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Abstract #4711

Enforcing Structural Similarity in Deep Learning MR Image Reconstruction

Kamlesh Pawar1,2, Zhaolin Chen1,3, N Jon Shah4, and Gary F Egan1,2

1Monash Biomedical Imaging, Monash University, Melbourne, Australia, 2School of Psychology, Monash University, Melbourne, Australia, 3Electrical and Computer System Engineering, Monash University, Melbourne, Australia, 4Institute of Medicine, Research Centre Juelich, Juelich, Australia

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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