Multi-echo multi-band versus standard multi-band acquisitions: tSNR advantages in subcortical areas
Poster No:
1933
Submission Type:
Abstract Submission
Authors:
Scott Peltier1, Krisanne Litinas1, Brice Fernandez2, Sherry Huang3, Colter Mitchell1, Chris Monk1, Luke Hyde1
Institutions:
1University of Michigan, Ann Arbor, MI, 2GE HealthCare, Buc, France, 3GE Healthcare, Royal Oak, MI
First Author:
Co-Author(s):
Introduction:
Multi-band (MB) acquisitions offer an increase in spatiotemporal acquisition in functional MRI, and have become the norm for multi-site acquisitions (HCP (Van Essen 2012), ABCD (Casey 2018), etc). However, it has recently been shown that MB acquisitions can suffer from SNR loss, especially in subcortical regions (Srirangarajan 2021).
In this study, we examine the advantage of MBME over MB in terms of SNR in subcortical regions, leveraging a longitudinal study where a subset of subjects had both acquisitions.
In this study, we examine the advantage of MBME over MB in terms of SNR in subcortical regions, leveraging a longitudinal study where a subset of subjects had both acquisitions.
Methods:
14 subjects were collected as part of an ongoing longitudinal study (SAND). All subjects were consented following IRB guidelines. Each subject had multiple MRI scans, average 4.5 years apart, with the following parameters: Scan 1, Multi-band acquisition, following parameters from the ABCD study: 800ms TR, 2.55mmx2.55mmx2.4mm resolution, TE: 30ms, MB x Phase acceleration: 6x1, 60 slices. Scan 2, Multi-band multi-echo acquisition, GE WIP patch, 900 ms TR, 3mm isotropic resolution, TE: 13ms/30ms/47 ms; MB x Phase acceleration: 4x2, 44 slices. Data used in the current analysis were resting-state scans (eyes open, fixation cross), 5:13 minutes of data (391 timepoints for multi-band, 348 timepoint for MBME).
All data were preprocessed using FMRIprep (Esteban 2019). The three echos of the MBME data were optimally combined using the tedana package (DuPre 2021). Voxel-wise temporal signal-to-noise (tSNR) maps were formed for dividing the mean of each voxel timecourse by its standard deviation. Subcortical ROI masks were generated with WFU Pickatlas (www.nitrc.org/projects/wfu_pickatlas), and used to calculate the average tSNR in those regions to examine the dependency by acquisition type. The second echo of the MBME sequence was also used to examine the effect of different single echo MB parameters.
All data were preprocessed using FMRIprep (Esteban 2019). The three echos of the MBME data were optimally combined using the tedana package (DuPre 2021). Voxel-wise temporal signal-to-noise (tSNR) maps were formed for dividing the mean of each voxel timecourse by its standard deviation. Subcortical ROI masks were generated with WFU Pickatlas (www.nitrc.org/projects/wfu_pickatlas), and used to calculate the average tSNR in those regions to examine the dependency by acquisition type. The second echo of the MBME sequence was also used to examine the effect of different single echo MB parameters.
Results:
Figure 1 shows temporal SNR maps averaged over all subjects for each type of sequence. It can be seen that tSNR is lower in the MB acquisition, especially in the frontal areas, interior and subcortical regions. It is somewhat better in the echo 2 data of the MBME sequence, with the optimally combined MBME sequence giving the highest and most uniform tSNR throughout the brain.
Figure 2 plots the temporal SNR for the subcortical ROIS for the MB data, the second echo of the MBME dara, and the optimally combined (OC) MBME data. It is seen that there is an increase in tSNR going from the original MB acquisition to the second echo of the MBME acquisiton, and then an increase again using the OC MBME data. All differences were significant at p<0.05 using paired t-tests.
Figure 2 plots the temporal SNR for the subcortical ROIS for the MB data, the second echo of the MBME dara, and the optimally combined (OC) MBME data. It is seen that there is an increase in tSNR going from the original MB acquisition to the second echo of the MBME acquisiton, and then an increase again using the OC MBME data. All differences were significant at p<0.05 using paired t-tests.
Conclusions:
Multi-band acquisitions can have reduced temporal SNR, with the severity depending on the parameters used. Multi-echo multi-band acquisitions offer an advantage over standard multi-band acquisitions, and can help ameliorate the subcortical tSNR loss in MB acquisitions.
The current work demonstrated benefit in subcortical tSNR using MEMB by examining the resting-state data collected in this study. Future work will examine the effect on task tSNR and resultant contrast images.
The current work demonstrated benefit in subcortical tSNR using MEMB by examining the resting-state data collected in this study. Future work will examine the effect on task tSNR and resultant contrast images.
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Imaging Methods Other 1
Keywords:
Acquisition
Sub-Cortical
1|2Indicates the priority used for review
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Provide references using APA citation style.
DuPre (2021). TE-dependent analysis of multi-echo fMRI with tedana. Journal of Open Source Software, 6:3669.
Esteban (2019). fMRIPrep: a robust preprocessing pipeline for functional MRI. Nature methods, 16:111.
Srirangarajan (2021). Multi‐band FMRI compromises detection of mesolimbic reward responses. NeuroImage, 244:118617.
Van Essen (2012). The Human Connectome Project: a data acquisition perspective. NeuroImage, 62:2222.