SC-GAN

The project repository for SC-GAN: 3D self-attention conditional GAN with spectral normalization for multi-modal neuroimaging synthesis

Data

The data directory should be organized as the following structure:

data
│
└───train
|   |
│   └─── subject 1
|   |         modality_1.nii.gz
|   |         modality_2.nii.gz
|   |         ...
|   |         target.nii.gz
│   └─── subject 2
|   |         modality_1.nii.gz
|   |         modality_2.nii.gz
|   |         ...
|   |         target.nii.gz
│   ...
|
└───val
|   |
│   └─── subject A
|   |         modality_1.nii.gz
|   |         modality_2.nii.gz
|   |         ...
|   |         target.nii.gz
│   └─── subject B
|   |         modality_1.nii.gz
|   |         modality_2.nii.gz
|   |         ...
|   |         target.nii.gz
│   ...
|   
└───test
    |
    └─── subject a
    |         modality_1.nii.gz
    |         modality_2.nii.gz
    |         ...
    └─── subject b
    |         modality_1.nii.gz
    |         modality_2.nii.gz
    |         ...
    ...

To evaluate SC-GAN, we used ADNI data. To access ADNI data, please visit: https://ida.loni.usc.edu/

Requirements

python 3 is required and python 3.6.9 was used in the study.

Requirements can be found at requirement.txt.

Please use pip install requirement.txt to install the requirements

Running the code

SC-GAN was assessed for positron emission tomography (PET), Fractional anisotropy (FA) and mean diffusivity (MD) synthesis from tasks using T1w and FLAIR magnetic resonance images (MRI).

Training:

Training script is at ./training

Use the following command to run the training script:

python training.py --trainig_size= --gpu= --epoches= --img_size= --data_dir= --modalities= --logdir=

configurations	meaning	default
--training_size	the number of training data to use	None
--gpu	gpu ID for training	None
--epoches	the number of training epoches	120
--img_size	input image size(same for three dimensions)	256
--data_dir	data directory	None
--modalities	modalities to use in the training. Last one is the name of target modality, the rests are names of input modalities(eg: modality1_modality2_..._target)	flair_t1w_av45
--logdir	directory to save tensorboard log	None

Add more parameters configuration to do hyperparameter tuning:

parameters	meaning	default values
--lr	learning rate	0.001
--g_reg	generator regularizer	0.001
--d_reg	discriminator regularizer	0.001
--reg	if using regularization	True
--l1_weight	l1 weight	200
--B_weight	B-rmse weight	200

Testing:

Testing script is at ./testing

Use the following command to run the testing script:

python testing.py --test_size= --gpu= --img_size= --data_dir= --modalities= --logdir= --output=

configurations	meaning	default
--test_size	the number of test data to use	None
--gpu	gpu ID for testing	None
--img_size	input image size(same for three dimensions)	256
--data_dir	data directory	None
--modalities	modalities to use in the testing. Last one is the name of target modality, the rests are names of input modalities(eg: modality1_modality2_..._target)	flair_t1w_av45
--log_dir	directory to read tensorboard log	None
--output	synthesis results directory	None

License

If you use this code for your research, please be familiar with the LICENSE and cite our paper.

Developer

Haoyu Lan
INI Microstructural imaging Group (IMG)
USC Stevens Neuroimaging and Informatics Institute
Keck School of Medicine, University of Southern California

Citation

Lan H, Toga AW, Sepehrband F. Three- dimensional self- attention conditional GAN with spectral normalization for multimodal neuroimaging synthesis. Magn Reson Med. 2021;00:1– 16. https://doi.org/10.1002/mrm.28819
[bioRxiv copy]