Fetching the paper…
Reading the bibliography…
Deep MRI reconstruction is commonly performed with conditional models that de-alias undersampled acquisitions to recover images consistent with fully-sampled data.
Time-dependent deep image prior for dynamic MRI
Jin, K.H., Gupta, H., Yerly, J., Stuber, M., Unser, M., 2019 · 1910
Earlier work this paper cites.
A few-shot learning approach for accelerated MRI via fusion of data-driven and subject-driven priors, in: Proceedings of ISMRM, p. 1949
Dar, S.U., Yurt, M., Çukur, T., 2021 · 1949
Earlier work this paper cites.
Deep residual learning for accelerated MRI using magnitude and phase networks
Lee, D., Yoo, J., Tak, S., Ye, J.C., 2018 · 1995
Earlier work this paper cites.
Sparse MRI: The application of compressed sensing for rapid MR imaging
Lustig, M., Donoho, D., Pauly, J.M., 2007 · 2007
Earlier work this paper cites.
Unsupervised MRI reconstruction with generative adversarial networks
Cole, E.K., Pauly, J.M., Vasanawala, S.S., Ong, F., 2020 · 2008
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B., 2020 · 2011
Earlier work this paper cites.
Coil compression for accelerated imaging with Cartesian sampling
Zhang, T., Pauly, J.M., Vasanawala, S.S., Lustig, M., 2013 · 2013
Earlier work this paper cites.
ESPIRiT-an eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA
Uecker, M., Lai, P., Murphy, M.J., Virtue, P., Elad, M., Pauly, J.M., Vasanawala, S.S., Lustig, M., 2014 · 2014
Earlier work this paper cites.
P-LORAKS: Low-Rank Modeling of Local k-Space Neighborhoods with Parallel Imaging Data
Haldar, J.P., Zhuo, J., 2016 · 2016
Earlier work this paper cites.
Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
He, K., Zhang, X., Ren, S., Sun, J., 2016 · 2016
Earlier work this paper cites.
Inertial Proximal Alternating Linearized Minimization (iPALM) for Nonconvex and Nonsmooth Problems
Pock, T., Sabach, S., 2016 · 2016
Earlier work this paper cites.
Accelerating magnetic resonance imaging via deep learning, in: IEEE ISBI, pp. 514–517
Wang, S., Su, Z., Ying, L., Peng, X., Zhu, S., Liang, F., Feng, D., Liang, D., 2016 · 2016
Earlier work this paper cites.
Deep ADMM-Net for compressive sensing MRI, in: Advances in Neural Information Processing Systems
Yang, Y., Sun, J., Li, H., Xu, Z., 2016 · 2016
Earlier work this paper cites.
Learning a variational network for reconstruction of accelerated MRI data
Hammernik, K., Klatzer, T., Kobler, E., Recht, M.P., Sodickson, D.K., Pock, T., Knoll, F., 2017 · 2017
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium, in: Advances in Neural Information Processing Systems
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S., 2017 · 2017
Earlier work this paper cites.
A parallel MR imaging method using multilayer perceptron
Kwon, K., Kim, D., Park, H., 2017 · 2017
Earlier work this paper cites.
A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction, in: Proceedings of IPMI, pp. 647–658
Schlemper, J., Caballero, J., Hajnal, J.V., Price, A., Rueckert, D., 2017 · 2017
Earlier work this paper cites.
Learned primal-dual reconstruction
Adler, J., Oktem, O., 2018 · 2018
Earlier work this paper cites.
Complex fully convolutional neural networks for mr image reconstruction, in: Machine Learning for Medical Image Reconstruction, pp. 30–38
Dedmari, M.A., Conjeti, S., Estrada, S., Ehses, P., Stöcker, T., Reuter, M., 2018 · 2018
Earlier work this paper cites.
KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images
Eo, T., Jun, Y., Kim, T., Jang, J., Lee, H.J., Hwang, D., 2018 · 2018
Earlier work this paper cites.
Deep learning with domain adaptation for accelerated projection-reconstruction MR
Han, Y., Yoo, J., Kim, H.H., Shin, H.J., Sung, K., Ye, J.C., 2018 · 2018
Earlier work this paper cites.
Deep learning for undersampled MRI reconstruction
Hyun, C.M., Kim, H.P., Lee, S.M., Lee, S., Seo, J.K., 2018 · 2018
Earlier work this paper cites.
Which training methods for GANs do actually converge?, in: Proceedings of ICML, pp. 3481–3490
Mescheder, L., Geiger, A., Nowozin, S., 2018 · 2018
Earlier work this paper cites.
Compressed sensing MRI reconstruction with cyclic loss in generative adversarial networks
Quan, T.M., Nguyen-Duc, T., Jeong, W.K., 2018 · 2018
Earlier work this paper cites.
Deep image prior, in: IEEE CVPR, pp. 9446–9454
Ulyanov, D., Vedaldi, A., Lempitsky, V., 2018 · 2018
Earlier work this paper cites.
