Matrix completion-based reconstruction for undersampled magnetic resonance fingerprinting data

Doneva, Mariya; Amthor, Thomas; Koken, Peter; Sommer, Karsten; Börnert, Peter

doi:10.1016/j.mri.2017.02.007

Cited by 68 publications

(113 citation statements)

References 15 publications

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“…NR is fulfilled, artifact free images can theoretically be reconstructed with Eq. [17]. Our LR inversion embodies some concepts akin to the methods described in Ref.…”

Section: Discussionmentioning

confidence: 99%

“…In the case of the LR inverse problem (Eq. [17]), spatial blurring can be observed, most prominently in the T 1 map. This is also reflected in Figure 2b, where the NRMSE increase at high R values is strongest for T 1 .…”

Section: Reconstructions With R ¼mentioning

confidence: 94%

“…[17]. The second summand compares the splitted variablex to its projection onto the dictionaryDD Hx .…”

Section: Lr Alternating Directions Methods Of Multipliersmentioning

confidence: 99%

“…; 20g. Equation [17] is solved by a CG algorithm with 100 iterations after initializing it with zero. The low rank ADMM reconstructions were performed with 20 CG steps for solving Eq.…”

Section: Image Reconstructionmentioning

confidence: 99%

“…Therefore, global convergence of MRF reconstruction algorithmsincluding the proposed one-is usually not guaranteed and a good initial guess is essential. The present article addresses this issue by a low rank (LR) approximation (15)(16)(17)(18)(19)(20)(21)(22) of the voxels' signal evolution, to which the alternating direction method of multipliers (ADMM) (13,14,23) is applied. The proposed algorithm splits the MRF reconstruction problem into two sub-problems that are solved alternately: A linear inverse problem with data consistency at its core and a dictionary matching step.…”

Section: Introductionmentioning

confidence: 99%

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Low rank alternating direction method of multipliers reconstruction for MR fingerprinting

Assländer

Cloos

Knoll

et al. 2017

Magnetic Resonance in Med

176

298

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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 transformations. Also, the LR approximation improves the conditioning of the problem, which is further improved by extending the LR inverse problem to an augmented Lagrangian that is solved by the alternating direction method of multipliers. The root mean square error and the noise propagation are analyzed in simulations. For verification, in vivo examples are provided. Results: The proposed LR alternating direction method of multipliers approach shows a reduced root mean square error compared to the original fingerprinting reconstruction, to a LR approximation alone and to an alternating direction method of multipliers approach without a LR approximation. Incorporating sensitivity encoding allows for further artifact reduction. Conclusion:The proposed reconstruction provides robust convergence, reduced computational burden and improved image quality compared to other magnetic resonance fingerprinting reconstruction approaches evaluated in this study. Magn Reson Med 79:83-96,

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“…NR is fulfilled, artifact free images can theoretically be reconstructed with Eq. [17]. Our LR inversion embodies some concepts akin to the methods described in Ref.…”

Section: Discussionmentioning

confidence: 99%

Section: Reconstructions With R ¼mentioning

confidence: 94%

“…[17]. The second summand compares the splitted variablex to its projection onto the dictionaryDD Hx .…”

Section: Lr Alternating Directions Methods Of Multipliersmentioning

confidence: 99%

“…; 20g. Equation [17] is solved by a CG algorithm with 100 iterations after initializing it with zero. The low rank ADMM reconstructions were performed with 20 CG steps for solving Eq.…”

Section: Image Reconstructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Low rank alternating direction method of multipliers reconstruction for MR fingerprinting

Assländer

Cloos

Knoll

et al. 2017

Magnetic Resonance in Med

176

298

View full text Add to dashboard Cite

show abstract

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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Cardiac MR Fingerprinting: Overview, Technical Developments, and Applications

Rashid,

Cruz,

Seiberlich

et al. 2023

Magnetic Resonance Imaging

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Cardiovascular magnetic resonance (CMR) is an established imaging modality with proven utility in assessing cardiovascular diseases. The ability of CMR to characterize myocardial tissue using T1‐ and T2‐weighted imaging, parametric mapping, and late gadolinium enhancement has allowed for the non‐invasive identification of specific pathologies not previously possible with modalities like echocardiography. However, CMR examinations are lengthy and technically complex, requiring multiple pulse sequences and different anatomical planes to comprehensively assess myocardial structure, function, and tissue composition. To increase the overall impact of this modality, there is a need to simplify and shorten CMR exams to improve access and efficiency, while also providing reproducible quantitative measurements. Multiparametric MRI techniques that measure multiple tissue properties offer one potential solution to this problem. This review provides an in‐depth look at one such multiparametric approach, cardiac magnetic resonance fingerprinting (MRF). The article is structured as follows. First, a brief review of single‐parametric and (non‐Fingerprinting) multiparametric CMR mapping techniques is presented. Second, a general overview of cardiac MRF is provided covering pulse sequence implementation, dictionary generation, fast k‐space sampling methods, and pattern recognition. Third, recent technical advances in cardiac MRF are covered spanning a variety of topics, including simultaneous multislice and 3D sampling, motion correction algorithms, cine MRF, synthetic multicontrast imaging, extensions to measure additional clinically important tissue properties (proton density fat fraction, T2*, and T1ρ), and deep learning methods for image reconstruction and parameter estimation. The last section will discuss potential clinical applications, concluding with a perspective on how multiparametric techniques like MRF may enable streamlined CMR protocols.Level of Evidence5Technical EfficacyStage 1

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Matrix completion-based reconstruction for undersampled magnetic resonance fingerprinting data

Cited by 68 publications

References 15 publications

Low rank alternating direction method of multipliers reconstruction for MR fingerprinting

Low rank alternating direction method of multipliers reconstruction for MR fingerprinting

Magnetic resonance fingerprinting review part 2: Technique and directions

Cardiac MR Fingerprinting: Overview, Technical Developments, and Applications

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