Unbalanced SSFP for Super-Resolution in MRI

Peter J. Lally, Paul Matthews, Neal K. Bangerter

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Purpose: To achieve rapid, low specific absorption rate (SAR) super-resolution imaging by exploiting the characteristic magnetization off-resonance profile in SSFP.

Theory and Methods: In the presented technique, low flip angle unbalanced SSFP imaging  is  used  to  acquire  a  series  of  images  at  a  low  nominal  resolution  that  are  then  combined  in  a  super-resolution  strategy  analogous  to  non-linear  structured  illumination microscopy. This is demonstrated in principle via Bloch simulations and synthetic phantoms, and the performance is quantified in terms of point-spread function (PSF) and SNR for gray and white matter from field strengths of 0.35T to 9.4T. A k-space reconstruction approach is proposed to account for B0 effects. This was applied to reconstruct super-resolution images from a test object at 9.4T.

Results: Artifact-free  super-resolution  images  were  produced  after  incorporating  sufficient preparation time for the magnetization to approach the steady state. High-resolution images of a test object were obtained at 9.4T, in the presence of consider-able  B0  inhomogeneity.  For  gray  matter,  the  highest  achievable  resolution  ranges  from 3% of the acquired voxel dimension at 0.35T, to 9% at 9.4T. For white matter, this corresponds to 3% and 10%, respectively. Compared to an equivalent segmented gradient echo acquisition at the optimal flip angle, with a fixed TR of 8 ms, gray mat-ter has up to 34% of the SNR at 9.4T while using a ×10 smaller flip angle. For white matter, this corresponds to 29% with a ×11 smaller flip angle.

Conclusion: This approach achieves high degrees of super-resolution enhancement with minimal RF power requirements.
Original languageAmerican English
JournalMagnetic Resonance in Medicine
Volume85
Issue number5
DOIs
StatePublished - May 2021
Externally publishedYes

Keywords

  • SSFP
  • spatial encoding
  • structured illumination microscopy
  • super-resolution

EGS Disciplines

  • Electrical and Computer Engineering

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