The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study
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Cites
Computed Tomography — An Increasing Source of Radiation Exposure
Brenner, D. J., & Hall, E. J. (2007) · New England Journal of Medicine · doi:10.1056/nejmra072149 · Cited by 8,748
A three‐dimensional statistical approach to improved image quality for multislice helical CT
Thibault, J., Sauer, K. D., Bouman, C. A., & Hsieh, J. (2007) · Medical Physics · doi:10.1118/1.2789499 · Cited by 937
State of the Art: Iterative CT Reconstruction Techniques
Geyer, L. L., Schoepf, U. J., Meinel, F. G., Nance, J. W., Bastarrika, G., Leipsic, J. A., Paul, N. S., Rengo, M., Laghi, A., & De Cecco, C. N. (2015) · Radiology · doi:10.1148/radiol.2015132766 · Cited by 704
Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks
Dou, Q., Chen, H., Yu, L., Zhao, L., Qin, J., Wang, D., Mok, V. C., Shi, L., & Heng, P.-A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528129 · Cited by 679
Willemink, M. J., & Noël, P. B. (2019) · European Radiology · doi:10.1007/s00330-018-5810-7 · Cited by 603
Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT
Akagi, M., Nakamura, Y., Higaki, T., Narita, K., Honda, Y., Zhou, J., Yu, Z., Akino, N., & Awai, K. (2019) · European Radiology · doi:10.1007/s00330-019-06170-3 · Cited by 346
Image quality in CT: From physical measurements to model observers
Verdun, F. R., Racine, D., Ott, J. G., Tapiovaara, M. J., Toroi, P., Bochud, F. O., Veldkamp, W. J. H., Schegerer, A., Bouwman, R. W., Giron, I. H., et al. (2015) · Physica Medica · doi:10.1016/j.ejmp.2015.08.007 · Cited by 300
Katsura, M., Matsuda, I., Akahane, M., Sato, J., Akai, H., Yasaka, K., Kunimatsu, A., & Ohtomo, K. (2012) · European Radiology · doi:10.1007/s00330-012-2452-z · Cited by 278
McCollough, C. H., Chen, G. H., Kalender, W., Leng, S., Samei, E., Taguchi, K., Wang, G., Yu, L., & Pettigrew, R. I. (2012) · Radiology · doi:10.1148/radiol.12112265 · Cited by 276
Powell, S., Magnotta, V. A., Johnson, H., Jammalamadaka, V. K., Pierson, R., & Andreasen, N. C. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.05.063 · Cited by 170
Metadata
- Authors
- Greffier, J., Hamard, A., Pereira, F., Barrau, C., Pasquier, H., Beregi, J. P., & Frandon, J.
- Venue
- European Radiology
- Published
- 2020-02-25
- Volume
- 30
- Issue
- 7
- Pages
- 3951-3959
- ISSN
- 0938-7994 (ISSN-L)
- 1432-1084
- OpenAlex
- W3007407399
- Cited by
- 328
- References
- 19
- FWCI
- 22.08
- Type
- journal article
- Language
- en
- Open access
- green
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- The paper
- Greffier, J., Hamard, A., Pereira, F., Barrau, C., Pasquier, H., Beregi, J. P., & Frandon, J. (2020). Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study. European Radiology. https://doi.org/10.1007/s00330-020-06724-w
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
