Version 1.0

Features

• Simulated data can be generated using either simple stripe artifacts or more realistic flat fields
• A neural network can be trained on this dataset with the following modes:
  • base: input whole sinograms, generate whole sinograms
  • window: input windowed sinograms, generate windowed sinograms; windows are automatically created from inputs
  • full: input whole sinograms, generate whole sinograms; whole sinograms are created by concatenating windows
  • mask: input masked sinograms, generate masked sinograms; masks are automatically created from inputs
  • simple: same as mask, but masks are created from both inputs and targets
• Neural networks can also be trained using LSGANs (rather than cGANs) with any of the above modes
• A trained network can then be tested, outputting the following test statistics:
  • MAE, L2 norm, MSE, Gradient Difference, Dice Coefficient, IoU, Histogram Intersection, Structural Similarity and PSNR