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4D flow MRI suffers from different sources of noise and aliasing artifacts. Recent denoising techniques are time-consuming or dependent of parameter estimation. We developed a physics informed neural network with divergence-free vector potential as a non-parametric denoising technique for 4D flow MRI. Results from simulated pulsatile flow and CFD vascular model shows significant noise reduction and aliasing correction. Future work includes comparison with other techniques on different types of data and uncertainty quantification.
Bisbal et al. (Wed,) studied this question.
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