Abstract #3573

Dictionary-based convolutional neural network (CNN) for MR Fingerprinting with highly undersampled data

Yong Chen^1,2, Zhenghan Fang³, and Weili Lin^1,2

¹Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, ²Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, ³Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States

In this study, we proposed a framework to generate simulated training dataset to train a convolutional neural network, which can be applied to highly undersampled MR Fingerprinting images to extract quantitative tissue properties. This eliminates the necessity to acquire training dataset from multiple subjects and has the potential to enable wide applications of deep learning techniques in quantitative imaging using MR Fingerprinting.

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