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Compressed sensing MRI is a classic inverse problem in the field of computational imaging, accelerating the MR imaging by measuring less k-space data.
Automatic compensation of motion artifacts in MRI
Atkinson, D., Hill, D.L., Stoyle, P.N., Summers, P.E., Clare, S., Bowtell, R., Keevil, S.F.: · 1999
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Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: · 2004
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Sparse MRI: The application of compressed sensing for rapid MR imaging
Lustig, M., Donoho, D., Pauly, J.M.: · 2007
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An efficient algorithm for compressed MR imaging using total variation and wavelets
Ma, S., Yin, W., Zhang, Y., Chakraborty, A.: · 2008
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k-t FOCUSS: A general compressed sensing framework for high resolution dynamic MRI
Jung, H., Sung, K., Nayak, K.S., Kim, E.Y., Ye, J.C.: · 2009
Earlier work this paper cites.
A fast alternating direction method for TVL1-L2 signal reconstruction from partial fourier data
Yang, J., Zhang, Y., Yin, W.: · 2010
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MR image reconstruction from highly undersampled k-space data by dictionary learning
Ravishankar, S., Bresler, Y.: · 2011
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Efficient MR image reconstruction for compressed MR imaging
Huang, J., Zhang, S., Metaxas, D.: · 2011
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Undersampled MRI reconstruction with patch-based directional wavelets
Qu, X., Guo, D., Ning, B., Hou, Y., Lin, Y., Cai, S., Chen, Z.: · 2012
Earlier work this paper cites.
Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator
Qu, X., Hou, Y., Lam, F., Guo, D., Zhong, J., Chen, Z.: · 2014
Earlier work this paper cites.
Bayesian nonparametric dictionary learning for compressed sensing MRI
Huang, Y., Paisley, J., Lin, Q., Ding, X., Fu, X., Zhang, X.P.: · 2014
Cited alongside, same era.
Compressive sensing via nonlocal low-rank regularization
Dong, W., Shi, G., Li, X., Ma, Y., Huang, F.: · 2014
Cited alongside, same era.
Visualizing and understanding convolutional networks
Zeiler, M.D., Fergus, R.: · 2014
Cited alongside, same era.
Efficient blind compressed sensing using sparsifying transforms with convergence guarantees and application to magnetic resonance imaging
Ravishankar, S., Bresler, Y.: · 2015
Cited alongside, same era.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., Brox, T.: · 2015
Cited alongside, same era.
Parallel multi-dimensional LSTM, with application to fast biomedical volumetric image segmentation
Combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation
Chen, J., Yang, L., Zhang, Y., Alber, M., Chen, D.Z.: · 2016
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Deep contrast learning for salient object detection
Li, G., Yu, Y.: · 2016
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A deep cascade of convolutional neural networks for MR image reconstruction
Schlemper, J., Caballero, J., Hajnal, J.V., Price, A., Rueckert, D.: · 2017
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Deep residual learning for compressed sensing MRI
Lee, D., Yoo, J., Ye, J.C.: · 2017
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VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images
Chen, H., Dou, Q., Yu, L., Qin, J., Heng, P.A.: · 2017
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Amulet: Aggregating multi-level convolutional features for salient object detection
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Stollenga, M.F., Byeon, W., Liwicki, M., Schmidhuber, J.: · 2015
Cited alongside, same era.
Mrbrains challenge: online evaluation framework for brain image segmentation in 3T MRI scans
Mendrik, A.M., Vincken, K.L., Kuijf, H.J., Breeuwer, M., Bouvy, W.H., De Bresser, J., Alansary, A., De Bruijne, M., Carass, A., El-Baz, A., et al.: · 2015
Cited alongside, same era.
Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform
Lai, Z., Qu, X., Liu, Y., Guo, D., Ye, J., Zhan, Z., Chen, Z.: · 2016
Cited alongside, same era.
Accelerating magnetic resonance imaging via deep learning
Wang, S., Su, Z., Ying, L., Peng, X., Zhu, S., Liang, F., Feng, D., Liang, D.: · 2016
Cited alongside, same era.
3D U-Net: learning dense volumetric segmentation from sparse annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: · 2016
Cited alongside, same era.
Zhang, P., Wang, D., Lu, H., Wang, H., Ruan, X.: · 2017
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Deeply supervised salient object detection with short connections
Hou, Q., Cheng, M.M., Hu, X., Borji, A., Tu, Z., Torr, P.: · 2017
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When image denoising meets high-level vision tasks: A deep learning approach
Liu, D., Wen, B., Liu, X., Huang, T.S.: · 2017
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AOD-Net: All-In-One dehazing network
Li, B., Peng, X., Wang, Z., Xu, J., Feng, D.: · 2017
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Automatic brain tumor detection and segmentation using u-net based fully convolutional networks
Dong, H., Yang, G., et al.: · 2017
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