This is a testing and training code for ConvDe-AliasingNet：Model-based Convolutional De-Aliasing Network Learning for Parallel MR Imaging (MICCAI 2019)

Our network refers to yangyan's admm-net. If you use this code, please cite these papers:

[1] Chen Y., Xiao T., Li C., Liu Q., Wang S. (2019) Model-Based Convolutional De-Aliasing Network Learning for Parallel MR Imaging. In: Shen D. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science, vol 11766. Springer, Cham

[2] Yan Yang, Jian Sun, Huibin Li, Zongben Xu. Deep ADMM-Net for Compressive Sensing MRI, NIPS(2016).

Parallel imaging has been an essential technique to accelerate MR imaging. Nevertheless, the acceleration rate is still limited due to the ill-condition and challenges associated with the undersampled reconstruction. In this paper, we propose a model-based convolutional dealiasing network with adaptive parameter learning to achieve accurate reconstruction from multi-coil undersampled k-space data. Three main contributions have been made: a de-aliasing reconstruction model was proposed to accelerate parallel MR imaging with deep learning exploring both spatial redundancy and multi-coil correlations; a split Bregman iteration algorithm was developed to solve the model efficiently; and unlike most existing parallel imaging methods which rely on the accuracy of the estimated multi-coil sensitivity, the proposed method can perform parallel reconstruction from undersampled data without explicit sensitivity calculation. Evaluations were conducted on in vivo brain dataset with a variety of undersampling patterns and different acceleration factors. Our results demonstrated that this method could achieve superior performance in both quantitative and qualitative analysis, compared to three state-of-the-art methods.

An illustration of the filter operator with convolutional neural networks for both spatial and multi-coil correlations.

The proposed convolutional de-aliasing network architecture for pMRI reconstruction. (a) is the flow chart. The orange arrow indicates the process of reconstructing the undersampled k-space data by forward propagation, and the green arrow indicates the parameter updating through back propagation. (b) and (c) are the detailed configurations of Conv1 and Conv2.

Comparison of different methods in reconstruction accuracy with different undersampling patterns and acceleration factors: reconstruction results and error maps are presented with corresponding quantitative measurements in PSNR/SSIM.

MATLAB R2018a
MatConvNet-1.0-beta25