Deep Fully Convolutional Network for MR Fingerprinting

Dongdong Chen, Mohammad Golbabaee, Pedro A Gómez, Marion I Menzel, Mike E Davies

Keywords: Magnetic Resonance Fingerprinting, Deep Learning

Abstract: This work proposes an end-to-end deep fully convolutional neural network for MRF reconstruction (MRF-FCNN), which firstly employs linear dimensionality reduction and then uses a neural network to project the data into the tissue parameters. The MRF dictionary is only used for training the network and not during image reconstruction. We show that MRF-FCNN is capable of achieving accuracy comparable to the ground-truth maps thanks to capturing spatio-temporal data structures without a need for the non-scalable dictionary matching step used in the baseline reconstructions.

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