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Abstract #0640

Neural Network MR Image Reconstruction with AUTOMAP: Automated Transform by Manifold Approximation

Bo Zhu1,2,3, Jeremiah Z. Liu1,4, Bruce R. Rosen1,2, and Matthew S. Rosen1,2,3

1A.A. Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Boston, MA, United States, 2Harvard Medical School, Boston, MA, United States, 3Dept. of Physics, Harvard University, Cambridge, MA, United States, 4Department of Biostatistics, Harvard University, Boston, MA, United States

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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