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Regularization by Adversarial Learning for Ultrasound Elasticity Imaging
[article]
2021
arXiv
pre-print
Classical model-based imaging methods for ultrasound elasticity inverse problem require prior constraints about the underlying elasticity patterns, while finding the appropriate hand-crafted prior for each tissue type is a challenge. In contrast, standard data-driven methods count solely on supervised learning on the training data pairs leading to massive network parameters for unnecessary physical model relearning which might not be consistent with the governing physical models of the imaging
arXiv:2106.00167v2
fatcat:v7mvvuwdtneuvg34wj3mryrhzm