Constrained Ellipse Fitting for Efficient Parameter Mapping with Phase-cycled bSSFP MRI

TL;DR

This paper introduces CELF, a new constrained ellipse fitting method that enhances the efficiency and accuracy of parameter mapping in phase-cycled bSSFP MRI, reducing the number of acquisitions needed for high-quality imaging.

Contribution

CELF employs geometrical constraints and dictionary-based identification within an elliptical signal model to improve parameter estimation in phase-cycled bSSFP MRI.

Findings

Accurately maps off-resonance with as few as 4 acquisitions.

Produces banding-free bSSFP images.

Estimation of relaxation parameters is limited by microstructural biases.

Abstract

Balanced steady-state free precession (bSSFP) imaging enables high scan efficiency in MRI, but differs from conventional sequences in terms of elevated sensitivity to main field inhomogeneity and nonstandard T2/T1-weighted tissue contrast. To address these limitations, multiple bSSFP images of the same anatomy are commonly acquired with a set of different RF phase-cycling increments. Joint processing of phase-cycled acquisitions serves to mitigate sensitivity to field inhomogeneity. Recently phase-cycled bSSFP acquisitions were also leveraged to estimate relaxation parameters based on explicit signal models. While effective, these model-based methods often involve a large number of acquisitions (N~10-16), degrading scan efficiency. Here, we propose a new constrained ellipse fitting method (CELF) for parameter estimation with improved efficiency and accuracy in phase-cycled bSSFP MRI. CELF is based on the elliptical signal model framework for complex bSSFP signals; and it introduces geometrical constraints on ellipse properties to improve estimation efficiency, and dictionary-based identification to improve estimation accuracy. CELF generates maps of T1, T2, off-resonance and on-resonant bSSFP signal by employing a separate B1 map to mitigate sensitivity to flip angle variations. Our results indicate that CELF can produce accurate off-resonance and banding-free bSSFP maps with as few as N=4 acquisitions, while estimation accuracy for relaxation parameters is notably limited by biases from microstructural sensitivity of bSSFP imaging.