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arXiv · 1909.10856

IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRI

Yiling Liu·Qiegen Liu·Minghui Zhang·Qingxin Yang·Shanshan Wang·Dong Liang
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Abstract

To improve the compressive sensing MRI (CS-MRI) approaches in terms of fine structure loss under high acceleration factors, we have proposed an iterative feature refinement model (IFR-CS), equipped with fixed transforms, to restore the meaningful structures and details. Nevertheless, the proposed IFR-CS still has some limitations, such as the selection of hyper-parameters, a lengthy reconstruction time, and the fixed sparsifying transform. To alleviate these issues, we unroll the iterative feature refinement procedures in IFR-CS to a supervised model-driven network, dubbed IFR-Net. Equipped with training data pairs, both regularization parameter and the utmost feature refinement operator in IFR-CS become trainable. Additionally, inspired by the powerful representation capability of convolutional neural network (CNN), CNN-based inversion blocks are explored in the sparsity-promoting denoising module to generalize the sparsity-enforcing operator. Extensive experiments on both simulated and in vivo MR datasets have shown that the proposed network possesses a strong capability to capture image details and preserve well the structural information with fast reconstruction speed.

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Yiling Liu, Qiegen Liu, Minghui Zhang, Qingxin Yang, Shanshan Wang, Dong Liang. 2019-09-24. IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRI. https://arxiv.org/abs/1909.10856

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