Deep convolutional framelets: A general deep learning framework for inverse problems
Ye, J.C., Han, Y., Cha, E., 2018 · 2018
Earlier work this paper cites.
Quantitative susceptibility mapping using deep neural network: QSMnet
Yoon, J., Gong, E., Chatnuntawech, I., Bilgic, B., Lee, J., Jung, W., Ko, J., Jung, H., Setsompop, K., Zaharchuk, G., Kim, E.Y., Pauly, J., Lee, J., 2018 · 2018
Earlier work this paper cites.
DAGAN: Deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction
Yu, S., Dong, H., Yang, G., Slabaugh, G., Dragotti, P.L., Ye, X., Liu, F., Arridge, S., Keegan, J., Firmin, D., Guo, Y., 2018 · 2018
Earlier work this paper cites.
The unreasonable effectiveness of deep features as a perceptual metric, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O., 2018 · 2018
Earlier work this paper cites.
Image reconstruction by domain transform manifold learning
Zhu, B., Liu, J.Z., Rosen, B.R., Rosen, M.S., 2018 · 2018
Earlier work this paper cites.
MoDL: Model-Based deep learning architecture for inverse problems
Aggarwal, H.K., Mani, M.P., Jacob, M., 2019 · 2019
Earlier work this paper cites.
Dynamic MRI using model-based deep learning and SToRM priors: MoDL-SToRM
Biswas, S., Aggarwal, H.K., Jacob, M., 2019 · 2019
Cited alongside, same era.
Model learning: Primal dual networks for fast MR imaging, in: Proceedings of MICCAI, pp. 21–29
Cheng, J., Wang, H., Ying, L., Liang, D., 2019 · 2019
Cited alongside, same era.
Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks
Dar, S.U.H., Yurt, M., Karacan, L., Erdem, A., Erdem, E., Çukur, T., 2019 · 2019
Cited alongside, same era.
Deep MRI reconstruction without ground truth for training, in: Proceedings of ISMRM, p. 4668
Huang, P., Li, C.H., Gaire, S.K., Liu, R., Zhang, X., Li, X., Ying, L., 2019 · 2019
Cited alongside, same era.
A style-based generator architecture for generative adversarial networks, in: IEEE CVPR, pp. 4401–4410
Karras, T., Laine, S., Aila, T., 2019 · 2019
Cited alongside, same era.
Model adaptation for image reconstruction using generalized Stein’s unbiased risk estimator
Aggarwal, H.K., Jacob, M., 2021 · 2021
Later among the works it cites.
Ensure: Ensemble stein’s unbiased risk estimator for unsupervised learning, in: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1160–1164
Aggarwal, H.K., Pramanik, A., Jacob, M., 2021 · 2021
Later among the works it cites.
Two-stage deep learning for accelerated 3D time-of-flight MRA without matched training data
Chung, H., Cha, E., Sunwoo, L., Ye, J.C., 2021 · 2021
Later among the works it cites.
Analysis of deep complex-valued convolutional neural networks for mri reconstruction and phase-focused applications
Cole, E., Cheng, J., Pauly, J., Vasanawala, S., 2021 · 2021
Later among the works it cites.
Accelerated mri with un-trained neural networks
Darestani, M.Z., Heckel, R., 2021 · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Assessment of the generalization of learned image reconstruction and the potential for transfer learning
Knoll, F., Hammernik, K., Kobler, E., Pock, T., Recht, M.P., Sodickson, D.K., 2019 · 2019
Cited alongside, same era.
Deep generative adversarial neural networks for compressive sensing MRI
Mardani, M., Gong, E., Cheng, J.Y., Vasanawala, S., Zaharchuk, G., Xing, L., Pauly, J.M., 2019 · 2019
Cited alongside, same era.
Inverse GANs for accelerated MRI reconstruction, in: Proceedings of SPIE, pp. 381 – 392
Narnhofer, D., Hammernik, K., Knoll, F., Pock, T., 2019 · 2019
Cited alongside, same era.
Convolutional recurrent neural networks for dynamic MR image reconstruction
Qin, C., Schlemper, J., Caballero, J., Price, A.N., Hajnal, J.V., Rueckert, D., 2019 · 2019
Cited alongside, same era.
Unsupervised deep basis pursuit: Learning reconstruction without ground-truth data, in: Proceedings of ISMRM, p. 0660
Tamir, J.I., Yu, S.X., Lustig, M., 2019 · 2019
Cited alongside, same era.
MR image reconstruction using deep density priors
Tezcan, K.C., Baumgartner, C.F., Luechinger, R., Pruessmann, K.P., Konukoglu, E., 2019 · 2019
Cited alongside, same era.
DIMENSION: Dynamic MR imaging with both k-space and spatial prior knowledge obtained via multi-supervised network training
Wang, S., Ke, Z., Cheng, H., Jia, S., Ying, L., Zheng, H., Liang, D., 2019 · 2019
Cited alongside, same era.
Diffusion models beat gans on image synthesis, in: Advances in Neural Information Processing Systems, Curran Associates, Inc.. pp. 8780–8794
Dhariwal, P., Nichol, A., 2021 · 2021
Later among the works it cites.
