Computer Science > Machine Learning
[Submitted on 24 Sep 2019 (v1), last revised 25 Sep 2019 (this version, v2)]
Title:IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRI
View PDFAbstract: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.
Submission history
From: Qiegen Liu [view email][v1] Tue, 24 Sep 2019 12:57:18 UTC (1,456 KB)
[v2] Wed, 25 Sep 2019 02:18:02 UTC (1,427 KB)
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