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The potential for photon-counting computed tomography and deep learning to reduce radiation dose in paediatric radiology: reply to Nagy et al.

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References

  1. Nagy E, Tschauner S, Schramek C, Sorantin E (2023) Paediatric CT made easy. Pediatr Radiol 53:581–588

    Article  PubMed  Google Scholar 

  2. Kleinerman RA (2006) Cancer risks following diagnostic and therapeutic radiation exposure in children. Pediatr Radiol 36:121–125

    Article  PubMed  PubMed Central  Google Scholar 

  3. Esquivel A, Ferrero A, Mileto A et al (2022) Photon-counting detector CT: key points radiologists should know. Korean J Radiol 23:854–865

    Article  PubMed  PubMed Central  Google Scholar 

  4. Nagayama Y, Goto M, Sakabe D et al (2022) Radiation dose reduction for 80-kVp pediatric CT using deep learning-based reconstruction: a clinical and phantom study. AJR Am J Roentgenol 219:315–324

    Article  PubMed  Google Scholar 

  5. Wang N, Li M, Haverinen P (2023) Photon-counting computed tomography thermometry via material decomposition and machine learning. Vis Comput Ind Biomed Art 6:2

    Article  PubMed  PubMed Central  Google Scholar 

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Correspondence to Ismail Mese.

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Mese, I. The potential for photon-counting computed tomography and deep learning to reduce radiation dose in paediatric radiology: reply to Nagy et al.. Pediatr Radiol 53, 1726–1727 (2023). https://doi.org/10.1007/s00247-023-05684-9

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  • DOI: https://doi.org/10.1007/s00247-023-05684-9

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