Donet: Dual-octave network for fast mr image reconstruction
Feng, C.M., Yang, Z., Fu, H., Xu, Y., Yang, J., Shao, L., 2021 · 2021
Later among the works it cites.
Compressed sensing mri with ℓ \ell 1-wavelet reconstruction revisited using modern data science tools, in: International Conference of the IEEE Engineering in Medicine & Biology Society, pp. 3596–3600
Gu, H., Yaman, B., Ugurbil, K., Moeller, S., Akçakaya, M., 2021 · 2021
Later among the works it cites.
Over-and-under complete convolutional rnn for mri reconstruction, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 13–23
Guo, P., Valanarasu, J.M.J., Wang, P., Zhou, J., Jiang, S., Patel, V.M., 2021 · 2021
Later among the works it cites.
Robust compressed sensing mri with deep generative priors, in: Advances in Neural Information Processing Systems, pp. 14938–14954
Jalal, A., Arvinte, M., Daras, G., Price, E., Dimakis, A.G., Tamir, J., 2021 · 2021
Later among the works it cites.
Wasserstein gans for mr imaging: From paired to unpaired training
Lei, K., Mardani, M., Pauly, J.M., Vasanawala, S.S., 2021 · 2021
Later among the works it cites.
Universal undersampled mri reconstruction, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 211–221
Liu, X., Wang, J., Liu, F., Zhou, S.K., 2021 · 2021
Later among the works it cites.
Two-stage self-supervised cycle-consistency network for reconstruction of thin-slice mr images, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 3–12
Lu, Z., Li, Z., Wang, J., Shi, J., Shen, D., 2021 · 2021
Later among the works it cites.
Zero-shot physics-guided deep learning for subject-specific mri reconstruction, in: NeurIPS 2021 Workshop on Deep Learning and Inverse Problems
Yaman, B., Hosseini, S.A.H., Akcakaya, M., 2021 · 2021
Later among the works it cites.
Deep generative SToRM model for dynamic imaging
Zou, Q., Ahmed, A.H., Nagpal, P., Kruger, S., Jacob, M., 2021 · 2021
Later among the works it cites.
Come-closer-diffuse-faster: Accelerating conditional diffusion models for inverse problems through stochastic contraction, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12413–12422
Chung, H., Sim, B., Ye, J.C., 2022 · 2022
Closest in time.
Score-based diffusion models for accelerated mri
Chung, H., Ye, J.C., 2022 · 2022
Closest in time.
Federated learning of generative image priors for mri reconstruction
Elmas, G., Dar, S.U., Korkmaz, Y., Ceyani, E., Susam, B., Ozbey, M., Avestimehr, S., Çukur, T., 2022 · 2022
Closest in time.
TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle Imaging
Güngör, A., Askin, B., Soydan, D.A., Saritas, E.U., Top, C.B., Çukur, T., 2022 · 2022
Closest in time.
Reconformer: Accelerated mri reconstruction using recurrent transformer
Guo, P., Mei, Y., Zhou, J., Jiang, S., Patel, V.M., 2022 · 2022
Closest in time.
Unsupervised mri reconstruction via zero-shot learned adversarial transformers
Korkmaz, Y., Dar, S.U.H., Yurt, M., Ozbey, M., Çukur, T., 2022 · 2022
Closest in time.
Undersampled mri reconstruction with side information-guided normalisation
Liu, X., Wang, J., Peng, C., Chandra, S.S., Liu, F., Zhou, S.K., 2022 · 2022
Closest in time.
Mri reconstruction via data driven markov chain with joint uncertainty estimation
Luo, G., Heide, M., Uecker, M., 2022 · 2022
Closest in time.
Unsupervised medical image translation with adversarial diffusion models
Ozbey, M., Dar, S.U., Bedel, H.A., Dalmaz, O., Ozturk, S., Güngör, A., Çukur, T., 2022 · 2022
Closest in time.
Towards performant and reliable undersampled mr reconstruction via diffusion model sampling
Peng, C., Guo, P., Zhou, S.K., Patel, V., Chellappa, R., 2022 · 2022
Closest in time.
Solving inverse problems in medical imaging with score-based generative models, in: International Conference on Learning Representations
Song, Y., Shen, L., Xing, L., Ermon, S., 2022 · 2022
Closest in time.
Sampling possible reconstructions of undersampled acquisitions in mr imaging with a deep learned prior
Tezcan, K.C., Karani, N., Baumgartner, C.F., Konukoglu, E., 2022 · 2022
Closest in time.
Tackling the generative learning trilemma with denoising diffusion GANs, in: International Conference on Learning Representations (ICLR)
Xiao, Z., Kreis, K., Vahdat, A., 2022 · 2022
Closest in time.
Xie, Y., Li, Q., 2022 · 2022
Closest in time.
Multi-modal mri reconstruction assisted with spatial alignment network
Xuan, K., Xiang, L., Huang, X., Zhang, L., Liao, S., Shen, D., Wang, Q., 2022 · 2022
Closest in time.