Purpose
Reconstructing the images from undersampled k‐space data are an ill‐posed inverse problem. As a solution to this problem, we propose a method to reconstruct magnetic resonance (MR) images directly from k‐space data using a recurrent neural network.
Methods
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 convolutional neural network (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.”
Results
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 up to 70%.
Conclusions
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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