A k-space-to-image reconstruction network for MRI using recurrent neural network.

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Abstract

Reconstructing the images from undersampled k-space data is an ill-posed inverse problem. As a solution to this problem, we propose a method to reconstruct MR images directly from k-space data using a recurrent neural network.
A novel neural network architecture named “ETER-net” is developed as a unified solution to reconstruct MR images from undersampled k-space data, where two bi-RNNs and CNN are utilized to perform domain transformation and de-aliasing. To demonstrate the practicality of the proposed method, we conducted model optimization, cross-validation, and network pruning using in-house data from a 3T MRI scanner and public dataset called “FastMRI”.
The experimental results showed that the proposed method could be utilized for accurate image reconstruction from undersampled k-space data. The size of the proposed model was optimized and cross-validation was performed to show the robustness of the proposed method. For in-house dataset (R=4), the proposed method provided nMSE = 1.09% and SSIM = 0.938. For “FastMRI” dataset, the proposed method provided nMSE = 1.05 % and SSIM = 0.931 for R=4, and nMSE = 3.12 % and SSIM = 0.884 for R=8. The performance of the pruned model trained the loss function including with L2 regularization was consistent for a pruning ratio of upto 70%.
The proposed method is an end-to-end MR image reconstruction method based on recurrent neural networks. It performs direct mapping of the input k-space data and the reconstructed images, operating as a unified solution that is applicable to various scanning trajectories.
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