Takeshi Nakaura 研究室

主宰者：Takeshi Nakaura

熊本大学

AI 要約（直近 5 年の研究成果）

本研究室は、医用画像診断技術の革新と臨床応用を軸に、複数の領域で研究を展開しています。 **研究の問い** 主な関心は、医療用CT・MRI検査においてどのように診断精度を高めつつ被ばく線量や検査負担を減らすか、また新しい画像解析手法がどの程度の臨床的価値を持つかという点にあります。特に小児医療や心臓疾患、腫瘍診断など、患者負担や被ばくリスクが重要な領域に焦点が当たっています。 **手法** 研究では、深層学習を用いた画像再構成法、スペクトラルCTなどの先進的な撮像技術、機械学習による画像特徴解析（ラジオミクス）、そして大規模言語モデルを活用した医学知識の処理など、多岐にわたるアプローチを採用しています。また、実際の患者データを用いた後ろ向き研究や、シミュレーション・ファントム実験による基礎検証も並行して行われています。 **主要な発見** 複数の研究から、深層学習による画像再構成が従来法と比べて低線量条件下でも画質を保持できることが示されています。また、スペクトラルCTから得られる定量的情報（拡張細胞外容積分画など）は臓器機能評価や疾患鑑別に有用であること、そして機械学習モデルが腫瘍の化学療法応答や血管浸潤の予測において臨床的有用性を有することが報告されています。

※ 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。

外部リンク

研究成果（100 件）

[2026] Contemporary strategies for dose optimization in pediatric congenital heart disease CT: size-specific acquisition and detector-level advances
DOI: https://doi.org/10.1007/s12194-026-01038-w
[2026] Scoping review of regulatory transparency in AI-based radiology software: analysis of PMDA-approved SaMD products
DOI: https://doi.org/10.1007/s11604-025-01942-y
[2026] Image quality comparison between low-dose thin-slice deep-learning reconstruction and standard-dose thick-slice hybrid iterative reconstruction in pediatric abdominal CT
DOI: https://doi.org/10.1016/j.ejrad.2026.112703
[2026] Contrast-specific conversion factor for accurate radiation dose estimation in contrast-enhanced abdominal computed tomography
DOI: https://doi.org/10.25259/jcis_151_2025
[2026] Image Quality Advancements in Low-Dose Pediatric CT Using Super-Resolution Deep-Learning Reconstruction
DOI: https://doi.org/10.1007/s10278-026-01929-9
[2025] Assessment of left ventricular thrombi using cardiac CT: A comparative evaluation of non-contrast, CT-angiography, delayed-enhanced images, and extracellular volume maps
DOI: https://doi.org/10.1016/j.jcct.2025.10.015
[2025] Calculating the heart rate cutoff that avoids motion artifacts with and without beta-blockers during 64-row coronary artery CT angiography
DOI: https://doi.org/10.1016/j.radi.2025.102950
[2025] Super-resolution deep-learning reconstruction with 1024 matrix improves CT image quality for pancreatic ductal adenocarcinoma assessment
DOI: https://doi.org/10.1016/j.ejrad.2025.111953
[2025] Performance of State-of-the-Art Multimodal Large Language Models on an Image-Rich Radiology Board Examination: Comparison to Human Examinees
DOI: https://doi.org/10.1016/j.acra.2025.10.051
[2025] Large Language Model Cost and Performance: A Comprehensive Analysis in the Context of the Japan Radiology Board Examination
DOI: https://doi.org/10.1097/rct.0000000000001807

