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- W2884293458 abstract "Compressed sensing magnetic resonance imaging(CS-MRI) achieves shorter acquisition time by reducing the sampling rate of k-space data. This not only cuts down the scanning cost, but also reduces the artifacts and alising caused by the patient's movement and improves the imaging quality. However, the iterative reconstruction of MRI based on compressed sensing needs very expensive computation. Recently, some works about CS-MRI reconstruction based on deep learning have achieved great success. In this paper, we proposed a novel network named ResGAN to improve imaging quality while significantly reducing MRI scan time. The proposed network, ResGAN, is a deep generative adversarial network, which learns a mapping from the under-sampled MRI data to fully sampled data by the generative network. In particular, we adopt the training strategy of residual learning to achieve lower convergence loss as well as avoiding over fitting. Moreover, despite of the pixel-wise L1-loss, the feed-forward generative network also utilizes frequency domain loss to maintain data consistency. Our method obtained promising experimental results on both imaging quality and reconstruction speed for 1.5T brain MRI while only 30 percent of the raw k-space data are sampled using radial sampling pattern." @default.
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- W2884293458 date "2018-07-24" @default.
- W2884293458 modified "2023-09-26" @default.
- W2884293458 title "Compressed Sensing MRI Reconstruction Based on Generative Adversarial Nets" @default.
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- W2884293458 doi "https://doi.org/10.12783/dtcse/csse2018/24492" @default.
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