3d-SMRnet: MPI system matrix recovery

This repository contains the official code and model weights for the following MICCAI 2020 paper:

@inproceedings{baltruschat20203d,
  title={3d-SMRnet: Achieving a new quality of MPI system matrix recovery by deep learning},
  author={Baltruschat, Ivo M and Szwargulski, Patryk and Griese, Florian and Grosser, Mirco and Werner, Rene and Knopp, Tobias},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={74--82},
  year={2020},
  organization={Springer}
}

Requirements

The code has been tested with Python 3.6 on Ubuntu 16.04 LTS and Windows 10. Use the following command to install all required Python packages:

pip install -r requirements.txt

Usage

We provide the system matrices: Perimag and Synomag-D
Both matrices are already processed and contain the RGB-encoded key="Data" and the complex key="DataImag"; key="DataReal" data. We use a threshold of SNR=3 for the frequencies. For downloading our trained model weights, please see here.

Prepare data

We only provide raw system matrices. Hence, we need to prepare the data first. The script "pre_processing.py" will create several HDF5-files with training, validation, and testing splits. Furthermore, LR system matrices with equidistant subsampling of the HR system matrix are created.

1. Modify the script file scripts/pre_processing.py
   Update all path specific parameters
2. Run command: python pre_processing.py

How to Test

Test SR-RRDB model with 2 channels (Image/Real) and a up-scaling of 4

1. Modify the configuration file experiments/001_Test_SR-RRDB-3d_complex_scale4.json
   Update all path specific parameters
2. Run command: python test.py -opt experiments/001_Test_SR-RRDB-3d_complex_scale4.json

Test SR-RRDB model with 3 channels (RGB) and a up-scaling of 4

1. Modify the configuration file experiments/002_Test_SR-RRDB-3d_RGB_scale4.json
   Update all path specific parameters
2. Run command: python test.py -opt experiments/002_Test_SR-RRDB-3d_RGB_scale4.json

How to Train

Train SR-RRDB model with 2 channels (Image/Real) and a up-scaling of 4

1. Modify the configuration file experiments/001_Test_SR-RRDB-3d_complex_scale4.json
2. Run the command: python train.py -opt experiments/001_Test_SR-RRDB-3d_complex_scale4.json

Train SR-RRDB model with 3 channels (RGB) and a up-scaling of 4

1. Modify the configuration file experiments/002_Test_SR-RRDB-3d_RGB_scale4.json
2. Run the command: python train.py -opt experiments/002_Test_SR-RRDB-3d_RGB_scale4.json

Our trained models

Results

Acknowledgement

• Code architecture is an extension of BasicSR and LapSRN