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Inhaltsangabe
Part I. Theory of Deep Learning for Image Reconstruction Michael Unser: 1. Formalizing deep neural networks Jong Chul Ye and Sangmin Lee; 2. Geometry of deep learning Saiprasad Ravishankar, Zhishen Huang, Michael McCann and Siqi Ye; 3. Model-based reconstruction with learning: from unsupervised to supervised and beyond Yuelong Li, Or Bar-Shira, Vishal Monga and Yonina C. Eldar; 4. Deep algorithm unrolling for biomedical; Part II. Deep Learning Architecture for Various Imaging Modalities Haimiao Zhang, Bin Dong, Ge Wang and Baodong Liu: 5. Deep learning for CT image reconstruction Guang-Hong Chen, Chengzhu Zhang, Yinsheng Li, Yoseob Han and Jong Chul Ye; 6. Deep learning in CT reconstruction: bring the measured data to tasks Patricia Johnson and Florian Knoll; 7. Overview deep learning reconstruction of accelerated MRI Mathews Jacob, Hemant K. Aggarwal and Qing Zou; 8. Model-based deep learning algorithms for inverse problems Mehmet Akcakaya, Gyutaek Oh, Jong Chul Ye; 9. k-space deep learning for MR reconstruction and artifact removal Ruud J. G. van Sloun, Jong Chul Ye and Yonina C Eldar; 10. Deep learning for ultrasound beamforming Jaeyoung Huh, Shujaat Khan and Jong Chul Ye; 11. Ultrasound image artifact removal using deep neural network; Part III. Generative Models for Biomedical Imaging Jaejun Yoo, Michael Unser: 12. Deep generative models for biomedical image reconstruction Tolga C¿ukur, Mahmut Yurt, Salman Ul Hassan Dar, Hyungjin Chun and, Jong Chul Ye; 13. Image synthesis in multi-contrast MRI with generative adversarial networks Jaejun Yoo and Michael Unser; 14. Regularizing deep-neural-network paradigm for the reconstruction of dynamic magnetic resonance images Thanh-an Pham, Fangshu Yang and Michael Unser; 15. Regularizing neural network for phase unwrapping Michael T. McCann, Laur`ene Donati, Harshit Gupta and Michael Unser; 16. CryoGAN: a deep generative adversarial approach to single-particle cryo-em; Index.
Part I. Theory of Deep Learning for Image Reconstruction Michael Unser: 1. Formalizing deep neural networks Jong Chul Ye and Sangmin Lee; 2. Geometry of deep learning Saiprasad Ravishankar, Zhishen Huang, Michael McCann and Siqi Ye; 3. Model-based reconstruction with learning: from unsupervised to supervised and beyond Yuelong Li, Or Bar-Shira, Vishal Monga and Yonina C. Eldar; 4. Deep algorithm unrolling for biomedical; Part II. Deep Learning Architecture for Various Imaging Modalities Haimiao Zhang, Bin Dong, Ge Wang and Baodong Liu: 5. Deep learning for CT image reconstruction Guang-Hong Chen, Chengzhu Zhang, Yinsheng Li, Yoseob Han and Jong Chul Ye; 6. Deep learning in CT reconstruction: bring the measured data to tasks Patricia Johnson and Florian Knoll; 7. Overview deep learning reconstruction of accelerated MRI Mathews Jacob, Hemant K. Aggarwal and Qing Zou; 8. Model-based deep learning algorithms for inverse problems Mehmet Akcakaya, Gyutaek Oh, Jong Chul Ye; 9. k-space deep learning for MR reconstruction and artifact removal Ruud J. G. van Sloun, Jong Chul Ye and Yonina C Eldar; 10. Deep learning for ultrasound beamforming Jaeyoung Huh, Shujaat Khan and Jong Chul Ye; 11. Ultrasound image artifact removal using deep neural network; Part III. Generative Models for Biomedical Imaging Jaejun Yoo, Michael Unser: 12. Deep generative models for biomedical image reconstruction Tolga C¿ukur, Mahmut Yurt, Salman Ul Hassan Dar, Hyungjin Chun and, Jong Chul Ye; 13. Image synthesis in multi-contrast MRI with generative adversarial networks Jaejun Yoo and Michael Unser; 14. Regularizing deep-neural-network paradigm for the reconstruction of dynamic magnetic resonance images Thanh-an Pham, Fangshu Yang and Michael Unser; 15. Regularizing neural network for phase unwrapping Michael T. McCann, Laur`ene Donati, Harshit Gupta and Michael Unser; 16. CryoGAN: a deep generative adversarial approach to single-particle cryo-em; Index.
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