Generation of short-term follow-up chest CT images using a latent diffusion model in COVID-19
The similarity and validity of generated predictive images for the course of COVID-19-associated pneumonia using a diffusion model were reasonable. The generation of prognostic images may suggest potential utility for early prediction of the clinical course in COVID-19-associated pneumonia and other respiratory diseases.
Authors
- Yasuo Takiguchi
- Hideaki Haneishi (ORCID: https://orcid.org/0000-0001-7157-4309)
- Naoko Kawata (ORCID: https://orcid.org/0000-0002-4083-4531)
- Takayuki Okamoto
- Takashi Higashide
- Takuji Suzuki (ORCID: https://orcid.org/0000-0002-7257-6792)
- Masaru Nagayoshi
- Yukiko Matsuura
- Yuki Sekiguchi
- Yuma Iwao
Institutions
- Chiba University (JP)
- Chiba University Hospital (JP)
- National Institutes for Quantum Science and Technology (JP)
- Chiba Aoba Municipal Hospital (JP)
- Japanese Red Cross Narita Hospital (JP)
Publication Details
- Journal
- Japanese Journal of Radiology
- Published
- 2024-11-25
- DOI
- https://doi.org/10.1007/s11604-024-01699-w
- Citations
- 3
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 1.21
Funders
- Japanese Respiratory Foundation
- Japan Society for the Promotion of Science