It has been widely observed that real-world data presented in high dimensional space tend to lie along a nonlinear manifold with much lower dimensionality. The reduced dimensionality manifold captures intrinsic data properties such as sparsity in a transform domain. We describe here an automated neural network framework that exploits the universal function approximation of multilayer perceptron regression and the manifold learning properties demonstrated by autoencoders to enable a new robust generalized reconstruction methodology. We demonstrate this approach over a variety of MR image acquisition strategies, showing excellent immunity to noise and acquisition artifacts.
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