2017
DOI: 10.1002/mrm.26639
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Low rank alternating direction method of multipliers reconstruction for MR fingerprinting

Abstract: Purpose: The proposed reconstruction framework addresses the reconstruction accuracy, noise propagation and computation time for magnetic resonance fingerprinting. Methods: Based on a singular value decomposition of the signal evolution, magnetic resonance fingerprinting is formulated as a low rank (LR) inverse problem in which one image is reconstructed for each singular value under consideration. This LR approximation of the signal evolution reduces the computational burden by reducing the number of Fourier … Show more

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Cited by 171 publications
(308 citation statements)
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“…b), for the in vivo and phantom experiments, respectively. Multiparametric maps (PD, T 1 , T 2 , B 1 + ) were reconstructed using an iterative approach . A dictionary with presimulated fingerprints was created using extended phase graphs .…”
Section: Methodsmentioning
confidence: 99%
“…b), for the in vivo and phantom experiments, respectively. Multiparametric maps (PD, T 1 , T 2 , B 1 + ) were reconstructed using an iterative approach . A dictionary with presimulated fingerprints was created using extended phase graphs .…”
Section: Methodsmentioning
confidence: 99%
“…This can result in relatively long acquisition time, particularly for 3D volumetric imaging. Recent studies that utilized sliding-window (SW) reconstruction (Cao et al, 2016), and sparse and/or low-rank models (Assländer et al, 2017; Davies et al, 2014; Liao et al, 2016; Mazor et al, 2016; Zhao et al, 2017, 2016) can mitigate this aliasing issue, and accelerate 2D MRF acquisition by reducing the number of acquisition time points. On the other hand, applications of Simultaneous Multi-Slice (SMS) to MRF (Jiang et al, 2016; Ye et al, 2016a, 2016b) have also improved the time-efficiency of MRF by simultaneously encoding multiple slices and accelerate the data acquisition process.…”
Section: Introductionmentioning
confidence: 99%
“…Assländer et al 37 , who embed the left singular vectors in the solution. These methods show that exploiting the redundancy via a low-rank based solution improves the results compared to a sparsity approach.…”
Section: Introductionmentioning
confidence: 99%