Chen, Dongdong

Davies, Mike E.

Golbabaee, Mohammad

Gómez, Pedro A.

Menzel, Marion I.

English

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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Deep Fully Convolutional Network for MR Fingerprinting

https://www.research.ed.ac.uk/files/146228217/1809.01749v2.pdf

Deep Fully Convolutional Network for MR Fingerprinting

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