Self-Correction of B₀ Drift and Trajectory Error in Alternating Unbalanced SSFP-FID and SSFP-ECHO (AUSFIDE)-Based 3D Transverse Relaxometry of the Human Brain.

Authors

Eunseo Bae, Sung Suk Oh, Dongyeob Han, Kathryn M Jaroszynski, Felix W Wehrli, Hyunyeol Lee

Published in

Magnetic resonance in medicine. Oct 02, 2026. Epub Oct 02, 2026.

Abstract

The alternating unbalanced SSFP (AUSFIDE) pulse sequence, which consists of multi-echo trains of SSFP-FID and SSFP-ECHO modules, enables rapid estimation of R₂ and R₂', but is prone to errors from temporal drift of the main magnetic field (B₀) and k-space trajectory mismatch. Here, we propose a self-correcting AUSFIDE method for joint correction of these two sources of error without requiring additional scans.
AUSFIDE signals were modeled in the presence of B₀ drift and k-space shifts. To estimate and correct for B₀ drift over the entire scan, phase navigators without spatial encoding were inserted before and after each multi-echo train. In addition, the intrinsic phase symmetry between SSFP-FID and SSFP-ECHO signals was exploited, enabling self-calibration of the k-space sampling trajectory. Numerical simulations and phantom experiments were performed for proof-of-concept validation, followed by in vivo brain imaging of 10 healthy subjects on two 3 T scanners. Measured parameters were analyzed in six brain regions, and test-retest reproducibility was evaluated on the two MRI platforms.
Simulations and phantom imaging consistently demonstrated that B₀ drift and trajectory errors resulted in image distortions and heavily overestimated R₂'. Experiments further revealed that these artifacts and quantification errors were substantially reduced by the proposed self-correction method. Inter-scan agreement of the quantified parameters was also improved after correction on both scanners.
The self-corrected AUSFIDE method is able to compensate for errors from B₀ drift and k-space trajectory mismatch, and thus is a potentially practical and reliable means for 3D transverse relaxometry.

PMID:
42823871
Bibliographic data and abstract were imported from PubMed on 02 Oct 2026.

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