Proceedings ArticleDOI
4D real-time phase-contrast flow MRI with sparse sampling
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TLDR
A novel model-based imaging method is presented, which integrates low-rank modeling with parallel imaging, to enable 4D real-time PC MRI without ECG gating and respiration control and is able to resolve beat-by-beat flow variations, which cannot be achieved by the conventional cine-based approach.Abstract:
Conventional phase-contrast (PC) MRI relies on electrocardiogram (ECG)-synchronized cine acquisition and respiration control. It often results in relatively low data acquisition efficiency, and is unable to assess blood flow variabilities. Real-time imaging is a promising technique to overcome these limitations; however, it results in a challenging image reconstruction problem with highly-undersampled (k; t)-space data. This paper presents a novel model-based imaging method, which integrates low-rank modeling with parallel imaging, to enable 4D real-time PC MRI without ECG gating and respiration control. The proposed method achieves an isotropic spatial resolution of 2.4 mm and temporal resolution of 35.2 ms, with three directional flow encodings. Moreover, it is able to resolve beat-by-beat flow variations, which cannot be achieved by the conventional cine-based approach. The proposed method was evaluated with in vivo experiments with one healthy subject and one arrhythmic patient. For the first time, we demonstrate the feasibility of 4D real-time PC MRI.read more
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Dissertation
Magnetic Resonance Augmented Cardiopulmonary Exercise Testing
TL;DR: An alternative non-invasive approach to exercise testing combining real-time magnetic resonance imaging (MRI) flow measurement with respiratory gas analysis, using a system modified for safe use in the MRI environment is developed.
Journal ArticleDOI
Accelerated 4D‐flow MRI with 3‐point encoding enabled by machine learning
TL;DR: In this paper , a convolutional neural network (CNN) was used to produce three directional velocities from three flow encodings, without requiring a fourth reference scan measuring background phase.
Journal ArticleDOI
Motion‐resolved real‐time 4D flow MRI with low‐rank and subspace modeling
TL;DR: In this article , a motion-resolved real-time four-dimensional (4D) flow MRI method was developed, which enables quantification and visualization of blood flow velocities with three-directional flow encodings.
Journal ArticleDOI
Accelerated partial separable model using dimension-reduced optimization technique for ultra-fast cardiac MRI
TL;DR: This work proposes to fully exploit the dimension-reduction property to accelerate the PS model, and optimize the data consistency term, and use a Tikhonov regularization term based on Frobenius norm of temporal difference, resulting in a totally dimensionreduced optimization technique.
References
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Journal ArticleDOI
4D flow cardiovascular magnetic resonance consensus statement
Petter Dyverfeldt,Malenka M. Bissell,Alex J. Barker,Ann F. Bolger,Ann F. Bolger,Carl-Johan Carlhäll,Tino Ebbers,Christopher J. Francios,Alex Frydrychowicz,Julia Geiger,Daniel Giese,Michael D. Hope,Philip J. Kilner,Sebastian Kozerke,Saul G. Myerson,Stefan Neubauer,Oliver Wieben,Michael Markl +17 more
TL;DR: It is described that 4D Flow CMR can be clinically advantageous because placement of a single acquisition volume is straightforward and enables flow through any plane across it to be calculated retrospectively and with good accuracy.
Journal ArticleDOI
4D flow MRI
TL;DR: This review intends to introduce currently used 4D flow MRI methods, including Cartesian and radial data acquisition, approaches for acceleratedData acquisition, cardiac gating, and respiration control, and an overview over the potential this new imaging technique has in different parts of the body from the head to the peripheral arteries.
Proceedings ArticleDOI
Spatiotemporal imagingwith partially separable functions
TL;DR: This paper describes a new way for Spatiotemporal imaging using partially separable functions that admits highly sparse sampling of the data space, providing an effective way to achieve high Spatiotsemporal resolution.
Proceedings ArticleDOI
Spatiotemporal Imaging with Partially Separable Functions
TL;DR: This paper describes a new way for spatiotemporal imaging using partially separable functions that admits highly sparse sampling of the data space, providing a novel, effective way to achieve high spatiotmporal resolution.
Journal ArticleDOI
Image Reconstruction From Highly Undersampled $( {\bf k}, {t})$ -Space Data With Joint Partial Separability and Sparsity Constraints
TL;DR: The proposed method combines the complementary advantages of PS and sparsity constraints using a unified formulation, achieving significantly better reconstruction performance than using either of these constraints individually.