“Crescent” artifacts with multiband fMRI acquisitions: appearance, causes, and consequences
Poster No:
1046
Submission Type:
Abstract Submission
Authors:
Jo Etzel1
Institutions:
1Washington University in St. Louis, St. Louis, MO
First Author:
Introduction:
Examination of fMRI dataset images is a critical component of quality control (QC) procedures, and necessary for reliable analysis results (Etzel 2023; Niso et al. 2022). Temporal standard deviation images of each run (i.e., each voxel's standard deviation over the run's frames) are a useful type of QC image, and one we included for the DMCC project (Etzel et al. 2022; Braver et al. 2021). Early in DMCC data collection I noticed that some participants had bright, crescent-shaped bands in their standard deviation images, most prominently frontally in PA encoding runs, and speculation was that they were a type of Nyquist ghost (Etzel 2018).
Further, the artifact appears to be stable over time: participants typically would have it in all scanning sessions and waves (i.e., over a span of months to years) or none, and informal observation suggested that it could be related to head size, which would be consistent with the artifact's long-term consistency. "Crescents" are not restricted to the DMCC study nor its acquisition protocol (3T Siemens Prisma, 32-channel headcoil, CMRR MB4, 2.4 mm iso, 1.2 s TR, alternating AP and PA runs), but visible in images from other studies, particularly those with PA encoding and higher multiband acceleration.
This abstract describes an investigation of the crescent artifact and its relationship with head size in the DMCC dataset, as well as suggestions for minimizing its possible impact.
Further, the artifact appears to be stable over time: participants typically would have it in all scanning sessions and waves (i.e., over a span of months to years) or none, and informal observation suggested that it could be related to head size, which would be consistent with the artifact's long-term consistency. "Crescents" are not restricted to the DMCC study nor its acquisition protocol (3T Siemens Prisma, 32-channel headcoil, CMRR MB4, 2.4 mm iso, 1.2 s TR, alternating AP and PA runs), but visible in images from other studies, particularly those with PA encoding and higher multiband acceleration.
This abstract describes an investigation of the crescent artifact and its relationship with head size in the DMCC dataset, as well as suggestions for minimizing its possible impact.
Methods:
For an unbiased evaluation of which participants have the crescent artifact, I asked three colleagues to rate their confidence that they saw the artifact in 115 participants from the DMCC dataset. Ratings were on the scale of 0 (confident artifact absent) to 5 (confident artifact present).
We did not measure the external size of DMCC participants' heads, but FreeSurfer produces several estimates of brain volume (Dale, Fischl, and Sereno 1999) (FreeSurfer 6.0.1, RRID:SCR_001847); I extracted the CortexVol, eTIV, BrainSegVol, and MaskVol fields (only) from the aseg.stats file for each person to serve as proxies for head size.
We did not measure the external size of DMCC participants' heads, but FreeSurfer produces several estimates of brain volume (Dale, Fischl, and Sereno 1999) (FreeSurfer 6.0.1, RRID:SCR_001847); I extracted the CortexVol, eTIV, BrainSegVol, and MaskVol fields (only) from the aseg.stats file for each person to serve as proxies for head size.
Results:
The raters agreed that 34 participants showed the artifact and 39 did not. Their ratings were mixed on the remaining 42 participants; I believe with closer examination in 3d space a consensus could be reached in nearly all cases, and the artifact visible in up to half of the participants.
Figure 1 shows the appearance of the crescent artifact (arrows) in temporal standard deviation images for two runs in each of two DMCC55B participants (doi:10.18112/openneuro.ds003465.v1.0.6). These QC images were calculated after preprocessing (fMRIPrep 1.3.2, RRID:SCR_016216); realignment, resampling to standard space, and other procedures cause warping and blurring of the artifact here compared to their appearance in the source images (i.e., in subject space; not shown).
Characteristically, in Figure 1 the crescent artifact is bright frontally in each participant's PA run standard deviation image, and faint to absent in the back of the brain for the AP run. The changing appearance of the "crescent" with encoding direction confirms that it is an artifact: other bright structures, such as large vessels, are comparably similar in both.
Next, I compared the FreeSurfer statistics of the participants in the two groups identified by my raters (Figure 2); with all four measures the group with the artifact tended to have smaller brains than those without.
Figure 1 shows the appearance of the crescent artifact (arrows) in temporal standard deviation images for two runs in each of two DMCC55B participants (doi:10.18112/openneuro.ds003465.v1.0.6). These QC images were calculated after preprocessing (fMRIPrep 1.3.2, RRID:SCR_016216); realignment, resampling to standard space, and other procedures cause warping and blurring of the artifact here compared to their appearance in the source images (i.e., in subject space; not shown).
Characteristically, in Figure 1 the crescent artifact is bright frontally in each participant's PA run standard deviation image, and faint to absent in the back of the brain for the AP run. The changing appearance of the "crescent" with encoding direction confirms that it is an artifact: other bright structures, such as large vessels, are comparably similar in both.
Next, I compared the FreeSurfer statistics of the participants in the two groups identified by my raters (Figure 2); with all four measures the group with the artifact tended to have smaller brains than those without.
·Figure 1.
·Figure 2.
Conclusions:
The crescents are most visible in white matter, but extend across grey as well, and likely reduce BOLD signal quality. Their clear presence in a substantial minority of participants and somewhat consistent spatial location in those participants leads me to recommend including them as a factor when deciding upon fMRI acquisition parameters; the encoding direction (and other parameters) should be selected so that the "crescent" artifacts are minimized and fall in areas of least interest for each particular study.
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 1
Motion Correction and Preprocessing 2
Keywords:
Acquisition
FUNCTIONAL MRI
Other - quality control; artifacts
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
Dale, Anders M., Bruce Fischl, and Martin I. Sereno. 1999. “Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction.” NeuroImage 9 (2): 179–94. https://doi.org/10.1006/nimg.1998.0395.
Etzel, Joset A. 2018. “Holy Crescents, Batman!” MVPA Meanderings (blog). January 17, 2018. https://mvpa.blogspot.com/2018/01/holy-crescents-batman.html.
———. 2023. “Efficient Evaluation of the Open QC Task fMRI Dataset.” Frontiers in Neuroimaging 2 (February):1070274. https://doi.org/10.3389/fnimg.2023.1070274.
Etzel, Joset A., Rachel E. Brough, Michael C. Freund, Alexander Kizhner, Yanli Lin, Matthew F. Singh, Rongxiang Tang, Allison Tay, Anxu Wang, and Todd S. Braver. 2022. “The Dual Mechanisms of Cognitive Control Dataset, a Theoretically-Guided within-Subject Task fMRI Battery.” Scientific Data 9 (1): 114. https://doi.org/10.1038/s41597-022-01226-4.
Niso, Guiomar, Rotem Botvinik-Nezer, Stefan Appelhoff, Alejandro De La Vega, Oscar Esteban, Joset A. Etzel, Karolina Finc, et al. 2022. “Open and Reproducible Neuroimaging: From Study Inception to Publication.” NeuroImage, September, 119623. https://doi.org/10.1016/j.neuroimage.2022.119623.