続きを表示（残り 90 件）

[2025] Performance of multimodal large language models in the Japanese surgical specialist examination
DOI: https://doi.org/10.1186/s12909-025-07938-6
[2025] Clarifications on Methods and Findings: Response to the Letter Regarding “Evaluating the Performance of Reasoning Large Language Models on Japanese Radiology Board Examination Questions”
DOI: https://doi.org/10.1016/j.acra.2025.08.059
[2025] Intra-axial primary brain tumor differentiation: comparing large language models on structured MRI reports vs. radiologists on images
DOI: https://doi.org/10.1007/s00330-025-11924-3
[2025] Dual-layer spectral detector CT for improved assessment of right adrenal vein in patients with primary aldosteronism
DOI: https://doi.org/10.1007/s00261-025-05165-7
[2025] Impact of super-resolution deep learning-based reconstruction for hippocampal MRI: A volunteer and phantom study
DOI: https://doi.org/10.1016/j.ejrad.2025.112289
[2025] Radiation dose in 1–60-month children undergoing 64-detector cardiac CT angiography: ECG-gated versus non-gated scans
DOI: https://doi.org/10.1016/j.radi.2025.102985
[2025] Evaluating the Performance of Reasoning Large Language Models on Japanese Radiology Board Examination Questions
DOI: https://doi.org/10.1016/j.acra.2025.04.060
[2025] Imaging features of wild-type transthyretin amyloid cardiomyopathy in gated and non-gated contrast-enhanced CT
DOI: https://doi.org/10.1007/s00330-025-11631-z
[2025] Use of Electron Density Maps and Fused Images in Dual-Energy Cardiac Computed Tomography to Facilitate Detection of Late Iodine Enhancement
DOI: https://doi.org/10.1253/circrep.cr-24-0115
[2025] Non-invasive Imaging Findings of Wild-type Transthyretin Amyloid Cardiomyopathy in Women: A Retrospective Study
DOI: https://doi.org/10.1016/j.acra.2025.03.009
[2025] MRI-Extracellular Volume Fraction Versus Histological Amyloid Load in Cardiac Amyloidosis: The Importance of T2 Mapping
DOI: https://doi.org/10.1161/circimaging.124.017427
[2025] Usefulness of 3T split acquisition fast spin-echo diffusion-weighted imaging for differentiating pituitary abscess from other sellar cystic lesions: a preliminary study
DOI: https://doi.org/10.1007/s00234-024-03531-8
[2025] Liver function estimation using multiphase hepatic CT: diagnostic performance of iodine-uptake and volumetric parameters
DOI: https://doi.org/10.1007/s00330-025-11497-1
[2025] Basic verification of myocardial extracellular volume quantification by prototype photon-counting detector computed tomography: A phantom study
DOI: https://doi.org/10.25259/jcis_157_2024
[2025] Radiomics-based Machine Learning Approach to Predict Chemotherapy Responses in Colorectal Liver Metastases
DOI: https://doi.org/10.23922/jarc.2024-077
[2025] Serial assessment of coronary artery inflammation using cardiac CT in anthracycline chemotherapy for breast cancer
DOI: https://doi.org/10.1007/s00330-025-11347-0
[2024] Super-resolution deep learning reconstruction approach for enhanced visualization in lumbar spine MR bone imaging
DOI: https://doi.org/10.1016/j.ejrad.2024.111587
[2024] Super-resolution deep-learning reconstruction for cardiac CT: impact of radiation dose and focal spot size on task-based image quality
DOI: https://doi.org/10.1007/s13246-024-01423-y
[2024] A Novel Approach to Detecting Contrast Extravasation in Computed Tomography: Evaluating the Injection Pressure-to-Injection Rate Ratio
DOI: https://doi.org/10.1097/rct.0000000000001614
[2024] Low tube voltage and deep-learning reconstruction for reducing radiation and contrast medium doses in thin-slice abdominal CT: a prospective clinical trial
DOI: https://doi.org/10.1007/s00330-024-10793-6
[2024] Improving Visualization of In-stent Lumen Using Prototype Photon-counting Detector Computed Tomography with High-resolution Plaque Kernel
DOI: https://doi.org/10.4103/jmp.jmp_163_23
[2024] Optimizing High-Resolution MR Angiography: The Synergistic Effects of 3D Wheel Sampling and Deep Learning–Based Reconstruction
DOI: https://doi.org/10.1097/rct.0000000000001590
[2024] Real‐world efficacy of radiomics versus clinical predictors for microvascular invasion in patients with hepatocellular carcinoma: Large cohort study
DOI: https://doi.org/10.1111/hepr.14149
[2024] Enhancing thin slice 3D T2-weighted prostate MRI with super-resolution deep learning reconstruction: Impact on image quality and PI-RADS assessment
DOI: https://doi.org/10.1016/j.mri.2024.110308
[2024] Effect of deep learning-based reconstruction on high-resolution three-dimensional T2-weighted fast asymmetric spin-echo imaging in the preoperative evaluation of cerebellopontine angle tumors
