Restoration of amyloid PET images obtained with short-time data using a generative adversarial networks framework

Abstract Our purpose in this study is to evaluate the clinical feasibility of deep-learning techniques for F-18 florbetaben (FBB) positron emission tomography (PET) image reconstruction using data acquired in a short time. We reconstructed raw FBB PET data of 294 patients acquired for 20 and 2 min i...

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Autores principales:	Young Jin Jeong, Hyoung Suk Park, Ji Eun Jeong, Hyun Jin Yoon, Kiwan Jeon, Kook Cho, Do-Young Kang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/45e7f2ba87c843b8884d08ce8c70d9f5
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spelling	oai:doaj.org-article:45e7f2ba87c843b8884d08ce8c70d9f52021-12-02T13:20:13ZRestoration of amyloid PET images obtained with short-time data using a generative adversarial networks framework10.1038/s41598-021-84358-82045-2322https://doaj.org/article/45e7f2ba87c843b8884d08ce8c70d9f52021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84358-8https://doaj.org/toc/2045-2322Abstract Our purpose in this study is to evaluate the clinical feasibility of deep-learning techniques for F-18 florbetaben (FBB) positron emission tomography (PET) image reconstruction using data acquired in a short time. We reconstructed raw FBB PET data of 294 patients acquired for 20 and 2 min into standard-time scanning PET (PET20m) and short-time scanning PET (PET2m) images. We generated a standard-time scanning PET-like image (sPET20m) from a PET2m image using a deep-learning network. We did qualitative and quantitative analyses to assess whether the sPET20m images were available for clinical applications. In our internal validation, sPET20m images showed substantial improvement on all quality metrics compared with the PET2m images. There was a small mean difference between the standardized uptake value ratios of sPET20m and PET20m images. A Turing test showed that the physician could not distinguish well between generated PET images and real PET images. Three nuclear medicine physicians could interpret the generated PET image and showed high accuracy and agreement. We obtained similar quantitative results by means of temporal and external validations. We can generate interpretable PET images from low-quality PET images because of the short scanning time using deep-learning techniques. Although more clinical validation is needed, we confirmed the possibility that short-scanning protocols with a deep-learning technique can be used for clinical applications.Young Jin JeongHyoung Suk ParkJi Eun JeongHyun Jin YoonKiwan JeonKook ChoDo-Young KangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Young Jin Jeong Hyoung Suk Park Ji Eun Jeong Hyun Jin Yoon Kiwan Jeon Kook Cho Do-Young Kang Restoration of amyloid PET images obtained with short-time data using a generative adversarial networks framework
description	Abstract Our purpose in this study is to evaluate the clinical feasibility of deep-learning techniques for F-18 florbetaben (FBB) positron emission tomography (PET) image reconstruction using data acquired in a short time. We reconstructed raw FBB PET data of 294 patients acquired for 20 and 2 min into standard-time scanning PET (PET20m) and short-time scanning PET (PET2m) images. We generated a standard-time scanning PET-like image (sPET20m) from a PET2m image using a deep-learning network. We did qualitative and quantitative analyses to assess whether the sPET20m images were available for clinical applications. In our internal validation, sPET20m images showed substantial improvement on all quality metrics compared with the PET2m images. There was a small mean difference between the standardized uptake value ratios of sPET20m and PET20m images. A Turing test showed that the physician could not distinguish well between generated PET images and real PET images. Three nuclear medicine physicians could interpret the generated PET image and showed high accuracy and agreement. We obtained similar quantitative results by means of temporal and external validations. We can generate interpretable PET images from low-quality PET images because of the short scanning time using deep-learning techniques. Although more clinical validation is needed, we confirmed the possibility that short-scanning protocols with a deep-learning technique can be used for clinical applications.
format	article
author	Young Jin Jeong Hyoung Suk Park Ji Eun Jeong Hyun Jin Yoon Kiwan Jeon Kook Cho Do-Young Kang
author_facet	Young Jin Jeong Hyoung Suk Park Ji Eun Jeong Hyun Jin Yoon Kiwan Jeon Kook Cho Do-Young Kang
author_sort	Young Jin Jeong
title	Restoration of amyloid PET images obtained with short-time data using a generative adversarial networks framework
title_short	Restoration of amyloid PET images obtained with short-time data using a generative adversarial networks framework
title_full	Restoration of amyloid PET images obtained with short-time data using a generative adversarial networks framework
title_fullStr	Restoration of amyloid PET images obtained with short-time data using a generative adversarial networks framework
title_full_unstemmed	Restoration of amyloid PET images obtained with short-time data using a generative adversarial networks framework
title_sort	restoration of amyloid pet images obtained with short-time data using a generative adversarial networks framework
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/45e7f2ba87c843b8884d08ce8c70d9f5
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Restoration of amyloid PET images obtained with short-time data using a generative adversarial networks framework

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