Keywords: Heart, Machine Learning/Artificial Intelligence, ReconstructionWe introduce a deep kernel model for the recovery of free-breathing and ungated cardiac MRI from highly undersampled measurements. The proposed scheme uses the cascade of two deep convolutional neural networks for the kernel representation of images. The parameters of the two CNNs in the proposed method are learned from the undersampled measurements directly in this work and hence the framework is unsupervised. The main benefits of the proposed scheme are (a) the elimination of the empirical choice of the feature map and kernel function in the kernel method, and (b) the unsupervised nature of the proposed framework.
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