These scripts and data provide example code for the method described in:
Haskell et al. MRM 2019, "Network Accelerated Motion Estimation and Reduction (NAMER): Convolutional neural network guided retrospective motion correction using a separable motion model"
To start, run "train_namer_cnn.py". This script requires that keras with TensorFlow backend is installed on your machine. Next, move the final trainded model from the "models" folder to the main directory, and update the variable "cnn_model_name" in "namer_recon.m" with the proper name. Third, run "namer_recon.m" to run the NAMER method in MATLAB.
Here a previously trained network "namer_trained_model.h5" is included if you want to skip the CNN training step.
The empty directories "convergence_curves", "model_weights", and "models" start as empty but are used by the "train_namer_cnn.py" script.
This example was most recently tested using matlab 2017b, and uses that versions's syntax for optimization settings, etc.
train_namer_cnn.py- This script constructs and trains the CNN, using the training data in "training_data.mat". When it completes training, move the model from the "models" folder to the main directory where the "namer_recon.m" script is.
namer_recon.m - This script performs the separable cost function version of the NAMER method (Eqn 3 in Haskell et al. 2019), and corresponds to the result shown in the bottom left of Figure 4-B in the paper.
run_namer_cnn.py - This script evaluates all of the patches for a given input image and returns the output of the motion artifact detecting CNN.
numpy=1.21.3
scipy=1.7.1
keras=2.6.0
tensorflow=2.6.0
Parallel Computing Toolbox
Image Processing Toolbox
Optimization Toolbox