Deep learning for tomographic image reconstruction

2020/12/10 by Ge Wang, Jong Chul Ye, Bruno De Man · 1 voice · 5 citations
Engineering · Medicine · #Advanced X-ray and CT Imaging #Medical Imaging Techniques and Applications #Radiomics and Machine Learning in Medical Imaging

paper · doi:10.1038/s42256-020-00273-z

crossref issued 2020/12/10 · crossref published 2020/12/10 · crossref published-online 2020/12/10 · openalex publication_date 2020/12/10 · crossref created 2020/12/10 · crossref deposited 2022/12/07 · openalex created_date 2025/10/10 · crossref indexed 2026/07/31 · openalex updated_date 2026/08/01

Citations

• Generalized autocalibrating partially parallel acquisitions (GRAPPA)
• Federated Learning: Strategies for Improving Communication Efficiency
• Deep Convolutional Neural Network for Inverse Problems in Imaging
• Deep-STORM: super-resolution single-molecule microscopy by deep learning
• Noise2Noise: Learning Image Restoration without Clean Data
• The Moral Machine experiment
• Consensus Neural Network for Medical Imaging Denoising with Only Noisy Training Samples
• Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement
• SENSE: Sensitivity encoding for fast MRI
• Compressed sensing
• Advances in Neural Information Processing Systems 32 (NeurIPS 2019)

Cited by

• Efficient Noise Calculation in Deep Learning-based MRI Reconstructions
• Sparse-view irradiation processing volumetric additive manufacturing
• Physics-Driven Neural Compensation For Electrical Impedance Tomography
• CoIL: Coordinate-based Internal Learning for Imaging Inverse Problems
• Machine learning on neutron and x-ray scattering and spectroscopies

Discussions

• Yes, this is a must: Wang, G., Ye, J.C. & De Man, B. Deep learning for tomographic image reconstruction. Nat Mach Intell 2, 737–748 (2020). doi.org/10.1038/s42256… [bsky, 0 points, 0 comments]