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Diagnostic value of a second-generation super-resolution deep learning–based reconstruction combined with a metal artifact reduction algorithm for pelvic CT

Crossref DOI link: https://doi.org/10.1007/s00256-025-05080-4

Published Online: 2025-11-15

Published Print: 2026-04

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Takaishi, Taku

Yasaka, Koichiro

Miyamoto, Kazuyoshi

Gotoda, Kohei

Sato, Chiaki

Abe, Osamu
Funding

Funding for this research was provided by:

The University of Tokyo
License Information

Text and Data Mining valid from 2025-11-15

Version of Record valid from 2025-11-15
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Article History

Received: 20 August 2025

Revised: 31 October 2025

Accepted: 3 November 2025

First Online: 15 November 2025

Declarations

:

: This retrospective study was approved by the Research Ethics Committee of the Faculty of Medicine of The University of Tokyo. Our Institutional Review Board waived the requirement for obtaining written informed consent from patients.

: The authors declare no competing interests.

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