Efficient parameter mapping for magnetic resonance imaging

Balanced steady-state free precession (bSSFP) is a magnetic resonance imaging (MRI) sequence that enables high signal-to-noise ratios in short scan times. However, it has elevated sensitivity to main eld inhomogeneity, which leads to banding artifacts near regions of relatively large o -resonance shifts. To suppress these artifacts, multiple bSSFP images of the same anatomy are commonly acquired with a set of di erent RF phase-cycling increments. Joint processing of phase-cycled acquisitions has long been employed to eliminate banding artifacts due to eld inhomogeneity. Multiple bSSFP acquisitions can be further used for parameter mapping by exploiting the signal model of phase-cycled bSSFP. While model based approaches for mapping are e ective, they often need a large number of acquisitions, inherently limiting scan e ciency. In this thesis, we propose a new method for parameter mapping with improved e ciency and accuracy in phasecycled bSSFP MRI. The proposed method is based on the elliptical signal model framework for complex bSSFP signals; and introduces an observation about the signal's geometry to the constrained parameter mapping problem, such that the number of unknowns and thereby the required number of acquisitions can be reduced. It also leverages dictionary-based identi cation to improve estimation accuracy. Simulated, phantom and in vivo experiments demonstrate that the proposed method enables improved parameter mapping with fewer acquisitions.