Masami Goto 研究室

AI 要約（直近 5 年の研究成果）

※ AI（Claude）が、公開されている論文要旨から研究の問い・手法・主要な発見を事実情報として抽出・再構成して自動生成しています。誤りを含む可能性があるため、正確性は研究室公式情報でご確認ください。

研究成果（10 件）

• [2026] Utility of Regional Cerebral Blood Flow <scp>SPECT</scp> and <scp>MRI</scp> for Predicting Amyloid Deposition in Real‐World Clinical Settings
  DOI: https://doi.org/10.1111/psyg.70158
• [2024] Deep Learning-based Hierarchical Brain Segmentation with Preliminary Analysis of the Repeatability and Reproducibility
  DOI: https://doi.org/10.2463/mrms.mp.2023-0124
• [2023] Accuracy of skull stripping in a single-contrast convolutional neural network model using eight-contrast magnetic resonance images
  DOI: https://doi.org/10.1007/s12194-023-00728-z
• [2023] Evaluation of motion artefact reduction depending on the artefacts’ directions in head MRI using conditional generative adversarial networks
  DOI: https://doi.org/10.1038/s41598-023-35794-1
• [2022] Analysis of synthetic magnetic resonance images by multi-channel segmentation increases accuracy of volumetry in the putamen and decreases mis-segmentation in the dural sinuses
  DOI: https://doi.org/10.1177/02841851221089835
• [2022] A cycle generative adversarial network for improving the quality of four-dimensional cone-beam computed tomography images
  DOI: https://doi.org/10.1186/s13014-022-02042-1
• [2021] Effect of hybrid of compressed sensing and parallel imaging on the quantitative values measured by 3D quantitative synthetic MRI: A phantom study
  DOI: https://doi.org/10.1016/j.mri.2021.01.001
• [2021] Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography
  DOI: https://doi.org/10.1186/s42492-021-00087-9
• [2021] Using modulated and smoothed data improves detectability of volume difference in group comparison, but reduces accuracy with atlas-based volumetry using Statistical Parametric Mapping 12 software
  DOI: https://doi.org/10.1177/02841851211032442
• [2021] Comparison of Brain Volume Measurements Made with 0.3- and 3-T MR Imaging
  DOI: https://doi.org/10.2463/mrms.tn.2020-0034

科研費（0 件）

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所属学会・役職（0 件）

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