The artifact isn't totally consistent across people, but when present, is very stable over time (i.e., sessions several years apart), and bright in temporal standard deviation QC images. The artifact's location varies with encoding direction: front for PA encoding, rear with AP encoding; the PA encoding versions tend to be much brighter and obvious than the AP.
Below is an example of the artifact, pointed out by the pink arrows. This is the temporal standard deviation image for sub-f8570ui from the DMCC55B dataset (doi:10.18112/openneuro.ds003465.v1.0.6), the first (left, AP encoding) and second (right, PA encoding) runs of Sternberg baseline (ses-wave1bas_task-Stern), after fmriprep preprocessing:
These two runs were collected sequentially within the same session, but the artifact is only visible in the PA encoding run (right). (Briefly, DMCC used a 3T Siemens Prisma, 32-channel headcoil, CMRR MB4, no in-plane acceleration, 2.4 mm iso, 1.2 s TR, alternating AP and PA encoding runs; details at openneuro, dataset description paper, and OSF sites, plus DMCC-tagged posts on this blog.)
In the previous "crescent" posts we speculated that these could be N/2 ghosts or related to incomplete fat suppression; I am now leaning away from the Nyquist ghost idea, because the crescents don't appear to line up with the most visible ghosts. (Some ghosts are a bit visible in the above image; playing with the contrast and looking in other slices makes the usual multiband-related sets of ghosts obvious, but none clearly intersect with the artifact.) It also seems odd that ghosts would be so much brighter and change their location with AP vs. PA encoding; I am no physicist, though!
link to cortex volume?
This week I gave three lab members a set of temporal standard deviation images (similar to the pair above) for 115 participants from the DMCC dataset. The participant images were in random order, and I asked the lab members to rate how confident they were that each showed the "crescent" artifact or not. My raters agreed that 34 participants showed the artifact, and 39 did not. (Ratings were mixed or less confident on the others; I asked them to decide quickly from single pairs of QC images, not investigate closely.)
We didn't measure external head size in the participants, but did run freesurfer during preprocessing, so I used its CortexVol and eTIV statistics as proxies (a different stat better?): and the group my colleagues rated as having the artifact tended to have smaller brains than those without:
If the appearance of this artifact is indeed somewhat related to head size, then it's logical that it would (as I've observed) generally be stable over time. DMCC's population was younger adults; it'd be interesting to see if there's a relationship with a wider range of head sizes.
only with DMCC or its MB4 acquisition?
Is the artifact restricted to this particular acquisition or study? No, not somehow related to DMCC; I've checked a few DMCC participants with the artifact who later participated in other studies and they have it (or not) in all of the datasets.
To see if it's restricted to the MB4 acquisition, I looked at a few images from a different study, which also has adult participants, a 3T Prisma, 32-channel headcoil, 2.4 mm iso voxels, and CMRR sequences, but with MB6 TR 0.8 s, and PA encoding for all runs. Below are standard deviation images for three different people from this MB6 study, one run each of the same task, after fmriprep preprocessing. (I chose these three because of the artifacts; not all are so prominent.)
Since this study has all PA runs I can't directly compare artifacts across the encoding directions, but there are clearly some "crescents", and more sets of rings than typical with MB4 (which makes sense for MB6). The rings are especially obvious in person #3; some of these appear to be full-brain ghosts. I suspect the artifacts would be much clearer in subject space; I haven't looked (I'm not directly involved in the study). But a substantial minority of these participants' standard deviation images resemble #1, whose artifacts strike me as quite similar to the "crescents" in some DMCC MB4 PA images.
but does it matter?
Not all artifacts that look strange in QC images actually change task-related BOLD statistics enough to be a serious concern. (Of course, how much is too much totally depends on the particular case!) I suspect that this artifact does matter for our analyses, though, both because of where it falls in the brain and because it affects BOLD enough to be visible by eye in some cases.
The artifact's most prominent frontal location with PA runs puts it uncomfortably close to regions of interest for most of my colleagues, and is one reason I have advised shifting to all AP encoding for new studies I'm involved with. Preprocessing, motion, transformation to standard space, and spatial smoothing blurs the artifact across a wider area, hopefully diluting its effect. But the artifact's location is somewhat consistent across participants, and present in a sizable enough minority (a third, perhaps, in the datasets I've looked at), that it seems possible it could reduce signal quality in our target ROIs.
For showing that it does indeed actually affect task-related BOLD enough to matter, so far I mostly just have qualitative impressions. For example, below left is the standard deviation of one DMCC person's PA Sternberg run, with the cursors on the artifact. The right side is from averaging together (after voxel-wise normalizing and detrending) frames after pressing a button with the right hand. Squinting, the statistical image is brighter in sensible motor-related grey matter areas, marked with green. But the "crescent" may also be faintly visible, as pointed out in pink.
I can imagine quantitative tests, such as comparing the single-run (so separating AP and PA encoding runs) GLM output images from the group of participants with and without the artifact. Differences in estimates in parcels/searchlights/areas overlapping the artifact would be suggestive, particularly as the estimates vary with encoding direction and participant subgroup (with-artifact or without).
thoughts?
I'm curious, have you seen similar? Do you think this artifact is from N/2 ghosting, incomplete fat suppression, or something else? (What should I call it? "Crescent" is visually descriptive, but not standard. 😅) Seem reasonable it could be related to head size? And that it can significantly affect BOLD? Other reactions? Thanks! (And we can chat about this at my OHBM 2025 poster, which will be on this topic.)
Hi Jo, I still think it's Nyquist ghosting from the scalp, most likely residual scalp fat signal.
ReplyDelete"It also seems odd that ghosts would be so much brighter and change their location with AP vs. PA encoding"
This actually makes sense. Think of AP as being a stretch, PA as being a compression of the residual scalp signal. If the true width of the residual signal is two pixels but in the AP case the signal is stretched out to three pixels while in the PA case it's compressed to one pixel, which is brightest? Obviously the piled up single pixel width, since the same total signal has been concentrated into a smaller volume. Hence, brighter ghost. We see the same phenomenon inside the brain for stretched vs. compressed regions. (And incidentally, this is why the piled up signal is impossible to redistribute properly with a distortion correction. Do we share the single pixel signal evenly over two pixels, or was the true distribution more like 80:20? We don't/can't know, so we make an approximation and distribute evenly. Which is why, given a choice, we seek to distortion-correct stretched signals over compressed, piled up signals.)
"Is the artifact restricted to this particular acquisition or study? No, not somehow related to DMCC; I've checked a few DMCC participants with the artifact who later participated in other studies and they have it (or not) in all of the datasets."
Also fits with Nyquist ghosting as being the underlying cause. We would see the same thing with no MB, using plain vanilla EPI. And it also fits with the idea of this crescent being a "biological phenotype," related to head size relative to the RF coil. (I still suspect the large array coils will show a worse effect for the same subject because the receive bias field is considerably stronger the larger the array, i.e. 64ch worse than 32ch worse than 20ch.) If I can find a suitable subject to test on, I suspect I could prove these points quickly with data. I'll put it on my to do list for January.
💡 Ah! It's not either Nyquist ghosts *or* something to do with fat suppression, but ghosts *from* the fat. I was thinking they were mutually exclusive explanations; a bit of both makes a lot more sense, as does the stretching/compressing of the artifact with encoding direction.
DeleteI've worked a bit with a dataset collected with CMRR MB4 on the Prisma, all AP encoding but with a 64 channel head coil. I don't remember noticing many artifacts, but it might be interesting to revisit and compare its images with 32 channel versions.
Thanks!