- 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.
- Code Of Conduct: I have read and accept the code of conduct.
- Remove If Rejected: Remove submission from public view if paper is rejected.