DOI: https://doi.org/10.1007/s00234-024-03328-9
[2024] The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI
DOI: https://doi.org/10.1007/s11604-024-01552-0
[2024] Using Patient-Specific Contrast Enhancement Optimizer Simulation Software During the Transcatheter Aortic Valve Implantation—Computed Tomography Angiography in Patients With Aortic Stenosis
DOI: https://doi.org/10.1097/rct.0000000000001603
[2024] CT-derived extracellular volume fraction in aortic stenosis, cardiac amyloidosis, and dual pathology
DOI: https://doi.org/10.1093/ehjci/jeae320
[2024] Diagnostic performance of hepatic CT and chemical-shift MRI to discriminate lipid-poor adrenal adenomas from hepatocellular carcinoma metastases
DOI: https://doi.org/10.1007/s00261-024-04228-5
[2024] Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations: a real-world clinical analysis
DOI: https://doi.org/10.1007/s00330-024-11212-6
[2024] Simultaneous 3D whole-heart T2 mapping and coronary angiography (3D-T2A)
DOI: https://doi.org/10.58530/2024/1817
[2024] Usefulness of Super-Resolution Deep Learning-Based Reconstruction on High-Resolution 3D T2WI for Evaluation of the Internal Auditory Canal
DOI: https://doi.org/10.58530/2024/1968
[2024] Prediction of endovascular leaks after thoracic endovascular aneurysm repair though machine learning applied to pre-procedural computed tomography angiographs
DOI: https://doi.org/10.1007/s13246-024-01429-6
[2024] Deep Learning Reconstruction for Enhanced Resolution and Image Quality in Breath-Hold MRCP: A Preliminary Study
DOI: https://doi.org/10.1097/rct.0000000000001680
[2024] Machine Learning for Evaluating Vulnerable Plaque on Coronary Computed Tomography Using Spectral Imaging
DOI: https://doi.org/10.1253/circrep.cr-24-0086
[2024] Performance of Multimodal Large Language Models in Japanese Diagnostic Radiology Board Examinations (2021-2023)
DOI: https://doi.org/10.1016/j.acra.2024.10.035
[2024] Effectiveness of the air-gap method for reducing radiation dose in neonate CT examinations
DOI: https://doi.org/10.1007/s12194-024-00855-1
[2024] The Evolution and Clinical Impact of Deep Learning Technologies in Breast MRI
DOI: https://doi.org/10.2463/mrms.rev.2024-0056
[2024] Diagnostic performance of unenhanced electrocardiogram-gated cardiac CT for detecting myocardial edema
DOI: https://doi.org/10.1097/md.0000000000038295
[2024] Clinical Impact of Radiologist’s Alert System on Patient Care for High-risk Incidental CT Findings: A Machine Learning-Based Risk Factor Analysis
DOI: https://doi.org/10.1016/j.acra.2024.09.034
[2024] Advances in spatial resolution and radiation dose reduction using super-resolution deep learning–based reconstruction for abdominal computed tomography: A phantom study
DOI: https://doi.org/10.1016/j.acra.2024.09.012
[2024] Non-contrast spectral CT vs chemical-shift MRI in discriminating lipid-poor adrenal lesions
DOI: https://doi.org/10.1007/s00330-024-10929-8
[2023] Intracranial aneurysms treated with stent-assisted coil embolization: evaluation with four-dimensional ultrashort-TE MR angiography
DOI: https://doi.org/10.1007/s00330-023-09755-1
[2023] Deep learning–based reconstruction and 3D hybrid profile order technique for MRCP at 3T: evaluation of image quality and acquisition time
DOI: https://doi.org/10.1007/s00330-023-09703-z
[2023] Utilidad de la línea orbitomeatal superior sin incluir los cristalinos en la exploración con diferentes voltajes del tubo en la TC craneal pediátrica
DOI: https://doi.org/10.1016/j.rx.2023.02.006
[2023] Usefulness of large beam-shaping filters at different tube voltages of newborn chest CT
DOI: https://doi.org/10.1007/s13246-023-01217-8
[2023] Exploring the impact of super-resolution deep learning on MR angiography image quality
DOI: https://doi.org/10.1007/s00234-023-03271-1
[2023] Posteroanterior Scout View of Pediatric Patients Using Computed Tomography During Different Tube Voltages: Validation Using Pediatric Anthropomorphic Phantoms
DOI: https://doi.org/10.1007/s42399-023-01624-8
[2023] Cardiac MRI T1 and T2 Mapping as a Quantitative Imaging Biomarker in Transthyretin Amyloid Cardiomyopathy
DOI: https://doi.org/10.1016/j.acra.2023.08.045
[2023] Preliminary assessment of automated radiology report generation with generative pre-trained transformers: comparing results to radiologist-generated reports
DOI: https://doi.org/10.1007/s11604-023-01487-y
[2023] Feasibility study of super-resolution deep learning-based reconstruction using k-space data in brain diffusion-weighted images
DOI: https://doi.org/10.1007/s00234-023-03212-y
[2023] Myocardial extracellular volume quantification in cardiac amyloidosis: a comparative study between cardiac computed tomography and magnetic resonance imaging
