Accurate multi-contrast MRI super-resolution via a dual cross-attention transformer network (DCAMSR)
- numpy==1.18.5
- scikit_image==0.16.2
- torchvision==0.8.1
- torch==1.7.0
- runstats==1.8.0
- pytorch_lightning==0.9.0
- h5py==2.10.0
- PyYAML==5.4
- timm
- einops
- python-opencv
The data used for the image super-resolution task comes from the fastMRI dataset and M4Raw.
The multi-contrast MR images csv file is released in dataset fold. fastMRI csv file comes from MINet.
Within each task folder, the following structure is expected:
data0/fastmri_knee
├── singlecoil_train
│ ├── xxx.h5
│ ├── ...
├── singlecoil_val
│ ├── xxx.h5
│ ├── ...
data0/M4RawV1.1
├── multicoil_train
│ ├── xxx.h5
│ ├── ...
├── multicoil_val
│ ├── xxx.h5
│ ├── ...
pip install -r requirement.txt
cd experimental/DCAMSR
python train.py
cd experimental/DCAMSR
python test.py --mode test --resume xxx
We borrow some codes from MASA and MINet. We thank the authors for their great work.