Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P (1999) SENSE: sensitivity encoding for fast MRI. Magn Reson Med 42:952–962
Article CAS PubMed Google Scholar
Griswold MA, Jakob PM, Heidemann RM, Nittka M, Jellus V, Wang J, Kiefer B, Haase A (2002) Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med 47:1202–1210
Lustig M, Donoho D, Pauly JM (2007) Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 58:1182–1195
Zhu B, Liu JZ, Cauley SF, Rosen BR, Rosen MS (2018) Image reconstruction by domain-transform manifold learning. Nature 555:487–492
Article CAS PubMed Google Scholar
Aggarwal HK, Mani MP, Jacob M (2019) MoDL: model-based deep learning architecture for inverse problems. IEEE Trans Med Imaging 38:394–405
Knoll F, Hammernik K, Kobler E, Pock T, Recht MP, Sodickson DK (2019) Assessment of the generalization of learned image reconstruction and the potential for transfer learning. Magn Reson Med 81:116–128
Knoll F, Murrell T, Sriram A, Yakubova N, Zbontar J, Rabbat M, Defazio A, Muckley MJ, Sodickson DK, Zitnick CL, Recht MP (2020) Advancing machine learning for MR image reconstruction with an open competition: overview of the 2019 fastMRI challenge. Magn Reson Med 84:3054–3070
Article PubMed PubMed Central Google Scholar
Sandino CM, Cheng JY, Chen F, Mardani M, Pauly JM, Vasanawala SS (2020) Compressed sensing: from research to clinical practice with deep neural networks: shortening scan times for magnetic resonance imaging. IEEE Signal Process Mag 37:117–127
Muckley MJ, Riemenschneider B, Radmanesh A, Kim S, Jeong G, Ko J, Jun Y, Shin H, Hwang D, Mostapha M, Arberet S, Nickel D, Ramzi Z, Ciuciu P, Starck J-L, Teuwen J, Karkalousos D, Zhang C, Sriram A, Huang Z, Yakubova N, Lui YW, Knoll F (2021) Results of the 2020 fastMRI challenge for machine learning MR image reconstruction. IEEE Trans Med Imaging 40:2306–2317
Article PubMed PubMed Central Google Scholar
Ueda T, Ohno Y, Yamamoto K, Murayama K, Ikedo M, Yui M, Hanamatsu S, Tanaka Y, Obama Y, Ikeda H, Toyama H (2022) Deep learning reconstruction of diffusion-weighted MRI improves image quality for prostatic imaging. Radiology 303:373–381
Johnson PM, Lin DJ, Zbontar J, Zitnick CL, Sriram A, Muckley M, Babb JS, Kline M, Ciavarra G, Alaia E, Samim M, Walter WR, Calderon L, Pock T, Sodickson DK, Recht MP, Knoll F (2023) Deep learning reconstruction enables prospectively accelerated clinical knee MRI. Radiology 307:e220425
Lin DJ, Walter SS, Fritz J (2023) Artificial intelligence-driven ultra-fast superresolution MRI : 10-fold accelerated musculoskeletal turbo spin echo MRI within reach. Invest Radiol 58:28–42
Zbontar J, Knoll F, Sriram A, Murrell T, Huang Z, Muckley MJ, Defazio A, Stern R, Johnson P, Bruno M, Parente M, Geras KJ, Katsnelson J, Chandarana H, Zhang Z, Drozdzal M, Romero A, Rabbat M, Vincent P, Yakubova N, Pinkerton J, Wang D, Owens E, Zitnick CL, Recht MP, Sodickson DK, Lui YW (2019) Fastmri: An Open Dataset and Benchmarks for Accelerated MRI. https://doi.org/10.48550/arXiv.1811.08839
Johnson PM, Muckley MJ, Bruno M, Kobler E, Hammernik K, Pock T, Knoll F (2019) Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions. In: Knoll F, Maier A, Rueckert D, Ye JC (eds) Mach. Springer International Publishing, Cham, Learn. Med. Image Reconstr, pp 71–79
Hammernik K, Schlemper J, Qin C, Duan J, Summers RM, Rueckert D (2021) Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity-weighted coil combination. Magn Reson Med 86:1859–1872
