Image-To-Image Generative Adversarial Networks for Synthesizing Perfusion Parameter Maps from DSC-MR Images In Cerebrovascular Disease

Aim

The aim of the project was to synthesize perfusion parameter maps from dynamic susceptibility contrast magnetic resonance imaging. For this, we implemented a pix2pix GAN as well as a pix2pix GAN with a temporal component, coined the temp-pix2pix GAN.

Files

• config.py: configuration file
• train.py: script for running both the pix2pix and temp-pix2pix GAN
• model.py: GAN model file
• evaluate.py: script for evaluating the generated perfusion maps
• utils: folder containing all helper functions
• dsc-perf.yml: conda environment to be able to run all scripts

How to run

1. Install the conda environment using the dsc-perf.yml file
2. Specify configurations and paths in config.py
3. To run the GAN with differential privacy, specify the hyperparameters, e.g.:

   python3 train.py \
       --trial "GAN1" \               # trial name
       --data dataset1 \              # name of dataset
       --netG_type tmpandunet \       # Architecture of generator (tmpandunet or unet)
       --netD_type patch \            # Architecture of discriminator
       --inputseq DSC \               # input for G
       --outputseq Tmax \             # output for G
       --batch_size \                 # batch size
       --ngf 128 --ndf 64 \           # number of filters G and D
       --nr_layer_g 6 --nr_layer_d 2  # number of layers G and D
       --norm_layer_g batchnorm \     # type of normalization (G)
       --norm_layer_d batchnorm \     # type of normalization (D)
       --weight_init normal \         # weight initialization distribution
       --epochs 100 \                 # number of epochs
       --lrg 0.0001 --lrd 0.0001 \    # learning rate D and G
       --betas_g 0.5 0.999 \          # optimization parameters G
       --betas_d 0.5 0.999 \          # optimization parameters D
       --loss_g BCE --loss_d BCE \    # loss G and D
       --loss_reconstr l1 \           # reconstruction loss
       --loss_ratio \                 # ratio between adversarial and reconstruction loss for G
       --n_discr 1 \                  # number of D updates
       --upsampling True \            # Upsampling vs deconvolutional layer
       --seed 12                      # number for random seed                 

4. For generating the perfusion maps and evaluate them, run evaluate.py e.g.:

   python3 evaluate.py \
       --trial "GAN1"                 # trial name 
       --database dataset1 \          # name of dataset
       --dataset test \               # dataset split
       --epoch 99  \                  # determine epoch for generation
       --netG_type \                  # architecture of G
       --upsampling True \            # Upsampling vs deconvolutional layer
       --norm_layer_g batchnorm \     # type of normalization (G)
       --nr_layer_g 6 \               # number of layers G
       --ngf 128 \                    # number of filters G
       --seed 12                      # number for random seed

How to cite

The preprint for this study will soon be available. As soon as the paper is published, this will be updated