Abstract #3377

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

Ouri Cohen^1,2, Bo Zhu^1,2, and Matthew S. Rosen^1,2

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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