Takahiro Ueda 研究室

主宰者：Takahiro Ueda

藤田医科大学・Fujita Health University Hospital

AI 要約（直近 5 年の研究成果）

Takahiro Ueda研究室では、医療診断画像の質向上と定量的な機能評価を主な研究テーマとしています。特に肺や腹部臓器の磁気共鳴画像（MRI）およびコンピュータ断層撮影（CT）を対象に、画像の鮮明さやノイズを改善する手法の開発と検証を行っています。深層学習を用いた画像再構成、圧縮センシング、デュアルエネルギーCTなど、複数の先進的な技術を組み合わせることで、より正確で低被曝の診断が可能な撮像方法を探索しています。研究は主にファントム実験と臨床患者データの両方を用いた検証を行う点に特徴があります。肺がん、乳がん、腎臓腫瘍など多くの疾患に対し、MRIやCTの画像から腫瘍の大きさや性質を正確に測定・評価する手法を開発しています。また機械学習を用いた画像解析により、結合組織病に伴う肺の病変の重症度や治療効果を定量的に評価する研究も進めています。さらに研究室は、従来のX線検査では見落とされやすかった肺の機能的変化（換気や血流など）をMRIやCTで可視化する方法に取り組んでいます。これらの研究を通じ、より高精度で患者への負担が少ない医療診断システムの構築を目指しています。

※ 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。

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研究成果（35 件）

[2026] Comparing the Perfusion and Functional Assessment Capabilities of Electrocardiography- and Photoplethysmography-monitored Phase-resolved Functional Lung and Dynamic Contrast-enhanced Perfusion MR Imaging
DOI: https://doi.org/10.2463/mrms.mp.2025-0201
[2026] Deep Learning Reconstruction Specialized for Inner Ear: Improving Image Quality and Anatomical Structure Visualization as Compared with Conventional Hybrid-Type Iterative Reconstruction on High-Definition CT
DOI: https://doi.org/10.3390/diagnostics16121756
[2026] Comparison of biomechanical assessment between upright and supine inspiratory-expiratory area-detector CT in progressive pulmonary fibrosis
DOI: https://doi.org/10.1186/s41747-026-00698-y
[2026] Reverse Encoding Distortion Correction of Female Pelvic Diffusion-weighted Imaging on a 1.5-T MR System: Influence on Image Quality and Apparent Diffusion Coefficient Measurements for Both <i>In Vitro</i> and <i>In Vivo</i> Studies
DOI: https://doi.org/10.2463/mrms.mp.2025-0202
[2025] High-resolution deep learning reconstruction for coronary CTA: compared efficacy of stenosis evaluation with other methods at in vitro and in vivo studies
DOI: https://doi.org/10.1007/s00330-025-11376-9
[2025] Dynamic perfusion area-detector CT in non-small cell lung cancer with progressive fibrosing interstitial lung disease
DOI: https://doi.org/10.1007/s00330-025-11653-7
[2024] Deep Learning Reconstruction for DWIs by EPI and FASE Sequences for Head and Neck Tumors
DOI: https://doi.org/10.3390/cancers16091714
[2024] Chest Magnetic Resonance Imaging
DOI: https://doi.org/10.1016/j.ccm.2024.02.017
[2024] Efficacy of compressed sensing and deep learning reconstruction for adult female pelvic MRI at 1.5 T
DOI: https://doi.org/10.1186/s41747-024-00506-5
[2024] Compressed sensing with deep learning reconstruction: Improving capability of gadolinium-EOB-enhanced 3D T1WI
DOI: https://doi.org/10.1016/j.mri.2024.01.015

