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Magnetic Resonance Fingerprinting (MRF) methods typically rely on dictionarymatching to map the temporal MRF signals to quantitative tissue parameters.These methods suffer from heavy storage and computation requirements as thedictionary size grows. To address these issues, we proposed an end to end fullyconvolutional neural network for MRF reconstruction (MRF-FCNN), which firstlyemploy linear dimensionality reduction and then use neural network to projectthe data into the tissue parameters manifold space. Experiments on the MAGICdata demonstrate the effectiveness of the method.

A Fully Convolutional Network for MR Fingerprinting