sampling_rotate.mov
This repository provides implementation codes for "Jointly estimating parametric maps of multiple diffusion models from undersampled q-space data: A comparison of three deep learning approaches."
To run Python codes provided in this repository, create a Python environment:
$ conda create -p /path_to_env/env_name python=3.x
and install the follwoing packages
$ conda install -p /path_to_env/env_name/ -c anaconda numpy
$ conda install -p /path_to_env/env_name/ -c conda-forge matplotlib
$ conda install -p /path_to_env/env_name/ -c conda-forge nibabel
$ conda install -p /path_to_env/env_name/ -c anaconda h5py
$ conda install -p /path_to_env/env_name/ -c conda-forge tqdm
$ conda install -p /path_to_env/env_name/ -c conda-forge argparse
$ conda install -p /path_to_env/env_name/ pytorch torchvision torchaudio cudatoolkit=x.x -c pytorch
where cudatoolkit=x.x in the last command depends on the installed cude version, e. g. 10.2 or 11.3. After these activate your environment using:
$ conda activate /path_to_env/env_name
The 1D-qDL, as shown in the above block diagram, uese fully connected layers to jointly estimate parametric diffusion maps on a per-voxel basis. The implementation codes are under ./1D-qDL/. The training data are not provided in this repository, but one test dataset, trained models for three undersampling patterns, and the test results can be found at ./Data/.
Training:
usage: python train_1d.py [-h] [--sampling SAMPLING] [--batch_size BATCH_SIZE]
[--num_of_channels NUM_OF_CHANNELS]
[--num_of_layers NUM_OF_LAYERS]
[--num_of_hidden NUM_OF_HIDDEN] [--drop_out DROP_OUT]
[--epochs EPOCHS] [--lr LR] [--data_path DATA_PATH]
Training 1D-qDL
optional arguments:
-h, --help show this help message and exit
--sampling SAMPLING Q-space undersampling pattern name
--batch_size BATCH_SIZE
Training batch size
--num_of_channels NUM_OF_CHANNELS
Number of qDL input channels
--num_of_layers NUM_OF_LAYERS
Number of qDL layers
--num_of_hidden NUM_OF_HIDDEN
Number of hidden nodes in qDL
--drop_out DROP_OUT Drop out probability in qDL
--epochs EPOCHS Number of training epochs
--lr LR Initial learning rate
--data_path DATA_PATH
Path to the data
Testing:
usage: python test_1d.py [-h] [--sampling SAMPLING] [--batch_size BATCH_SIZE]
[--num_of_channels NUM_OF_CHANNELS]
[--num_of_layers NUM_OF_LAYERS]
[--num_of_hidden NUM_OF_HIDDEN] [--drop_out DROP_OUT]
[--data_path DATA_PATH] [--test_cases TEST_CASES]
1D-qDL testing
optional arguments:
-h, --help show this help message and exit
--sampling SAMPLING Q-space undersampling pattern name
--batch_size BATCH_SIZE
Testing batch size
--num_of_channels NUM_OF_CHANNELS
Number of qDL input channels
--num_of_layers NUM_OF_LAYERS
Number of qDL layers
--num_of_hidden NUM_OF_HIDDEN
Number of hidden nodes in qDL
--drop_out DROP_OUT Drop out probability in qDL
--data_path DATA_PATH
Path to the data
--test_cases TEST_CASES
List of test subjects ids
The 2D-CNN uses convolutional blocks with residual connections to jointly estimate diffusion parametric maps on a per-slice basis. The implementation codes are under ./2D_CNN/.
Training:
usage: python train_2d.py [-h] [--sampling SAMPLING] [--batch_size BATCH_SIZE]
[--num_of_channels NUM_OF_CHANNELS] [--epochs EPOCHS]
[--lr LR] [--data_path DATA_PATH]
Training 2D-CNN
optional arguments:
-h, --help show this help message and exit
--sampling SAMPLING Q-space undersampling pattern name
--batch_size BATCH_SIZE
Training batch size
--num_of_channels NUM_OF_CHANNELS
Number of CNN input channels
--epochs EPOCHS Number of training epochs
--lr LR Initial learning rate
--data_path DATA_PATH
Path to the data
Testing:
usage: python test_2d.py [-h] [--sampling SAMPLING] [--batch_size BATCH_SIZE]
[--num_of_channels NUM_OF_CHANNELS] [--data_path DATA_PATH]
[--test_cases TEST_CASES]
2D-CNN testing
optional arguments:
-h, --help show this help message and exit
--sampling SAMPLING Q-space undersampling pattern name
--batch_size BATCH_SIZE
Testing batch size
--num_of_channels NUM_OF_CHANNELS
Number of CNN input channels
--data_path DATA_PATH
Path to the data
--test_cases TEST_CASES
List of test subjects ids
The MESC-SD uses a dictionary-based sparse coding representation to jointly estimate parametric diffusion maps using 3D input patches. The implementation codes are under ./3D_MESC_SD/.
Training:
usage: python train_3d.py [-h] [--sampling SAMPLING] [--batch_size BATCH_SIZE]
[--num_of_channels NUM_OF_CHANNELS]
[--num_of_voxels NUM_OF_VOXELS]
[--num_hidden_lstm NUM_HIDDEN_LSTM]
[--num_hidden_fc NUM_HIDDEN_FC] [--epochs EPOCHS] [--lr LR]
[--data_path DATA_PATH]
Training MESC-SD
optional arguments:
-h, --help show this help message and exit
--sampling SAMPLING Q-space undersampling pattern name
--batch_size BATCH_SIZE
Training batch size
--num_of_channels NUM_OF_CHANNELS
Number of MESC_SD input channels
--num_of_voxels NUM_OF_VOXELS
Number of voxels in 3D input patches
--num_hidden_lstm NUM_HIDDEN_LSTM
Number of hidden nodes in LSTM units
--num_hidden_fc NUM_HIDDEN_FC
Number of hidden nodes in FC layers
--epochs EPOCHS Number of training epochs
--lr LR Initial learning rate
--data_path DATA_PATH
Path to the data
Testing:
usage: python test_3d.py [-h] [--sampling SAMPLING] [--batch_size BATCH_SIZE]
[--num_of_channels NUM_OF_CHANNELS]
[--num_of_voxels NUM_OF_VOXELS]
[--num_hidden_lstm NUM_HIDDEN_LSTM]
[--num_hidden_fc NUM_HIDDEN_FC] [--data_path DATA_PATH]
[--test_cases TEST_CASES]
MESC-SD testing
optional arguments:
-h, --help show this help message and exit
--sampling SAMPLING Q-space undersampling pattern name
--batch_size BATCH_SIZE
Testing batch size
--num_of_channels NUM_OF_CHANNELS
Number of MESC_SD input channels
--num_of_voxels NUM_OF_VOXELS
Number of voxels in 3D input patches
--num_hidden_lstm NUM_HIDDEN_LSTM
Number of hidden nodes in LSTM units
--num_hidden_fc NUM_HIDDEN_FC
Number of hidden nodes in FC layers
--data_path DATA_PATH
Path to the data
--test_cases TEST_CASES
List of test subjects ids`