Deep Fully Convolutional Network for MR Fingerprinting

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

Research output: Contribution to conferencePaperpeer-review

Abstract / Description of output

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 languageUndefined/Unknown
Number of pages4
Publication statusPublished - 10 Jul 2019
EventThe 2nd International Conference on Medical Imaging with Deep Learning (MIDL) -
Duration: 1 Jul 2019 → …

Conference

ConferenceThe 2nd International Conference on Medical Imaging with Deep Learning (MIDL)
Period1/07/19 → …

Keywords / Materials (for Non-textual outputs)

  • cs.LG
  • stat.ML

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