Keywords: Image Reconstruction, Machine Learning/Artificial Intelligence, Signal Processing, Sparsity-based Methods, Convolutional Dictionary LearningIn this work we consider three different variants physics-informed Neural Networks (PINNs) which use the convolutional dictionary learning framework for image reconsruction in dynamic cardiac MRI. Although all three NNs share the same mechanism for regularization, the iterative schemes differ because they are derived from different problem formulations. We compare the methdos in terms of reconstruction performance as well as stability with respect to the number of iterations. All three methods yield similarly accurate reconstructions. However, by construction, only one of the three methods defines a convergent reconstruction algorithm and is therefore stable w.r.t. to the number of iterations.
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