Browsing by Subject "Parameter mapping"

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Open Access
Constrained ellipse fitting for efficient parameter mapping with phase-cycled bSSFP MRI
(IEEE, 2021-08-05) Keskin, K.; Yılmaz, Uğur; Çukur, Tolga
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.
Open Access
Efficient parameter mapping for magnetic resonance imaging
(2019-07) Keskin, Kübra
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.