An Interpretable MRI Reconstruction Network with Two-grid-cycle Correction and Geometric Prior Distillation
This repository contains the CS-MRI reconstruction pytorch codes for the following paper：
X. Fan, Y. Yang, K. Chen, J. Zhang, and K. Dong, “An interpretable MRI reconstruction network with two-grid-cycle correction and geometric prior distillation,” Biomedical Signal Processing and Control, vol. 84, p. 104821, Jul. 2023, doi: 10.1016/j.bspc.2023.104821.[pdf]
X. Fan, Y. Yang, K. Chen, J. Zhang, and K. Dong, “An interpretable MRI reconstruction network with two-grid-cycle correction and geometric prior distillation,” arXiv, May 2022. [pdf]
Although existing deep learning compressed-sensing-based Magnetic Resonance Imaging (CS-MRI) methods have achieved considerably impressive performance, explainability and generalizability continue to be challenging for such methods since the transition from mathematical analysis to network design not always natural enough, often most of them are not flexible enough to handle multi-sampling-ratio reconstruction assignments. {In this work, to tackle explainability and generalizability, we propose a unifying deep unfolding multi-sampling-ratio interpretable CS-MRI framework.} The combined approach offers more generalizability than previous works whereas deep learning gains explainability through a geometric prior module. Inspired by the multigrid algorithm, we first embed the CS-MRI-based optimization algorithm into correction-distillation scheme that consists of three ingredients: pre-relaxation module, correction module and geometric prior distillation module. Furthermore, we employ a condition module to learn adaptively step-length and noise level, which enables the proposed framework to jointly train multi-ratio tasks through a single model. { The proposed model not only compensates for the lost contextual information of reconstructed image which is refined from low frequency error in geometric characteristic k-space}, but also integrates the theoretical guarantee of model-based methods and the superior reconstruction performances of deep learning-based methods. Therefore, it can give us a novel perspective to design biomedical imaging networks. { Numerical experiments show that our framework outperforms state-of-the-art methods in terms of qualitative and quantitative evaluations.} {Our method achieves 3.18 dB improvement at low CS ratio 10% and average 1.42 dB improvement over other comparison methods on brain dataset using Cartesian sampling mask.
Fig. 1. The overall architecture of the proposed unifying multi-sampling-ratio CS-MRI framework with two-grid-cycle correction and geometric prior distillation (CGPD-CSNet).
These codes are built on PyTorch and tested on Ubuntu 18.04/20.04 (Python3.x, PyTorch>=0.4) with Intel Xeon CPU E5-2630 and Nvidia Tesla V100 GPU.
pytorch <= 1.7.1 (recommend 1.6.0, 1.7.1)
scikit-image <= 0.16.2 (recommend 0.16.1, 0.16.2)
1.1、Pre-trained models:
All pre-trained models for our paper are in './model_MRI'.
1.2、Prepare test data:
The original test sets (BrainImages_test and heart_test) are in './data/'.
1.3、Prepare code:
Open './Core_brainMRI-CGPD-CSNet-Cartesian-together.py' and change the default run_mode to test in parser (parser.add_argument('--run_mode', type=str, default='test', help='train、test')).
1.4、Run the test script (Core_brainMRI-CGPD-CSNet-Cartesian-together.py).
1.5、Check the results in './result/'.
2.1、Prepare training data:
We use the same datasets and training data pairs as ISTA-Net+ and CDDN for CS-MRI. Due to upload file size limitation, we are unable to upload training data directly. Here we provide a link to download the datasets for you.
2.2、Prepare measurement matrix:
The measurement matrixs (pseudo-radial/2D-random/Cartesian) are in './sampling_matrix/'.
2.3、Prepare code:
Open './Core_brainMRI-CGPD-CSNet-Cartesian-together.py' and change the default run_mode to train in parser (parser.add_argument('--run_mode', type=str, default='train', help='train、test')).
2.4、Run the train script (Core_brainMRI-CGPD-CSNet-Cartesian-together.py).
2.5、Check the results in './log_MRI/'.
If you find the code helpful in your resarch or work, please cite the following papers.
@Article{Fan2023,
author = {Xiaohong Fan and Yin Yang and Ke Chen and Jianping Zhang and Ke Dong},
journal = {Biomedical Signal Processing and Control},
title = {An interpretable {MRI} reconstruction network with two-grid-cycle correction and geometric prior distillation},
year = {2023},
month = {Jul.},
pages = {104821},
volume = {84},
doi = {10.1016/j.bspc.2023.104821},
}
Thanks to the authors of ISTA-Net+ and FISTA-Net, our codes are adapted from the open source codes of ISTA-Net+ and FISTA-Net.
The code is provided to support reproducible research. If the code is giving syntax error in your particular python configuration or some files are missing then you may open an issue or directly email me at fanxiaohong1992@gmail.com or fanxiaohong@smail.xtu.edu.cn