@article{KazuhinoWernerToriumietal.2018,
  author    = {Kazuhino, Koshino and Werner, Rudolf A. and Toriumi, Fuijo and Javadi, Mehrbod S. and Pomper, Martin G. and Solnes, Lilja B. and Verde, Franco and Higuchi, Takahiro and Rowe, Steven P.},
  title     = {Generative Adversarial Networks for the Creation of Realistic Artificial Brain Magnetic Resonance Images},
  series = {Tomography},
  volume    = {4},
  journal   = {Tomography},
  number    = {4},
  doi       = {10.18383/j.tom.2018.00042},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-172185},
  pages     = {159-163},
  year      = {2018},
  abstract  = {Even as medical data sets become more publicly accessible, most are restricted to specific medical conditions. Thus, data collection for machine learning approaches remains challenging, and synthetic data augmentation, such as generative adversarial networks (GAN), may overcome this hurdle. In the present quality control study, deep convolutional GAN (DCGAN)-based human brain magnetic resonance (MR) images were validated by blinded radiologists. In total, 96 T1-weighted brain images from 30 healthy individuals and 33 patients with cerebrovascular accident were included. A training data set was generated from the T1-weighted images and DCGAN was applied to generate additional artificial brain images. The likelihood that images were DCGAN-created versus acquired was evaluated by 5 radiologists (2 neuroradiologists [NRs], vs 3 non-neuroradiologists [NNRs]) in a binary fashion to identify real vs created images. Images were selected randomly from the data set (variation of created images, 40\%-60\%). None of the investigated images was rated as unknown. Of the created images, the NRs rated 45\% and 71\% as real magnetic resonance imaging images (NNRs, 24\%, 40\%, and 44\%). In contradistinction, 44\% and 70\% of the real images were rated as generated images by NRs (NNRs, 10\%, 17\%, and 27\%). The accuracy for the NRs was 0.55 and 0.30 (NNRs, 0.83, 0.72, and 0.64). DCGAN-created brain MR images are similar enough to acquired MR images so as to be indistinguishable in some cases. Such an artificial intelligence algorithm may contribute to synthetic data augmentation for "data-hungry" technologies, such as supervised machine learning approaches, in various clinical applications.},
  subject      = {Magnetresonanztomografie},
  language  = {en}
}
@article{WernerHiguchiNoseetal.2022,
  author    = {Werner, Rudolf A. and Higuchi, Takahiro and Nose, Naoko and Toriumi, Fujio and Matsusaka, Yohji and Kuji, Ichiei and Kazuhiro, Koshino},
  title     = {Generative adversarial network-created brain SPECTs of cerebral ischemia are indistinguishable to scans from real patients},
  series = {Scientific reports},
  volume    = {12},
  journal   = {Scientific reports},
  doi       = {10.1038/s41598-022-23325-3},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-300757},
  year      = {2022},
  abstract  = {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 (123I-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.},
  language  = {en}
}