Wasserstein GAN super-resolution recovers near-wall velocities in 4D Flow MRI with vNRMSE 6.9% versus 9.6% for non-adversarial baseline, though training stability depends on loss function choice.
Takehara, 4d flow when and how?, La radiologia medica 125 (9) (2020) 838–850
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI
Wasserstein GAN super-resolution recovers near-wall velocities in 4D Flow MRI with vNRMSE 6.9% versus 9.6% for non-adversarial baseline, though training stability depends on loss function choice.