Fast quantitative MRI as a nonlinear tomography problem

Sbrizzi, Alessandro; Heide, Oscar van der; Cloos, Martijn A.; Toorn, Annette van der; Hoogduin, Hans; Luijten, Peter R.; Berg, Cornelis A.T. van den

doi:10.1016/j.mri.2017.10.015

Cited by 65 publications

(105 citation statements)

References 25 publications

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“…In addition, the proposed GIRF‐based signal model can be used for numerical sequence optimization or to predict the impact of eddy currents on other sequences such as spoiled SSFP sequences or spin‐echo sequences. In addition, we believe that a rigorous understanding of the impact of eddy current on the signal evolution is crucial for quantitative imaging applications that require precise modeling of the physical MR acquisition …”

Section: Discussionmentioning

confidence: 99%

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Prospective GIRF‐based RF phase cycling to reduce eddy current‐induced steady‐state disruption in bSSFP imaging

Bruijnen

Stemkens

Berg

et al. 2019

Magnetic Resonance in Med

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Purpose To propose an explicit Balanced steady‐state free precession (bSSFP) signal model that predicts eddy current‐induced steady‐state disruptions and to provide a prospective, practical, and general eddy current compensation method. Theory and Methods Gradient impulse response functions (GIRF) were used to simulate trajectory‐specific eddy current‐induced phase errors at the end of a repetition block. These phase errors were included in bloch simulations to establish a bSSFP signal model to predict steady‐state disruptions and their corresponding image artifacts. The signal model was embedded in the MR system and used to compensate the phase errors by prospectively modifying the phase cycling scheme of the RF pulse. The signal model and eddy current compensation method were validated in phantom and in vivo experiments. In addition, the signal model was used to analyze pre‐existing eddy current mitigation methods, such as 2D tiny golden angle radial and 3D paired phase encoded Cartesian acquisitions. Results The signal model predicted eddy current‐induced image artifacts, with the zeroth‐order GIRF being the primary factor to predict the steady‐state disruption. Prospective RF phase cycling schemes were automatically computed online and considerably reduced eddy current‐induced image artifacts. The signal model provides a direct relationship for the smoothness of k‐space trajectories, which explains the effectiveness of phase encode pairing and tiny golden angle trajectory. Conclusions The proposed signal model can accurately predict eddy current‐induced steady‐state disruptions for bSSFP imaging. The signal model can be used to derive the eddy current‐induced phase errors required for trajectory‐specific RF phase cycling schemes, which considerably reduce eddy current‐induced image artifacts.

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Section: Discussionmentioning

confidence: 99%

“…In addition, we believe that a rigorous understanding of the impact of eddy current on the signal evolution is crucial for quantitative imaging applications that require precise modeling of the physical MR acquisition. 5,30…”

Section: Discussionmentioning

confidence: 99%

Prospective GIRF‐based RF phase cycling to reduce eddy current‐induced steady‐state disruption in bSSFP imaging

Bruijnen

Stemkens

Berg

et al. 2019

Magnetic Resonance in Med

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“…and

normalΔ B_{0} = 0

Hz. In previous work, the variable projection method (VARPRO) was utilized to separate the linear parameters (i.e., proton density) from the nonlinear parameters. The VARPRO method in principle requires computation (through SVD or QR decomposition) and storage of an orthogonal basis for the matrix

boldM

.…”

Section: Methodsmentioning

confidence: 99%

“…and ΔB 0 = 0 Hz. In previous work, 12 the variable projection method (VARPRO 29 ) was utilized to separate the linear parameters (i.e., proton density) from the nonlinear parameters. The VARPRO method in principle requires computation (through SVD or QR decomposition) and storage of an orthogonal basis for the matrix M. For the matrix sizes in the current work, that would be computationally infeasible and it is nontrivial to extend the VARPRO technique to matrix-free methods.…”

Section: Reconstructionsmentioning

confidence: 99%

High‐resolution in vivo MR‐STAT using a matrix‐free and parallelized reconstruction algorithm

et al. 2020

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MR‐STAT is a recently proposed framework that allows the reconstruction of multiple quantitative parameter maps from a single short scan by performing spatial localisation and parameter estimation on the time‐domain data simultaneously, without relying on the fast Fourier transform (FFT). To do this at high resolution, specialized algorithms are required to solve the underlying large‐scale nonlinear optimisation problem. We propose a matrix‐free and parallelized inexact Gauss–Newton based reconstruction algorithm for this purpose. The proposed algorithm is implemented on a high‐performance computing cluster and is demonstrated to be able to generate high‐resolution (1 mm × 1 mm in‐plane resolution) quantitative parameter maps in simulation, phantom, and in vivo brain experiments. Reconstructed T1 and T2 values for the gel phantoms are in agreement with results from gold standard measurements and, for the in vivo experiments, the quantitative values show good agreement with literature values. In all experiments, short pulse sequences with robust Cartesian sampling are used, for which MR fingerprinting reconstructions are shown to fail.

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“…Such approaches, which use one acquisition to quantify multiple properties, include inversion recovery True‐FISP for T 1 , T 2 , and proton density and QRAPMASTER to quantify T 1 , T 2 , proton density, and B1 field amplitude. Other quantitative methods for multiple properties include MRF spin tomography in the time domain to quantify T 1 , T 2 , and B1, and the multipathway multiecho imaging method for 3D quantification of T 1 , T 2 , T 2 *, B0, and B1. Quantification of additional properties, including T 1 , T 2 *, and magnetic susceptibility was demonstrated, and magnetization transfer was quantified along with R1 and R2 …”

mentioning

confidence: 99%

Magnetic resonance fingerprinting review part 2: Technique and directions

McGivney

Boyacıoğlu

Jiang

et al. 2019

Magnetic Resonance Imaging

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Magnetic resonance fingerprinting (MRF) is a general framework to quantify multiple MR‐sensitive tissue properties with a single acquisition. There have been numerous advances in MRF in the years since its inception. In this work we highlight some of the recent technical developments in MRF, focusing on sequence optimization, modifications for reconstruction and pattern matching, new methods for partial volume analysis, and applications of machine and deep learning. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:993–1007.

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Fast quantitative MRI as a nonlinear tomography problem

Cited by 65 publications

References 25 publications

Prospective GIRF‐based RF phase cycling to reduce eddy current‐induced steady‐state disruption in bSSFP imaging

Prospective GIRF‐based RF phase cycling to reduce eddy current‐induced steady‐state disruption in bSSFP imaging

High‐resolution in vivo MR‐STAT using a matrix‐free and parallelized reconstruction algorithm

Magnetic resonance fingerprinting review part 2: Technique and directions

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