An implementation of 3D brain MRI super-resolution method by image gradient-tensor distance based patch clustering

• Super-Resolution of 3D Brain MRI With Filter Learning Using Tensor Feature Clustering [Paper]

• IEEE Access, 2022

• 3D Brain MRI Super-Resolution with Image Gradient Tensor Feature Clustering [Poster]

• Organization for Human Brain Mapping Annual Meeting, 2021

• Both Linux and Windows are supported.

• Package Required: numpy, numba, scipy, skimage, nibabel

• We got young-adult T1-weighted masked MRI brain Dataset from 'Human Connectome Project' (https://www.humanconnectome.org/study/hcp-young-adult)

• We used HCP-900 dataset with 30 images to train, 867 images to estimate metrics.

• Store your HR train data to 'train' folder, HR test data to 'test' folder. In test stage, data will be downscaled to estimate HR image.

.
├── train
|   ├── T1w_acpc_dc_restore_brain_id1.nii.gz
|   └── T1w_acpc_dc_restore_brain_id2.nii.gz
├── test
|   ├── T1w_acpc_dc_restore_brain_id3.nii.gz
|   └── T1w_acpc_dc_restore_brain_id4.nii.gz
├── result
|   └── 110521
|       ├── T1w_acpc_dc_restore_brain_id3.nii.gz
|       ├── T1w_acpc_dc_restore_brain_id4.nii.gz
├── arrays
|   ├── h_2x_1023.npy
|   └── space_2x_1023.km
├── train.py
├── test.py
├── feature_model.py
├── filter_constant.py
├── kmeans_vector.py
├── preprocessing.py
├── filter_func.py
├── matrix_compute.py
└── util.py

• Run following commmand to start training.

git clone https://github.com/Snailpong/SR_Tensor.git
cd SR_Tensor
python train.py

• Run following commmand to get upscaled images.

python test.py

• https://github.com/Snailpong/MRI_Super_Resolution_3D_Gradient_Filter_Learning.git

GNU General Public License 3.0 License