A residual CNN, adapted from a spatial super-resolution network, temporally upsamples 4D Flow MRI by 2x with lower error than linear or sinc interpolation and generalizes to in-vivo data.
4D Flow MRI quantification of blood flow patterns, turbulence and pressure drop in normal and stenotic prosthetic heart valves.Magnetic Resonance Imag- ing, 55:118–127, 1 2019
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Deep learning for temporal super-resolution 4D Flow MRI
A residual CNN, adapted from a spatial super-resolution network, temporally upsamples 4D Flow MRI by 2x with lower error than linear or sinc interpolation and generalizes to in-vivo data.