This repository is the PyTorch implementation of our manuscript "An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation". [ArXiv, IEEE Xplore]

Figure 1: Overview of the ArSSR model.

The MR images shown in Figure 2 can be downloaded in LR image, 2x SR result, 3.2x SR result, 4x SR result.

Figure 2: An example of the SISR tasks of three different isotropic up-sampling scales k={2, 3.2, 4} for a 3D brain MR image by the single ArSSR model.

• python 3.7.9
• pytorch-gpu 1.8.1
• tensorboard 2.6.0
• SimpleITK, tqdm, numpy, scipy, skimage

In the pre_trained_models folder, we provide the three pre-trained ArSSR models (with three difference encoder networks) on HCP-1200 dataset. You can improve the resolution of your images thourgh the following commands:

python test.py -input_path [input_path] \
               -output_path [output_path] \
               -encoder [RDN, ResCNN, or SRResNet] \
               -pre_trained_model [pre_trained_model]
               -scale [scale] \
               -is_gpu [is_gpu] \
               -gpu [gpu]

where,

• input_path is the path of LR input image, it should be not contain the input finename.

• output_path is the path of outputs, it should be not contain the output finename.

• encoder_name is the type of the encoder network, including RDN, ResCNN, or SRResNet.

• pre_trained_model is the full-path of pre-trained ArSSR model (e.g, for ArSSR model with RDB encoder network: ./pre_trained_models/ArSSR_RDN.pkl).

• !!! Note that here encoder_name and pre_trained_model have to be matched. E.g., if you use the ArSSR model with ResCNN encoder network, encoder_name should be ResCNN and pre_trained_model should be ./pre_trained_models/ArSSR_ResCNN.pkl

• scale is up-sampling scale k, it can be int or float.

• is_gpu is the identification of whether to use GPU (0->CPU, 1->GPU).

• gpu is the number of GPU.

In our experiment, we train the ArSSR model on the HCP-1200 Dataset. In particular, the HCP-1200 dataset is split into three parts: 780 training examples, 111 validation examples, and 222 testing examples. More details about the HCP-1200 can be found in our manuscript [ArXiv]. And you can download the pre-processed training set and validation set [Google Drive].

By using the pre-processed training set and validation set by ourselves from [Google Drive], the pipeline of training the ArSSR model can be divided into three steps:

1. unzip the downloaded file data.zip.
2. put the data in the ArSSR directory.
3. run the following command.

python train.py -encoder_name [encoder_name] \
                -decoder_depth [decoder_depth]	\
                -decoder_width [decoder_width] \
                -feature_dim [feature_dim] \
                -hr_data_train [hr_data_train] \
                -hr_data_val [hr_data_val] \
                -lr [lr] \
                -lr_decay_epoch [lr_decay_epoch] \
                -epoch [epoch] \
                -summary_epoch [summary_epoch] \
                -bs [bs] \
                -ss [ss] \
                -gpu [gpu]

where,

• encoder_name is the type of the encoder network, including RDN, ResCNN, or SRResNet.
• decoder_depth is the depth of the decoder network (default=8).
• decoder_width is the width of the decoder network (default=256).
• feature_dim is the dimension size of the feature vector (default=128)
• hr_data_train is the file path of HR patches for training (if you use our pre-processed data, this item can be ignored).
• hr_data_val is the file path of HR patches for validation (if you use our pre-processed data, this item can be ignored).
• lr is the initial learning rate (default=1e-4).
• lr_decay_epoch is learning rate multiply by 0.5 per some epochs (default=200).
• epoch is the total number of epochs for training (default=2500).
• summary_epoch is the current model that will be saved per some epochs (default=200).
• bs is the number of LR-HR patch pairs, i.e., N in Equ. 3 (default=15).
• ss is the number of sampled voxel coordinates, i.e., K in Equ. 3 (default=8000).
• gpu is the number of GPU.

If you find our work useful in your research, please cite:

@ARTICLE{9954892,
  author={Wu, Qing and Li, Yuwei and Sun, Yawen and Zhou, Yan and Wei, Hongjiang and Yu, Jingyi and Zhang, Yuyao},
  journal={IEEE Journal of Biomedical and Health Informatics}, 
  title={An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation}, 
  year={2023},
  volume={27},
  number={2},
  pages={1004-1015},
  doi={10.1109/JBHI.2022.3223106}}