The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT
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Citations
Cites
Management of Hepatocellular Carcinoma: An Update Δσ
Bruix, J., & Sherman, M. (2011) · Hepatology · doi:10.1002/hep.24199 · Cited by 8,103
Management of Hepatocellular Carcinoma *
Bruix, J., & Sherman, M. (2005) · Hepatology · doi:10.1002/hep.20933 · Cited by 5,857
Mathews, J. D., Forsythe, A. V., Brady, Z., Butler, M. W., Goergen, S. K., Byrnes, G. B., Giles, G. G., Wallace, A. B., Anderson, P. R., Guiver, T. A., et al. (2013) · BMJ · doi:10.1136/bmj.f2360 · Cited by 2,029
How sample size influences research outcomes
Faber, J., & Fonseca, L. M. (2014) · Dental Press Journal of Orthodontics · doi:10.1590/2176-9451.19.4.027-029.ebo · Cited by 946
Reproducibility of histomorphologic diagnoses with special reference to the kappa statistic
SVANHOLM, H., STARKLINT, H., GUNDERSEN, H. J. G., FABRICIUS, J., BARLEBO, H., & OLSEN, S. (1989) · APMIS · doi:10.1111/j.1699-0463.1989.tb00464.x · Cited by 453
Nakayama, Y., Awai, K., Funama, Y., Hatemura, M., Imuta, M., Nakaura, T., Ryu, D., Morishita, S., Sultana, S., Sato, N., et al. (2005) · Radiology · doi:10.1148/radiol.2373041655 · Cited by 422
Self-citationPickhardt, P. J., Lubner, M. G., Kim, D. H., Tang, J., Ruma, J. A., del Rio, A. M., & Chen, G.-H. (2012) · American Journal of Roentgenology · doi:10.2214/ajr.12.9382 · Cited by 309
Deák, Z., Grimm, J. M., Treitl, M., Geyer, L. L., Linsenmaier, U., Körner, M., Reiser, M. F., & Wirth, S. (2013) · Radiology · doi:10.1148/radiol.12112707 · Cited by 238
Size-specific Dose Estimates for Adult Patients at CT of the Torso
Christner, J. A., Braun, N. N., Jacobsen, M. C., Carter, R. E., Kofler, J. M., & McCollough, C. H. (2012) · Radiology · doi:10.1148/radiol.12112365 · Cited by 188
Brady, S. L., & Kaufman, R. A. (2012) · Radiology · doi:10.1148/radiol.12120131 · Cited by 137
Cited by
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Xu, X., Jiang, X., Ma, C., Du, P., Li, X., Lv, S., Yu, L., Ni, Q., Chen, Y., Su, J., et al. (2020) · Engineering · doi:10.1016/j.eng.2020.04.010 · Cited by 1,209
Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment
Jamshidi, M., Lalbakhsh, A., Talla, J., Peroutka, Z., Hadjilooei, F., Lalbakhsh, P., Jamshidi, M., Spada, L. L., Mirmozafari, M., Dehghani, M., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3001973 · Cited by 551
Greffier, J., Hamard, A., Pereira, F., Barrau, C., Pasquier, H., Beregi, J. P., & Frandon, J. (2020) · European Radiology · doi:10.1007/s00330-020-06724-w · Cited by 328
Data drift in medical machine learning: implications and potential remedies
Sahiner, B., Chen, W., Samala, R. K., & Petrick, N. (2023) · The British Journal of Radiology · doi:10.1259/bjr.20220878 · Cited by 279
Hamamoto, R., Koyama, T., Takahashi, S., Yasuda, T., Kobayashi, K., Akagi, Y., Kouno, N., Sudo, K., Hirata, M., Sunami, K., et al. (2026) · Journal of Hematology & Oncology · doi:10.1186/s13045-026-01781-y · Cited by 8
Metadata
- Authors
- Akagi, M., Nakamura, Y., Higaki, T., Narita, K., Honda, Y., Zhou, J., Yu, Z., Akino, N., & Awai, K.
- Venue
- European Radiology
- Published
- 2019-04-11
- Volume
- 29
- Issue
- 11
- Pages
- 6163-6171
- ISSN
- 0938-7994 (ISSN-L)
- 1432-1084
- OpenAlex
- W2936378778
- Cited by
- 346
- References
- 18
- FWCI
- 28.35
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT. https://app.cochat.ai/papers/10.1007/s00330-019-06170-3
- The paper
- Akagi, M., Nakamura, Y., Higaki, T., Narita, K., Honda, Y., Zhou, J., Yu, Z., Akino, N., & Awai, K. (2019). Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT. European Radiology. https://doi.org/10.1007/s00330-019-06170-3
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