Lin K, Heckel R (2023) Robustness of deep learning for accelerated MRI: benefits of diverse training data. https://doi.org/10.48550/arXiv.2312.10271
Fabian Z, Heckel R, Soltanolkotabi M (2021) Data augmentation for deep learning based accelerated MRI reconstruction with limited data. Proc. 38th Int. Conf. Mach. Learn. PMLR, pp 3057–3067
Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6:60
Weiss K, Khoshgoftaar TM, Wang D (2016) A survey of transfer learning. J Big Data 3:9
Han Y, Yoo J, Kim HH, Shin HJ, Sung K, Ye JC (2018) Deep learning with domain adaptation for accelerated projection-reconstruction MR. Magn Reson Med 80:1189–1205
Dar SUH, Özbey M, Çatlı AB, Çukur T (2020) A Transfer-learning approach for accelerated mri using deep neural networks. Magn Reson Med 84:663–685
Korkmaz Y, Dar SUH, Yurt M, Özbey M, Çukur T (2022) Unsupervised MRI reconstruction via zero-shot learned adversarial transformers. IEEE Trans Med Imaging 41:1747–1763
Wang F, Zhang H, Dai F, Chen W, Wang C, Wang H (2021) MAGnitude-image-to-complex K-space (MAGIC-K) net: a data augmentation network for image reconstruction. Diagnostics 11:1935
Article CAS PubMed PubMed Central Google Scholar
Wang Z, Yu X, Wang C, Chen W, Wang J, Chu Y-H, Sun H, Li R, Li P, Yang F, Han H, Kang T, Lin J, Yang C, Chang S, Shi Z, Hua S, Li Y, Hu J, Zhu L, Zhou J, Lin M, Guo J, Cai C, Chen Z, Guo D, Qu X (2023) One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction. https://doi.org/10.48550/arXiv.2307.13220
Deveshwar N, Rajagopal A, Sahin S, Shimron E, Larson PEZ (2023) Synthesizing complex-valued multicoil MRI data from magnitude-only images. Bioengineering 10:358
Article PubMed PubMed Central Google Scholar
Shimron E, Tamir JI, Wang K, Lustig M (2021) Subtle Inverse Crimes: Naïvely training machine learning algorithms could lead to overly-optimistic results. https://doi.org/10.48550/arXiv.2109.08237
Tremblay J, Prakash A, Acuna D, Brophy M, Jampani V, Anil C, To T, Cameracci E, Boochoon S, Birchfield S (2018) Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization. IEEE Computer Society, pp 1082–10828
OpenAI, Akkaya I, Andrychowicz M, Chociej M, Litwin M, McGrew B, Petron A, Paino A, Plappert M, Powell G, Ribas R, Schneider J, Tezak N, Tworek J, Welinder P, Weng L, Yuan Q, Zaremba W, Zhang L (2019) Solving Rubik’s Cube with a Robot Hand. [cs.LG] https://doi.org/10.48550/arXiv.1910.07113
Ghorbani A, Natarajan V, Coz D, Liu Y (2019) DermGAN: Synthetic Generation of Clinical Skin Images with Pathology. https://doi.org/10.48550/arXiv.1911.08716
Nikolenko SI (2019) Synthetic data for deep learning. https://doi.org/10.48550/arXiv.1909.11512
Khan AR, Khan S, Harouni M, Abbasi R, Iqbal S, Mehmood Z (2021) Brain tumor segmentation using K-means clustering and deep learning with synthetic data augmentation for classification. Microsc Res Tech 84:1389–1399
de Melo CM, Torralba A, Guibas L, DiCarlo J, Chellappa R, Hodgins J (2022) Next-generation deep learning based on simulators and synthetic data. Trends Cogn Sci 26:174–187
Shin H-C, Tenenholtz NA, Rogers JK, Schwarz CG, Senjem ML, Gunter JL, Andriole KP, Michalski M (2018) Medical Image Synthesis for Data Augmentation and Anonymization Using Generative Adversarial Networks. In: Gooya A, Goksel O, Oguz I, Burgos N (eds) Simul. Springer International Publishing, Cham, Synth. Med. Imaging, pp 1–11
