Ultra-high spatial resolution BOLD fMRI in humans using combined segmented-accelerated VFA-FLEET with a recursive RF pulse design
Avery J.L. Berman (1, 2), William A. Grissom (3, 4), Thomas Witzel (1,, 2), Shahin Nasr (1, 2), Daniel J. Park (1), Kawin Setsompop (1, 2, 5),, Jonathan R. Polimeni (1, 2, 5) ((1) Athinoula A. Martinos Center for, Biomedical Imaging, Massachusetts General Hospital, Charlestown

TL;DR
This paper introduces a novel recursive RF pulse design combined with segmented-accelerated VFA-FLEET to achieve ultra-high-resolution BOLD fMRI with reduced artifacts and ghosting, enabling detailed whole-brain imaging at 7 Tesla.
Contribution
The study develops a recursive RF pulse design for VFA-FLEET, significantly reducing ghosting artifacts and enabling reliable ultra-high-resolution fMRI at 7 Tesla.
Findings
VFA-FLEET-SLR reduces ghosting compared to previous methods.
Achieved 0.6-mm isotropic resolution at 7 T without zoomed imaging.
Demonstrated reliable detection of BOLD responses at high resolution.
Abstract
Purpose To alleviate the spatial encoding limitations of single-shot EPI by developing multi-shot segmented EPI for ultra-high-resolution fMRI with reduced ghosting artifacts from subject motion and respiration. Methods Segmented EPI can reduce readout duration and reduce acceleration factors, however, the time elapsed between segment acquisitions (on the order of seconds) can result in intermittent ghosting, limiting its use for fMRI. Here, "FLEET" segment ordering--where segments are looped over before slices--was combined with a variable flip angle progression (VFA-FLEET) to improve inter-segment fidelity and maximize signal for fMRI. Scaling a sinc pulse's flip angle for each segment (VFA-FLEET-Sinc) produced inconsistent slice profiles and ghosting, therefore, a recursive Shinnar-Le Roux (SLR) RF pulse design was developed (VFA-FLEET-SLR) to generate unique pulses for every…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
