Deep Learning (DL) MR image reconstruction from undersampled data involves minimization of a loss function. The loss function to be minimized drives the DL training process and thus determine the features learned. Usually, a loss function such as mean squared error or mean absolute error is used as the similarity metric. Minimizing such loss function may not always predict visually pleasing images required by the radiologist. In order to predict visually appealing MR images in this work, we propose to use the difference of structural similarity as a regularizer along with the mean squared loss.
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