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Generative adversarial network-created brain SPECTs of cerebral ischemia are indistinguishable to scans from real patients

Deep convolutional generative adversarial networks (GAN) allow for creating images from existing databases. We applied a modified light-weight GAN (FastGAN) algorithm to cerebral blood flow SPECTs and aimed to evaluate whether this technology can generate created images close to real patients. Inves...

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Autores principales: Werner, Rudolf A., Higuchi, Takahiro, Nose, Naoko, Toriumi, Fujio, Matsusaka, Yohji, Kuji, Ichiei, Kazuhiro, Koshino
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9637159/
https://www.ncbi.nlm.nih.gov/pubmed/36335166
http://dx.doi.org/10.1038/s41598-022-23325-3
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author Werner, Rudolf A.
Higuchi, Takahiro
Nose, Naoko
Toriumi, Fujio
Matsusaka, Yohji
Kuji, Ichiei
Kazuhiro, Koshino
author_facet Werner, Rudolf A.
Higuchi, Takahiro
Nose, Naoko
Toriumi, Fujio
Matsusaka, Yohji
Kuji, Ichiei
Kazuhiro, Koshino
author_sort Werner, Rudolf A.
collection PubMed
description Deep convolutional generative adversarial networks (GAN) allow for creating images from existing databases. We applied a modified light-weight GAN (FastGAN) algorithm to cerebral blood flow SPECTs and aimed to evaluate whether this technology can generate created images close to real patients. Investigating three anatomical levels (cerebellum, CER; basal ganglia, BG; cortex, COR), 551 normal (248 CER, 174 BG, 129 COR) and 387 pathological brain SPECTs using N-isopropyl p-I-123-iodoamphetamine ((123)I-IMP) were included. For the latter scans, cerebral ischemic disease comprised 291 uni- (66 CER, 116 BG, 109 COR) and 96 bilateral defect patterns (44 BG, 52 COR). Our model was trained using a three-compartment anatomical input (dataset ‘A’; including CER, BG, and COR), while for dataset ‘B’, only one anatomical region (COR) was included. Quantitative analyses provided mean counts (MC) and left/right (LR) hemisphere ratios, which were then compared to quantification from real images. For MC, ‘B’ was significantly different for normal and bilateral defect patterns (P < 0.0001, respectively), but not for unilateral ischemia (P = 0.77). Comparable results were recorded for LR, as normal and ischemia scans were significantly different relative to images acquired from real patients (P ≤ 0.01, respectively). Images provided by ‘A’, however, revealed comparable quantitative results when compared to real images, including normal (P = 0.8) and pathological scans (unilateral, P = 0.99; bilateral, P = 0.68) for MC. For LR, only uni- (P = 0.03), but not normal or bilateral defect scans (P ≥ 0.08) reached significance relative to images of real patients. With a minimum of only three anatomical compartments serving as stimuli, created cerebral SPECTs are indistinguishable to images from real patients. The applied FastGAN algorithm may allow to provide sufficient scan numbers in various clinical scenarios, e.g., for “data-hungry” deep learning technologies or in the context of orphan diseases.
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spelling pubmed-96371592022-11-07 Generative adversarial network-created brain SPECTs of cerebral ischemia are indistinguishable to scans from real patients Werner, Rudolf A. Higuchi, Takahiro Nose, Naoko Toriumi, Fujio Matsusaka, Yohji Kuji, Ichiei Kazuhiro, Koshino Sci Rep Article Deep convolutional generative adversarial networks (GAN) allow for creating images from existing databases. We applied a modified light-weight GAN (FastGAN) algorithm to cerebral blood flow SPECTs and aimed to evaluate whether this technology can generate created images close to real patients. Investigating three anatomical levels (cerebellum, CER; basal ganglia, BG; cortex, COR), 551 normal (248 CER, 174 BG, 129 COR) and 387 pathological brain SPECTs using N-isopropyl p-I-123-iodoamphetamine ((123)I-IMP) were included. For the latter scans, cerebral ischemic disease comprised 291 uni- (66 CER, 116 BG, 109 COR) and 96 bilateral defect patterns (44 BG, 52 COR). Our model was trained using a three-compartment anatomical input (dataset ‘A’; including CER, BG, and COR), while for dataset ‘B’, only one anatomical region (COR) was included. Quantitative analyses provided mean counts (MC) and left/right (LR) hemisphere ratios, which were then compared to quantification from real images. For MC, ‘B’ was significantly different for normal and bilateral defect patterns (P < 0.0001, respectively), but not for unilateral ischemia (P = 0.77). Comparable results were recorded for LR, as normal and ischemia scans were significantly different relative to images acquired from real patients (P ≤ 0.01, respectively). Images provided by ‘A’, however, revealed comparable quantitative results when compared to real images, including normal (P = 0.8) and pathological scans (unilateral, P = 0.99; bilateral, P = 0.68) for MC. For LR, only uni- (P = 0.03), but not normal or bilateral defect scans (P ≥ 0.08) reached significance relative to images of real patients. With a minimum of only three anatomical compartments serving as stimuli, created cerebral SPECTs are indistinguishable to images from real patients. The applied FastGAN algorithm may allow to provide sufficient scan numbers in various clinical scenarios, e.g., for “data-hungry” deep learning technologies or in the context of orphan diseases. Nature Publishing Group UK 2022-11-05 /pmc/articles/PMC9637159/ /pubmed/36335166 http://dx.doi.org/10.1038/s41598-022-23325-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Werner, Rudolf A.
Higuchi, Takahiro
Nose, Naoko
Toriumi, Fujio
Matsusaka, Yohji
Kuji, Ichiei
Kazuhiro, Koshino
Generative adversarial network-created brain SPECTs of cerebral ischemia are indistinguishable to scans from real patients
title Generative adversarial network-created brain SPECTs of cerebral ischemia are indistinguishable to scans from real patients
title_full Generative adversarial network-created brain SPECTs of cerebral ischemia are indistinguishable to scans from real patients
title_fullStr Generative adversarial network-created brain SPECTs of cerebral ischemia are indistinguishable to scans from real patients
title_full_unstemmed Generative adversarial network-created brain SPECTs of cerebral ischemia are indistinguishable to scans from real patients
title_short Generative adversarial network-created brain SPECTs of cerebral ischemia are indistinguishable to scans from real patients
title_sort generative adversarial network-created brain spects of cerebral ischemia are indistinguishable to scans from real patients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9637159/
https://www.ncbi.nlm.nih.gov/pubmed/36335166
http://dx.doi.org/10.1038/s41598-022-23325-3
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