Back

Framewise multi-echo distortion correction for superior functional MRI

Van, A. N.; Montez, D. F.; Laumann, T. O.; Suljic, V.; Madison, T.; Baden, N. J.; Ramirez-Perez, N.; Scheidter, K. M.; Monk, J. S.; Whiting, F. I.; Adeyemo, B.; Chauvin, R. J.; Krimmel, S. R.; Metoki, A.; Rajesh, A.; Roland, J. L.; Salo, T.; Wang, A.; Weldon, K. B.; Sotiras, A.; Shimony, J. S.; Kay, B. P.; Nelson, S. M.; Tervo-Clemmens, B.; Marek, S. A.; Vizioli, L.; Yacoub, E.; Satterthwaite, T. D.; Gordon, E. M.; Fair, D. A.; Tisdall, D.; Dosenbach, N. U. F.

2023-11-29 bioengineering
10.1101/2023.11.28.568744 bioRxiv
Show abstract

Functional MRI (fMRI) data are severely distorted by magnetic field (B0) inhomogeneities which currently must be corrected using separately acquired field map data. However, changes in the head position of a scanning participant across fMRI frames can cause changes in the B0 field, preventing accurate correction of geometric distortions. Additionally, field maps can be corrupted by movement during their acquisition, preventing distortion correction altogether. In this study, we use phase information from multi-echo (ME) fMRI data to dynamically sample distortion due to fluctuating B0 field inhomogeneity across frames by acquiring multiple echoes during a single EPI readout. Our distortion correction approach, MEDIC (Multi-Echo DIstortion Correction), accurately estimates B0 related distortions for each frame of multi-echo fMRI data. Here, we demonstrate that MEDICs framewise distortion correction produces improved alignment to anatomy and decreases the impact of head motion on resting-state functional connectivity (RSFC) maps, in higher motion data, when compared to the prior gold standard approach (i.e., TOPUP). Enhanced framewise distortion correction with MEDIC, without the requirement for field map collection, furthers the advantage of multi-echo over single-echo fMRI.

Published in Imaging Neuroscience (predicted rank #1) · training set

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.