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. Author manuscript; available in PMC: 2021 Jan 1.
Published in final edited form as: Proc IEEE Inst Electr Electron Eng. 2019 Sep 11;108(1):69–85. doi: 10.1109/JPROC.2019.2936998

Fig. 4.

Fig. 4.

Diagram of a convolutional neural network (CNN) proposed for MRF pattern recognition. The CNN takes a measured signal evolution from an undersampled MRF scan as input. Although not shown here, the signal is divided into two channels for the real and imaginary parts. Next there are 4-6 convolutional layers (note that only one layer is shown for clarity), an average pooling layer, and 4-6 fully-connected layers (again, only one layer is shown for clarity). The output is an estimate for the T1 and T2 value at the target pixel.