続きを表示（残り 25 件）

[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
[2024] Evaluating the Efficacy of Deep Learning Reconstruction in Reducing Radiation Dose for Computer-Aided Volumetry for Liver Tumor: A Phantom Study
DOI: https://doi.org/10.1097/rct.0000000000001657
[2024] Mesothelioma Mimicking a Mediastinal Tumor in the Prevascular Compartment
DOI: https://doi.org/10.1097/rti.0000000000000809
[2024] Proton Density Fat Fraction Quantification (PD-FFQ): Capability for hematopoietic ability assessment and aplastic anemaia diagnosis of adults
DOI: https://doi.org/10.1016/j.mri.2024.110240
[2023] Area-Detector Computed Tomography for Pulmonary Functional Imaging
DOI: https://doi.org/10.3390/diagnostics13152518
[2023] Lung Magnetic Resonance Imaging
DOI: https://doi.org/10.1097/rli.0000000000001017
[2023] Deep Learning Reconstruction to Improve the Quality of MR Imaging: Evaluating the Best Sequence for T-category Assessment in Non-small Cell Lung Cancer Patients
DOI: https://doi.org/10.2463/mrms.mp.2023-0068
[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] Effectiveness of deep learning reconstruction on standard to ultra-low-dose high-definition chest CT images
DOI: https://doi.org/10.1007/s11604-023-01470-7
[2023] Capability for dose reduction while maintaining nodule detection: Comparison of silver and copper X-ray spectrum modulation filters for chest CT using a phantom study with different reconstruction methods
DOI: https://doi.org/10.1016/j.ejrad.2023.110969
[2023] Advances for Pulmonary Functional Imaging: Dual-Energy Computed Tomography for Pulmonary Functional Imaging
DOI: https://doi.org/10.3390/diagnostics13132295
[2023] Chemical Exchange Saturation Transfer <scp>MRI</scp>: Capability for Predicting Therapeutic Effect of Chemoradiotherapy on Non‐Small Cell Lung Cancer Patients
DOI: https://doi.org/10.1002/jmri.28691
[2023] Reverse encoding distortion correction for diffusion-weighted MRI: Efficacy for improving image quality and ADC evaluation for differentiating malignant from benign areas in suspected prostatic cancer patients
DOI: https://doi.org/10.1016/j.ejrad.2023.110764
[2023] State of the Art MR Imaging for Lung Cancer TNM Stage Evaluation
DOI: https://doi.org/10.3390/cancers15030950
[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
[2022] Computed <scp>DWI</scp> MRI Results in Superior Capability for <scp>N‐Stage</scp> Assessment of <scp>Non‐Small</scp> Cell Lung Cancer Than That of Actual <scp>DWI</scp>, <scp>STIR</scp> Imaging, and <scp>FDG‐PET</scp>/<scp>CT</scp>
DOI: https://doi.org/10.1002/jmri.28288
[2022] Comparison of utility of deep learning reconstruction on 3D MRCPs obtained with three different k-space data acquisitions in patients with IPMN
DOI: https://doi.org/10.1007/s00330-022-08877-2
[2022] Deep Learning Reconstruction of Diffusion-weighted MRI Improves Image Quality for Prostatic Imaging
DOI: https://doi.org/10.1148/radiol.204097
[2021] Efficacy of Ultrashort Echo Time Pulmonary MRI for Lung Nodule Detection and Lung-RADS Classification
DOI: https://doi.org/10.1148/radiol.211254
[2021] Machine learning for lung texture analysis on thin-section CT: Capability for assessments of disease severity and therapeutic effect for connective tissue disease patients in comparison with expert panel evaluations
DOI: https://doi.org/10.1177/02841851211044973
[2021] Interstitial lung disease associated with adjuvant and neoadjuvant chemotherapy in early breast cancer
DOI: https://doi.org/10.1186/s12957-021-02289-0
[2021] State-of-the-art MR Imaging for Thoracic Diseases
DOI: https://doi.org/10.2463/mrms.rev.2020-0184
[2021] Successful Transcatheter Arterial Embolization to Control Intratumoral Hemorrhage in Clear-Cell Sarcoma of the Kidney
DOI: https://doi.org/10.1272/jnms.jnms.2022_89-108
[2021] Overview of MRI for pulmonary functional imaging
DOI: https://doi.org/10.1259/bjr.20201053

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AI 要約（直近 5 年の研究成果）

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