Human radiologists gain experience from reading numerous MRI images to recognize pathologies and anatomical structures. To integrate this experience into deep learning approaches, two major components are required: We need both a suitable network architecture and a suitable loss function that measures the similarity between the reconstruction and the reference. In this work, we compare pixel-based and patch-based loss functions. We show that it is beneficial to consider other loss functions than the squared L2 norm to get a better representation of the human perceptual system and thus to preserve the texture in the tissue.
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