A k-space-to-image reconstruction network for MRI using recurrent neural network
- Authors
- Oh, Changheun; Kim, Dongchan; Chung, Jun-Young; Han, Yeji; Park, HyunWook
- Issue Date
- Jan-2021
- Publisher
- WILEY
- Keywords
- deep learning; end-to-end reconstruction network (ETER-net); MR image reconstruction; parallel imaging; recurrent neural network
- Citation
- MEDICAL PHYSICS, v.48, no.1, pp.193 - 203
- Journal Title
- MEDICAL PHYSICS
- Volume
- 48
- Number
- 1
- Start Page
- 193
- End Page
- 203
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/80011
- DOI
- 10.1002/mp.14566
- ISSN
- 0094-2405
- Abstract
- 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. © 2020 American Association of Physicists in Medicine
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