qianchne/Qspace_Deep_Learning

Implementation codes for "Jointly estimating parametric maps of multiple diffusion models"

Q-space Deep Learning

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."

Setting up a Python env and installing required packages

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

Training and Testing the 1D-qDL network

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 

Training and testing the 2D-CNN network

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 

Training and Testing the MESC-SD network

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`