セッション詳細
Image Informatics: Generative AI-2
2026年4月18日(土) 9:40 〜 10:50
502
Chairperson:Shogo Baba(Seinan Gakuin University), Akihiro Haga(Tokushima University)
[TPI-095]Evaluating image quality reproducibility using deep learning for exposure reduction in sparse-projection and low-dose CT images
Horiuchi Hyogo, Usui Keisuke, Kyogoku Shinsuke, Sakamoto Hajime, Akimoto Shintarou (Department of Radiological Technology, Faculty of Health Science, Juntendo University)
[TPI-096]Ensemble learning of diffusion models for sparse-view CT reconstruction
Sho Ozaki1, Shizuo Kaji2, Kanabu Nawa3, Toshikazu Imae4, Hideomi Yamashita4, Keiichi Nakagawa4 (1.Graduate School of Science and Technology, Hirosaki University, 2.Center for Science Adventure and Collaborative Research Advancement, Kyoto University, 3.Kansai BNCT Medical Center Osaka Medical and Pharmaceutical University, 4.Department of Radiology, University of Tokyo Hospital)
[TPI-097]Boosting diffusion models for ultra-sparse-view CT reconstruction
Yura Kimura1, Sho Ozaki2, Hideki Obara3, Masahiko Aoki3 (1.Faculty of Science and Technology, Hirosaki University, 2.Graduate School of Science and Technology, Hirosaki University, 3.Department of Radiation Oncology, Hirosaki University)
[TPI-098]Image quality improvement of dual-source cone-beam computed tomography using a conditional latent diffusion model
Ayane Nakanishi, Megumi Nakao, Naruki Murahashi, Hinako Isogai, Hedaki Hirashima, Takahiro Iwai, Michio Yoshimura, Takashi Mizowaki, Mitsuhiro Nakamura (Kyoto University)
[TPI-099]Impact of deformable registration on CBCT image quality improvement using diffusion-basedgenerative models
Mizuki Igarashi, Megumi Nakao, Naruki Murahashi, Hideaki Hirashima, Takahiro Iwai, Michio Yoshimura, Takashi Mizowaki, Mitsuhiro Nakamura (Kyoto University)
[TPI-100]Fundamental study on DeepPET image reconstruction using GATE simulations modeling a LYSO scintillator array
Soichiro Kayo, Yutaka Otaka (Department of Radiological sciences, Faculty of Medical science technology, Morinomiya University of Medical sciences)
[TPI-101]Image quality enhancement of low-dose PET/CT using a Pix2Pix-based deep learning model
Juwon LIM1, Jisoo Hong1, Dohyeon Im1, Dahyun Yun1, Inseok Kil1, Jeho No1, Moojin JEONG2, Hoonhee PARK1 (1.Dept. of Radiological Science, Shingu University, 2.Dept. of Radiology, Samsung Medical Center)