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Deep learning for tomographic image reconstruction
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“Recent advances in image processing, especially deep learning-based imaging [15], [16], have offered a potential solution to overcome the low SNR challenge in the ultra-low-field regime, ensuring their values in clinical applications.”
BackgroundSelf-citationOptimization and image quality in CT studies: application of DRLs based on clinical indication
Picone, C., Porto, A., Fusco, R., Granata, C., Brunese, M. C., Granata, V., Ottaiano, A., Barretta, M., Caranci, F., Pecori, B., et al. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-07125-8 · Cited by 3
Metadata
- Authors
- Wang, G., Ye, J. C., & De Man, B.
- Venue
- Nature Machine Intelligence
- Published
- 2020-12-10
- Volume
- 2
- Issue
- 12
- Pages
- 737-748
- ISSN
- 2522-5839
- Cited by
- 531
- References
- 7
- Language
- en
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Deep learning for tomographic image reconstruction. https://app.cochat.ai/papers/10.1038/s42256-020-00273-z
- The paper
- Wang, G., Ye, J. C., & De Man, B. (2020). Deep learning for tomographic image reconstruction. Nature Machine Intelligence. https://doi.org/10.1038/s42256-020-00273-z