Keywords: Image Reconstruction, Machine Learning/Artificial Intelligence, Interpretable Machine Learning, Algorithmic Unrolling, Iterative Neural Networks
We propose a method for estimating spatio-temporal regularization parameter-maps to be used for dynamic cardiac MR image reconstruction using total variation (TV)-minimization. Based on recent developments in algorithmic unrolling using Neural Networks (NNs), our approach uses two sub-networks. The first one predicts a spatio-temporal regularization parameter-map from an input image. Then, a second sub-network approximately solves a TV-reconstruction problem which is formulated with the estimated regularization parameter-map. We show that the proposed method can be used to further improve the TV-reconstructions compared to using only one single scalar regularization parameter or two regularization parameters for space and time.
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