Tariq U, Qureshi R, Zafar A, Aftab D, Wu J, Alam T, Shah Z, Ali H (2023) Brain Tumor Synthetic Data Generation with Adaptive StyleGANs. In: Longo L, O’Reilly R (eds) Artif. Intell. Cogn. Sci. Springer Nature Switzerland, Cham, pp 147–159
Yang Q, Lin Y, Wang J, Bao J, Wang X, Ma L, Zhou Z, Yang Q, Cai S, He H, Cai C, Dong J, Cheng J, Chen Z, Zhong J (2022) MOdel-based synthetic data-driven learning (MOST-DL): application in single-shot T2 mapping with severe head motion using overlapping-echo acquisition. IEEE Trans Med Imaging 41:3167–3181
Sun H, Plawinski J, Subramaniam S, Jamaludin A, Kadir T, Readie A, Ligozio G, Ohlssen D, Baillie M, Coroller T (2021) A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANs. https://doi.org/10.48550/arXiv.2106.13199
Tudosiu P-D, Pinaya WHL, Graham MS, Borges P, Fernandez V, Yang D, Appleyard J, Novati G, Mehra D, Vella M, Nachev P, Ourselin S, Cardoso J (2022) Morphology-Preserving Autoregressive 3D Generative Modelling of the Brain. In: Zhao C, Svoboda D, Wolterink JM, Escobar M (eds) Simul. Springer International Publishing, Cham, Synth. Med. Imaging, pp 66–78
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative Adversarial Nets. Adv. Neural Inf. Process. Syst. 27
Wang S-Y, Wang O, Zhang R, Owens A, Efros AA (2020) CNN-Generated Images Are Surprisingly Easy to Spot… for Now. 2020 IEEECVF Conf. Comput. Vis. Pattern Recognit, Institute of Electrical and Electronics Engineers (IEEE), CVPR. pp 8692–8701
Lee J, Mustafaev T, Nishikawa RM (2023) Impact of GAN artifacts for simulating mammograms on identifying mammographically occult cancer. J Med Imaging 10:054503
Isola P, Zhu J-Y, Zhou T, Efros AA (2017) Image-to-Image Translation with Conditional Adversarial Networks. 2017 IEEE Conf. Comput. Vis. Pattern Recognit, Institute of Electrical and Electronics Engineers (IEEE), CVPR. pp 5967–5976
Makhzani A, Shlens J, Jaitly N, Goodfellow I, Frey B (2016) Adversarial Autoencoders. https://doi.org/10.48550/arXiv.1511.05644
Inati SJ, Hansen MS, Kellman P (2014) A Fast Optimal Method for Coil Sensitivity Estimation and Adaptive Coil Combination for Complex Images. Proc. 22nd Annu. Meet. ISMRM
Sriram A, Zbontar J, Murrell T, Defazio A, Zitnick CL, Yakubova N, Knoll F, Johnson P (2020) End-to-End Variational Networks for Accelerated MRI Reconstruction. https://doi.org/10.48550/arXiv.2004.06688
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13:600–612
Miao J, Huang F, Narayan S, Wilson DL (2013) A new perceptual difference model for diagnostically relevant quantitative image quality evaluation: A preliminary study. Magn Reson Imaging 31:596–603
Cubuk ED, Zoph B, Mané D, Vasudevan V, Le QV (2019) AutoAugment: Learning Augmentation Strategies From Data. 2019 IEEECVF Conf. Comput. Vis. Pattern Recognit, Institute of Electrical and Electronics Engineers (IEEE), CVPR. pp 113–123
Kingma DP, Welling M (2014) Auto-Encoding Variational Bayes. 2nd Int. Conf. Learn. Represent. ICLR 2014 Banff AB Can. April 14–16 2014 Conf. Track Proc.
Larsen ABL, Sønderby SK, Larochelle H, Winther O (2016) Autoencoding beyond pixels using a learned similarity metric. In: International conference on machine learning. PMLR
Ho J, Jain A, Abbeel P (2020) Denoising Diffusion Probabilistic Models. https://doi.org/10.48550/arXiv.2006.11239
Ramesh A, Dhariwal P, Nichol A, Chu C, Chen M (2022) Hierarchical Text-Conditional Image Generation with CLIP Latents. https://doi.org/10.48550/arXiv.2204.06125
Rombach R, Blattmann A, Lorenz D, Esser P, Ommer B (2022) H
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