研究者基本情報

• https://researchmap.jp/19960118

• https://jglobal.jst.go.jp/detail?JGLOBAL_ID=202601019092121995

• 01041079

• 202601019092121995

研究活動情報

• 2025年06月, 日本メディカルAI学会, 日本メディカルAI学会奨励賞 優秀一般演題賞
  2.5D-SRCNNを用いた低カウントPET画像の画質改善：Total Segmentatorを活用した多臓器における定量性の検証
• 2022年11月, 日本核医学会 核医学理工分科会 核医学画像解析研究会, 第10回 菅野賞
  SRCNN を用いた短時間収集 PET 画像の画質改善
• 2020年12月, 日本核医学会 核医学理工分科会 核医学画像解析研究会, 第4回研究奨励賞
  SRCNNによる超解像：FDG-PET検査への応用と検討

• Development and validation of 3D super-resolution convolutional neural network for 18F-FDG-PET images.
  Hiroki Endo; Kenji Hirata; Keiichi Magota; Takaaki Yoshimura; Chietsugu Katoh; Kohsuke Kudo
  EJNMMI physics, 12, 1, 77, 77, 2025年08月19日, ［国際誌］
  英語, 研究論文（学術雑誌）, BACKGROUND: Positron emission tomography (PET) is a valuable tool for cancer diagnosis but generally has a lower spatial resolution compared to computed tomography (CT) or magnetic resonance imaging (MRI). High-resolution PET scanners that use silicon photomultipliers and time-of-flight measurements are expensive. Therefore, cost-effective software-based super-resolution methods are required. This study proposes a novel approach for enhancing whole-body PET image resolution applying a 2.5-dimensional Super-Resolution Convolutional Neural Network (2.5D-SRCNN) combined with logarithmic transformation preprocessing. This method aims to improve image quality and maintain quantitative accuracy, particularly for standardized uptake value measurements, while addressing the challenges of providing a memory-efficient alternative to full three-dimensional processing and managing the wide dynamic range of tracer uptake in PET images. We analyzed data from 90 patients who underwent whole-body FDG-PET/CT examinations and reconstructed low-resolution slices with a voxel size of 4 × 4 × 4 mm and corresponding high-resolution (HR) slices with a voxel size of 2 × 2 × 2 mm. The proposed 2.5D-SRCNN model, based on the conventional 2D-SRCNN structure, incorporates information from adjacent slices to generate a high-resolution output. Logarithmic transformation of the voxel values was applied to manage the large dynamic range caused by physiological tracer accumulation in the bladder. Performance was assessed using the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The quantitative accuracy of standardized uptake values (SUV) was validated using a phantom study. RESULTS: The results demonstrated that the 2.5D-SRCNN with logarithmic transformation significantly outperformed the conventional 2D-SRCNN in terms of PSNR and SSIM (p < 0.0001). The proposed method also showed an improved depiction of small spheres in the phantom while maintaining the accuracy of the SUV. CONCLUSIONS: Our proposed method for whole-body PET images using a super-resolution model with the 2.5D approach and logarithmic transformation may be effective in generating super-resolution images with a lower spatial error and better quantitative accuracy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40658-025-00791-y.