DualReconstruction

This reposity is organized mainly for an image decomposition algorithm which is proposed to solve the image reconstruction and image-domain decomposition problem in Dual-energy Computed Tomography (DECT).

The algorithm is designed based on deep learning paradigm. For more theoretical details, please go to Deep Learning and Material Decomposition Using DECT.

The code is currently based on python 3.6, Tensorflow 1.4.0 and ODL in Windows 7 platform.

• data: contains 3 paths.
  • system_matrix: the system matrix used in the reconstruction algorithms. You can generate the matrix by running the 'iterative_reconstruction.m' or 'FBP_reconstruction.m' file in the src path.
  • testing_set: The data used for testing.
  • training_set: The data used for training the deep model. Both training and testing set can be download from here(Extraction number: t4ya).
    
• log: save the Tensorflow log file in training process.
• model: save the trained model
• result: save the result generated by the reconstruction algorithms.
• src: the codes for the proposed algorithm and two other competing ones:
  • Filter back projection (FBP) followed by direct matrix inversion (FBP_reconstruction.m)
  • Combined iterative reconstruction and image decomposition (iterative_reconstruction.m). Related paper: Combined iterative reconstruction and image-domain decomposition for dual energy CT using total-variation regularization.
  • The proposed deep model (main.py). You can start to train the proposed deep model via the cmd:

  python main.py --dataset="../data/training_set/" --mode="train" --model_name="your-saved-model-result-name" --lr = 0.0001 --epoch=30 --model_step=1000 --batch_size=1

  After finishing the training process, you can test the trained model via the cmd:

  python main.py --dataset="../data/testing_set/" --mode="feedforward" --model_name="your-saved-model-result-name" --checkpoint="../model/your-saved-model-result-name"

Contact

Email: [email protected]