Deep Fully Convolutional Network for MR Fingerprinting

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

Research output: Contribution to conferencePaper

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.
Original languageEnglish
Publication statusPublished - 10 Jul 2019

Cite this

Chen, D., Golbabaee, M., Gómez, P. A., Menzel, M. I., & Davies, M. (2019). Deep Fully Convolutional Network for MR Fingerprinting.

Deep Fully Convolutional Network for MR Fingerprinting. / Chen, Dongdong; Golbabaee, Mohammad; Gómez, Pedro A. ; Menzel, Marion I.; Davies, Mike.

2019.

Research output: Contribution to conferencePaper

Chen, D, Golbabaee, M, Gómez, PA, Menzel, MI & Davies, M 2019, 'Deep Fully Convolutional Network for MR Fingerprinting'.
Chen D, Golbabaee M, Gómez PA, Menzel MI, Davies M. Deep Fully Convolutional Network for MR Fingerprinting. 2019.
Chen, Dongdong ; Golbabaee, Mohammad ; Gómez, Pedro A. ; Menzel, Marion I. ; Davies, Mike. / Deep Fully Convolutional Network for MR Fingerprinting.
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