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Abstract #3377

Characterization of Sparsely Trained Deep Learning Reconstruction of Noisy MR Fingerprinting Data

Ouri Cohen1,2, Bo Zhu1,2, and Matthew S. Rosen1,2

1Radiology, MGH Athinoula A. Martinos Center/Harvard Medical School, Charlestown, MA, United States, 2Physics, Harvard University, Cambridge, MA, United States

MR Fingerprinting offers the ability to obtain simultaneous tissue (T1,T2…) and hardware (B1, B0…) parameter maps in a fast acquisition time but is limited by the size of the reconstruction dictionary. In previous work we demonstrated that these issues can be overcome by reconstructing the data using a properly trained neural network. Here we characterize the accuracy of a neural network trained on sparse dictionaries for reconstruction of noisy data.

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