Anatomy-Weighted CTDIvol from Routine CT Metadata: A Patient-Specific, Multi-Vendor Study Using Deep-Learning Segmentation

Shuji Yamamoto

Open source

DOI: 10.3390/tomography12090125
Published: 08
Container: Tomography
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3390/tomography12090125,
  title = {Anatomy-Weighted CTDIvol from Routine CT Metadata: A Patient-Specific, Multi-Vendor Study Using Deep-Learning Segmentation},
  author = {Shuji Yamamoto},
  year = {2026},
  journal = {Tomography},
  doi = {10.3390/tomography12090125},
  url = {https://doi.org/10.3390/tomography12090125}
}

RIS

TY  - JOUR
TI  - Anatomy-Weighted CTDIvol from Routine CT Metadata: A Patient-Specific, Multi-Vendor Study Using Deep-Learning Segmentation
AU  - Shuji Yamamoto
PY  - 2026
JO  - Tomography
DO  - 10.3390/tomography12090125
UR  - https://doi.org/10.3390/tomography12090125
ER  -

APA

Yamamoto, S. (2026). Anatomy-Weighted CTDIvol from Routine CT Metadata: A Patient-Specific, Multi-Vendor Study Using Deep-Learning Segmentation. Tomography. https://doi.org/10.3390/tomography12090125

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doaj · retrieved 2026-09-25T09:00:07.120Z