio because there is only the one diffusion-weighted EPI sequence. Fortunately, however, I have a research diffusion imaging pulse sequence that has an option called "Extra Fat Suppr." (See Note 5.) So I tried it. And finally the artifact could be eliminated! On the left is the standard fat saturation while on the right is the Extra Fat Suppr. option:
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Note the complete elimination of fat artifacts overlapping the water phantom only in the right-hand images. With sufficient fat elimination, all that remains is a thin crescent of oil signal located correctly underneath the water-filled sphere. The fat suppression demonstrated on the right is what we require for diffusion imaging of human brain. It implies that the b value could be increased, imaging parameters could be altered, etc. and there would be no residual fat signal to be concerned about.
Checking that you don't have a fat suppression problem
The need for excellent fat suppression for diffusion imaging is well known. I've included a couple of references in Note 6, papers that show examples of scalp fat artifact as strikingly as those you've just seen. But these relatively new methods may not be available on your scanner; they aren't on mine.
So, where does this leave you? Whichever scanner and diffusion imaging pulse sequence you use, I would suggest checking your fat suppression performance before applying it on a person. (Siemens users, see Note 7.) It's easy enough to put some vegetable oil in a container and include it with a water-filled phantom. You don't have to put the oil in a bag like I did; I was trying to replicate a real scalp effect. But bags can leak fairly easily and you don't want a mess to clean up! It should suffice to include the oil in a small leak-proof plastic container. Then, once you have your sample ready to test it's as easy as acquiring your diffusion imaging protocol and assessing the raw, diffusion-weighted images. (See Note 8.) If there's any doubt, disable the fat suppression scheme and do a comparison. The difference with and without fat suppression should be striking.
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Notes:
1. The so-called b value (or b factor) is simply the reciprocal of the diffusion coefficient, hence the units of s/mm2. D, the mean diffusivity (sometimes called the apparent diffusion coefficient, ADC) is in units of mm2/s. The b value takes into account all of the dephasing caused by applied magnetic field gradients (it doesn't include dephasing caused by magnetic susceptibility gradients because these are unknown) in a simple term that relates signal loss according to:
S/S0 = e-bD
where S0 is the signal in the absence of applied diffusion gradients. The imaging gradients themselves should be included in the calculation of b, although for conceptual simplicity we can think of b=0 for an image with no diffusion weighting gradients enabled.
2. It doesn’t actually matter what these directions were or why there were 32 of them in order to understand the artifact and why it’s a problem. But in case you know about such things, it was to fit to a HARDI model and this was half of the total data acquired. Diffusion scans differ in how the gradients are applied because different models of diffusion may be used to reconstruct the data. A simple tensor process may be used, for instance. Several models use a fixed magnitude of the b value (which is, strictly speaking, a 3x3 matrix for 3D gradient encoding and not a single number) and simply rotate its direction. The trigonometry is done behind the scenes. This is akin to sampling different points on a sphere of constant b radius.
3. There is also a smaller shift of about a quarter of a pixel in the readout dimension, which we can safely ignore. The effect is far smaller in the readout dimension than in the phase encoding dimension of EPI because the readout gradient bandwidth is typically between 1500-3000 Hz per pixel. It’s also useful to recognize that the chemical shift difference between water and fat scales in proportion to the magnetic field strength, making the effect at 3 T twice as bad as at 1.5 T.
4. It would be terrific if there were a reliable way to arrange the phase encoding parameters such that the fat artifacts always fell in non-critical parts of the image. This is easier said than done, however. One tactic might be to adjust the head position relative to the phase encode gradient center such that the crescents fall mostly outside of the brain. Another tactic is to increase the image field-of-view (FOV), but that is inefficient use of spatial encoding. You could try swapping the readout and phase encoding axes, thereby putting the fat artifacts in the orthogonal dimension; left-right in the case of the axial slices shown so far. But that causes the phase encode distortion to also shift to the L-R direction, and people seem to have an inherent dislike of kidney bean-shaped brains (even if the actual distortion level is similar on a quantitative basis). Finally, you could try reversing the phase encoding axis; here it would be P-A from A-P. Altering the phase encoding axis necessarily alters the distortion: compressions in A-P become stretches in P-A, and vice versa. Furthermore, you may have to choose between signal from the back of the head being displaced up into the brain for one phase encoding direction, versus fat signal from the forehead being displaced down into the brain if the phase encode direction is reversed! Pick your poison. Similarly, if the phase encoding dimension is left-right (for axial slices) then the scalp fat from one side or other will be displaced into the brain; you only get to choose whichside gets contaminated in selecting the phase encode direction. None of these tricks is ideal, and none would be easy to implement across an array of disparate heads.
