Inadequate fat suppression for diffusion imaging

Diffusion imaging is 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:

(Click to enlarge.)

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. 

Physics for understanding fMRI artifacts: Part Thirteen

A tour through a real EPI pulse sequence

In some posts I've got planned it will be important for you to know something about all of the different functional modules that are included in a real EPI pulse sequence. So far in this PFUFA series I've used schematics of the particular segment of the sequence that I was writing about, e.g. the echo train that covers 2D k-space for single-shot EPI. Except that there comes a time when you need to know about the sequence in its entirety, as it is implemented on a scanner. Why? Because there are various events that I've given short shrift - fat saturation and N/2 ghost correction, for instance - that have significant temporal overheads in the sequence, and these additional delays obviously affect how quickly one can scan a brain.

So, without further ado, here is a pulse sequence for fat-suppressed, single-shot gradient echo EPI, as used for fMRI:

(Click to enlarge.)

Okay, so it's not the entire pulse sequence. The readout gradient echo train in this diagram has been curtailed after just nine of 64 total gradient echoes that will be acquired, for EPI with a matrix of 64x64 pixels. The omitted 55 echoes are simply clones of the nine echoes that you can see. (Note that there are no additional gradient episodes at the end of this particular EPI sequence; all the crusher gradients occur at the start of the sequence and these are visible in the above figure. More on crusher gradients below.) I should also point out that this is the timing diagram for acquisition of a single 64x64 matrix EPI slice. The pulse sequence as shown would be repeated n times for n slices within each TR. (See Note 1.)


Interpreting what you see

Let's first determine what information is being displayed on the figure above. There are five axes, all handily labeled on the far right-hand side of the figure. The top axis is the RF transmit channel; we've got two RF pulses in this sequence. The second axis down is the receiver, or analog-to-digital converter (ADC) channel. The scanner is receiving signals only when there's a rectangle specified on the second axis. Finally, the bottom three axes represent the pulsed field gradients, in the order X, Y, Z.

Just for fun, let's quickly determine what the scanner is doing in the logical frame of reference, before we delve into the nitty-gritty. The slice selection gradient will occur in concert with an RF excitation pulse, and we have two RF pulses to choose from. Slice selection can't be the first RF pulse because that pulse occurs without any concomitant gradients. Thus, the slice excitation pulse must be the second one and we can deduce that slice selection is along the Z axis, which is the magnet bore axis. We're doing axial slices.