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Medical Radiation Exposure Reduction in PET via Super-Resolution Deep Learning Model.
Yoshimura, Takaaki; Hasegawa, Atsushi; Kogame, Shoki; Magota, Keiichi; Kimura, Rina; Watanabe, Shiro; Hirata, Kenji; Sugimori, Hiroyuki.
Afiliación
  • Yoshimura T; Department of Health Sciences and Technology, Faculty of Health Sciences, Hokkaido University, Sapporo 060-0812, Japan.
  • Hasegawa A; Department of Medical Physics, Hokkaido University Hospital, Sapporo 060-8648, Japan.
  • Kogame S; Department of Health Sciences, School of Medicine, Hokkaido University, Sapporo 060-0812, Japan.
  • Magota K; Graduate School of Biological Science and Engineering, Hokkaido University, Sapporo 060-8638, Japan.
  • Kimura R; Division of Medical Imaging and Technology, Hokkaido University Hospital, Sapporo 060-8648, Japan.
  • Watanabe S; Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo 060-8648, Japan.
  • Hirata K; Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Sapporo 060-8638, Japan.
  • Sugimori H; Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Sapporo 060-8638, Japan.
Diagnostics (Basel) ; 12(4)2022 Mar 31.
Article en En | MEDLINE | ID: mdl-35453920
In positron emission tomography (PET) imaging, image quality correlates with the injected [18F]-fluorodeoxyglucose (FDG) dose and acquisition time. If image quality improves from short-acquisition PET images via the super-resolution (SR) deep learning technique, it is possible to reduce the injected FDG dose. Therefore, the aim of this study was to clarify whether the SR deep learning technique could improve the image quality of the 50%-acquisition-time image to the level of that of the 100%-acquisition-time image. One-hundred-and-eight adult patients were enrolled in this retrospective observational study. The supervised data were divided into nine subsets for nested cross-validation. The mean peak signal-to-noise ratio and structural similarity in the SR-PET image were 31.3 dB and 0.931, respectively. The mean opinion scores of the 50% PET image, SR-PET image, and 100% PET image were 3.41, 3.96, and 4.23 for the lung level, 3.31, 3.80, and 4.27 for the liver level, and 3.08, 3.67, and 3.94 for the bowel level, respectively. Thus, the SR-PET image was more similar to the 100% PET image and subjectively improved the image quality, as compared to the 50% PET image. The use of the SR deep-learning technique can reduce the injected FDG dose and thus lower radiation exposure.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Observational_studies / Prognostic_studies Idioma: En Revista: Diagnostics (Basel) Año: 2022 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Observational_studies / Prognostic_studies Idioma: En Revista: Diagnostics (Basel) Año: 2022 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Suiza