The goal of this study is to make new contrast image from multiple contrast Magnetic Resonance Image (MRI) using deep learning with loss function specialized for multiple image processing. Our contrast-conversion deep neural network (CC-DNN) is an end-to-end architecture that trains the model to create one image (STIR image) from three images (T1-weighted, T2-weighted, and GRE images). And we propose a new loss function to take into account intensity differences, misregistration, and local intensity variations.
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