Journal Article10.1002/JMRI.23632
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.
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Abstract: Traditionally, magnetic resonance imaging (MRI) of flow using phase contrast (PC) methods is accomplished using methods that resolve single-directional flow in two spatial dimensions (2D) of an individual slice. More recently, three-dimensional (3D) spatial encoding combined with three-directional velocity-encoded phase contrast MRI (here termed 4D flow MRI) has drawn increased attention. 4D flow MRI offers the ability to measure and to visualize the temporal evolution of complex blood flow patterns within an acquired 3D volume. Various methodological improvements permit the acquisition of 4D flow MRI data encompassing individual vascular structures and entire vascular territories such as the heart, the adjacent aorta, the carotid arteries, abdominal, or peripheral vessels within reasonable scan times. To subsequently analyze the flow data by quantitative means and visualization of complex, three-directional blood flow patterns, various tools have been proposed. This review intends to introduce currently used 4D flow MRI methods, including Cartesian and radial data acquisition, approaches for accelerated data acquisition, cardiac gating, and respiration control. Based on these developments, an overview is provided over the potential this new imaging technique has in different parts of the body from the head to the peripheral arteries.
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Citations
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.
Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
TL;DR: In this paper, a machine learning framework is proposed to constrain the output of deep neural networks such that their predictions satisfy the conservation of mass and momentum principles, which can return physically consistent predictions for velocity, pressure and wall displacement pulse wave propagation.
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