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<p>White matter hyperintensities (WMH) may decrease cortical glucose metabolism and appear hypodense on <sup>18</sup>F-fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT), respectively. Currently, T2-weighted fluid-attenuated inversion recovery (FLAIR) images on magnetic resonance imaging (MRI) are considered as a sequence of choice to evaluate WMH. This study aimed to use a generative adversarial network (GAN) to predict FLAIR MRI images from <sup>18</sup>F-FDG PET/CT. From 2017 to 2019, we selected 167 patients who had MRI and FDG PET/CT scans within 6&#xa0;months. We categorized WMH into three groups using the Fazekas scale and trained a GAN model to predict MR FLAIR images from PET and CT data (pix2pix-PT), or only CT data (pix2pix-CT). We compared these predicted images with actual MR FLAIR images, then performed WMH segmentation and volume estimation, assessing their agreement. To predict ground-truth FLAIR images, the pix2pix-PT method demonstrated superior performance compared with pix2pix-CT, as evidenced by the lower NMAE and higher PSNR in all groups. Integrating these findings with the segmentation results, the performance of the pix2pix-PT model in WMH segmentation was differential across groups. Notably, the pix2pix-PT model accurately segmented WMH lesions over 0.3 cm<sup>2</sup> without false positives or negatives and maintained a DSC above 0.7 for lesions over 2 cm<sup>2</sup>. For WMH volume estimation, the pix2pix-PT method showed excellent correlations in Group 2 (<i>r</i> = 0.903) and Group 3 (<i>r</i> = 0.984), and moderate in Group 1 (<i>r</i> = 0.780). In this study, the prediction of T2-weighted FLAIR MR images using the GAN model was better achieved when both FDG PET and CT data were provided to the model, compared to CT data alone. Predicted T2-FLAIR images derived from our model could aid in selecting patients who need MRI to assess WMH and related vascular pathology.</p>

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Prediction of FLAIR MRI from 18F-FDG PET/CT for the Evaluation of White Matter Hyperintensity Using Generative Adversarial Network

  • 期刊论文

  • 发表日期：

    2026年4月27日

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  • Kyeong Taek Oh,
  • Sangwon Lee,
  • Dongwoo Kim,
  • Kyobin Choo,
  • Seungbeom Seo,
  • Yeo Jun Yoon,
  • YoungJoo Park,
  • Young-Gun Lee,
  • Sun Kook Yoo,
  • Mijin Yun

摘要

White matter hyperintensities (WMH) may decrease cortical glucose metabolism and appear hypodense on 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT), respectively. Currently, T2-weighted fluid-attenuated inversion recovery (FLAIR) images on magnetic resonance imaging (MRI) are considered as a sequence of choice to evaluate WMH. This study aimed to use a generative adversarial network (GAN) to predict FLAIR MRI images from 18F-FDG PET/CT. From 2017 to 2019, we selected 167 patients who had MRI and FDG PET/CT scans within 6 months. We categorized WMH into three groups using the Fazekas scale and trained a GAN model to predict MR FLAIR images from PET and CT data (pix2pix-PT), or only CT data (pix2pix-CT). We compared these predicted images with actual MR FLAIR images, then performed WMH segmentation and volume estimation, assessing their agreement. To predict ground-truth FLAIR images, the pix2pix-PT method demonstrated superior performance compared with pix2pix-CT, as evidenced by the lower NMAE and higher PSNR in all groups. Integrating these findings with the segmentation results, the performance of the pix2pix-PT model in WMH segmentation was differential across groups. Notably, the pix2pix-PT model accurately segmented WMH lesions over 0.3 cm2 without false positives or negatives and maintained a DSC above 0.7 for lesions over 2 cm2. For WMH volume estimation, the pix2pix-PT method showed excellent correlations in Group 2 (r = 0.903) and Group 3 (r = 0.984), and moderate in Group 1 (r = 0.780). In this study, the prediction of T2-weighted FLAIR MR images using the GAN model was better achieved when both FDG PET and CT data were provided to the model, compared to CT data alone. Predicted T2-FLAIR images derived from our model could aid in selecting patients who need MRI to assess WMH and related vascular pathology.