5. I don't know what the extra fat suppression option is doingexcept that it increases the minimum TE by about a millisecond, so it may be adding an extra fat suppression pulse. I honestly can't tell you right now, but I'll add a footnote to this post if I ever find out.
6. Some references for more information on improved fat elimination schemes, including examples of the problem:
Robust fat suppression at 3T in high-resolution diffusion-weighted single-shot echo-planar imaging of human brain.
JE Sarlls, C Pierpaoli, SL Talagala, WM Luh. Magn. Reson. Med.66(6):1658-65 (2011).
PMID: 21604298
DOI: 10.1002/mrm.22940
JE Sarlls, C Pierpaoli, SL Talagala, WM Luh. Magn. Reson. Med.66(6):1658-65 (2011).
PMID: 21604298
DOI: 10.1002/mrm.22940
Efficient fat suppression by slice-selection gradient reversal in twice-refocused diffusion encoding.
Z Nagy, N Weiskopf. Magn. Reson. Med.60(5):1256-60 (2008).
Z Nagy, N Weiskopf. Magn. Reson. Med.60(5):1256-60 (2008).
DOI: 10.1002/mrm.21746
7. If you own a Siemens scanner running Syngo MR B15 or B17 (which includes Trio and Verio scanners), you have the product diffusion imaging sequence (ep2d_diff) as your only option, and you find that your fat suppression is insufficient when you test then you may want to talk to your local applications support people. To my way of thinking, insufficient fat suppression is a pulse sequence bug that should be patched. However, if you are fortunate to have a research agreement with Siemens then you should be able to get a work-in-progress (WIP) sequence to use instead of ep2d_diff. I tested the WIP sequence numbered 511E, but I note that 511C also has the Extra Fat Suppr. option in it. Finally, I found a document online that claims Syngo MR D11, the software that comes on the Magnetom Skyra 3 T scanner, has "improved fat saturation schemes" as part of the product diffusion sequence. I haven't seen it or tested it for myself, but I would hope that the performance of the WIP sequence I have tested is matched in the latest product.
Update(4th March, 2013):There is an "extra fatsat" option on the product diffusion imaging sequence (ep2d_diff) available on the Skyra running Syngo MR D11. I haven't tested the standard or the extra options but I would bet considerable money that the "extra" option is required in order to fully eliminate scalp fat signal with b > 1000 s/mm^2.
Update(4th March, 2013):There is an "extra fatsat" option on the product diffusion imaging sequence (ep2d_diff) available on the Skyra running Syngo MR D11. I haven't tested the standard or the extra options but I would bet considerable money that the "extra" option is required in order to fully eliminate scalp fat signal with b > 1000 s/mm^2.
8. You should test the diffusion protocol as similarly as possible to the conditions to be used for brain imaging. However, if you use b values significantly above 1000 you won't see very much residual water signal in an isotropic water phantom. That may not be a big deal because it would still be possible to see chemical shift artifacts from an oil sample once the water signal has been eliminated. Indeed, there's no strict need to have a water phantom in the coil with the oil at all! But I prefer to have a water signal to shim on, and to have a water signal background against which to contrast residual fat signals. It's all a matter of preference, and my preference is to maintain all other parameters as used in the brain experiment, but to reduce the b value to 1000 for testing purposes. If I was paranoid I might then repeat the tests with the higher b value of an actual experiment, but once I'm satisfied the fat suppression is working at b=1000 then I am confident it will work well at other values.