Satomu Hanamatsu 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（16 件）

• [2026] Automated Quantitative Analysis of Enhancing and Peritumoral Cerebral Blood Volume for Differentiating Glioblastoma From Central Nervous System Lymphoma
  DOI: https://doi.org/10.1097/rct.0000000000001871
• [2025] Fast 3D wheel acquisition versus conventional parallel imaging in cerebral magnetic resonance angiography for the evaluation of intracranial aneurysms
  DOI: https://doi.org/10.1007/s00234-025-03776-x
• [2024] Deep Learning Reconstruction for DWIs by EPI and FASE Sequences for Head and Neck Tumors
  DOI: https://doi.org/10.3390/cancers16091714
• [2024] Diffusion weighted imaging with reverse encoding distortion correction: Improvement of image quality and distortion for accurate ADC evaluation in in vitro and in vivo studies
  DOI: https://doi.org/10.1016/j.ejrad.2024.111289
• [2023] Pulmonary MRI with ultra-short TE using single- and dual-echo methods: comparison of capability for quantitative differentiation of non- or minimally invasive adenocarcinomas from other lung cancers with that of standard-dose thin-section CT
  DOI: https://doi.org/10.1007/s00330-023-10105-4
• [2023] Deep learning reconstruction for brain diffusion-weighted imaging: efficacy for image quality improvement, apparent diffusion coefficient assessment, and intravoxel incoherent motion evaluation in <i>in vitro</i> and <i>in vivo</i> studies
  DOI: https://doi.org/10.4274/dir.2023.232149
• [2023] Three-Dimensional Gradient-Echo–Based Amide Proton Transfer-Weighted Imaging of Brain Tumors: Comparison With Two-Dimensional Spin-Echo–Based Amide Proton Transfer-Weighted Imaging
  DOI: https://doi.org/10.1097/rct.0000000000001432
• [2022] A Bayesian estimation method for cerebral blood flow measurement by area-detector CT perfusion imaging
  DOI: https://doi.org/10.1007/s00234-022-03013-9
• [2022] MR imaging for shoulder diseases: Effect of compressed sensing and deep learning reconstruction on examination time and imaging quality compared with that of parallel imaging
  DOI: https://doi.org/10.1016/j.mri.2022.08.004
• [2022] Comparison of lung CT number and airway dimension evaluation capabilities of ultra-high-resolution CT, using different scan modes and reconstruction methods including deep learning reconstruction, with those of multi-detector CT in a QIBA phantom study
  DOI: https://doi.org/10.1007/s00330-022-08983-1

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