DOI: https://doi.org/10.1007/s00330-023-10129-w
[2023] Spin-Echo Revisited: Improved Visualization of Accelerated T2W Spin-Echo Spiral Imaging with Compressed Sensing with SENSE (CS-Spiral)
DOI: https://doi.org/10.58530/2022/4698
[2023] The Feasibility of Using a Deep Learning–Based Model to Determine Cardiac Computed Tomographic Contrast Dose
DOI: https://doi.org/10.1097/rct.0000000000001532
[2023] Optimization of wrist tendon detection in virtual monochromatic images using dual energy-computed tomography
DOI: https://doi.org/10.1007/s11604-023-01467-2
[2023] Improving image quality with super-resolution deep-learning-based reconstruction in coronary CT angiography
DOI: https://doi.org/10.1007/s00330-023-09888-3
[2023] Accelerating TOF-MRA: The impact of the combined use of compressed sensitivity encoding and spiral imaging
DOI: https://doi.org/10.1016/j.mri.2023.06.019
[2023] Coronary Stent Evaluation by CTA: Image Quality Comparison Between Super-Resolution Deep Learning Reconstruction and Other Reconstruction Algorithms
DOI: https://doi.org/10.2214/ajr.23.29506
[2023] Usefulness of electrocardiogram mA modulation during the electrocardiogram-gated CT scan in paediatrics with high heart rate for different helical pitch: a phantom-based assessment study
DOI: https://doi.org/10.1093/rpd/ncad180
[2023] Noise power spectrum properties of deep learning–based reconstruction and iterative reconstruction algorithms: Phantom and clinical study
DOI: https://doi.org/10.1016/j.ejrad.2023.110914
[2023] Usefulness of the superior orbitomeatal line without the lens included in the scan range at different tube voltage during pediatric head CT
DOI: https://doi.org/10.1016/j.rxeng.2023.05.003
[2023] Cardiac MRI–derived Extracellular Volume Fraction versus Myocardium-to-Lumen R1 Ratio at Postcontrast T1 Mapping for Detecting Cardiac Amyloidosis
DOI: https://doi.org/10.1148/ryct.220327
[2023] Deep learning-based reconstruction can improve the image quality of low radiation dose head CT
DOI: https://doi.org/10.1007/s00330-023-09559-3
[2023] Effect of patient characteristics on vessel enhancement on arterio-venous fistula CT angiography in a retrospective cohort study
DOI: https://doi.org/10.1097/md.0000000000033328
[2023] Can Machine Learning Identify the Intravenous Contrast Dose and Injection Rate Needed for Optimal Enhancement on Dynamic Liver Computed Tomography?
DOI: https://doi.org/10.1097/rct.0000000000001468
[2023] RADIATION DOSE REDUCTION AT LOW TUBE VOLTAGE WITH CORONARY ARTERY BYPASS GRAFT COMPUTED TOMOGRAPHY ANGIOGRAPHY BASED ON THE CONTRAST NOISE RATIO INDEX
DOI: https://doi.org/10.1093/rpd/ncad049
[2023] Combination Use of Compressed Sensing and Deep Learning for Shoulder Magnetic Resonance Imaging With Various Sequences
DOI: https://doi.org/10.1097/rct.0000000000001418
[2022] Machine learning to predict left ventricular reverse remodeling by guideline-directed medical therapy by utilizing texture feature of extracellular volume fraction in patients with non-ischemic dilated cardiomyopathy
DOI: https://doi.org/10.1007/s00380-022-02167-z
[2022] Practical Preventive Strategies for Extravasation of Contrast Media During CT: What the Radiology Team Should Do
DOI: https://doi.org/10.1016/j.acra.2022.01.007
[2022] Myocardial extracellular volume quantification by cardiac CT in pulmonary hypertension: Comparison with cardiac MRI
DOI: https://doi.org/10.1016/j.ejrad.2022.110386
[2022] Influence of contrast enhancement at the contrast injection location for the arm or leg in neonatal and infant patients during cardiac computed tomography
DOI: https://doi.org/10.1016/j.rxeng.2021.10.001
[2022] CT Extracellular Volume Fraction versus Myocardium-to-Lumen Signal Ratio for Cardiac Amyloidosis
DOI: https://doi.org/10.1148/radiol.220542
[2022] COMPARISON OF PEDIATRIC LENS DOSE MEASUREMENTS BETWEEN AXIAL SCAN MODE WITHOUT ACTIVE COLLIMATOR AND HELICAL SCAN MODE WITH ACTIVE COLLIMATOR BY USING A 64 DETECTOR-ROW COMPUTED TOMOGRAPHY SCANNER
DOI: https://doi.org/10.1093/rpd/ncac196
[2022] Echo planar imaging with compressed sensitivity encoding (EPICS): Usefulness for head and neck diffusion-weighted MRI
DOI: https://doi.org/10.1016/j.ejrad.2022.110489
[2022] Hybrid deep-learning-based denoising method for compressed sensing in pituitary MRI: comparison with the conventional wavelet-based denoising method
DOI: https://doi.org/10.1007/s00330-022-08552-6
[2022] Comparison of the effects of varying tube voltage and iodinated concentration on increasing the iodinated radiation dose in computed tomography
DOI: https://doi.org/10.1016/j.ejmp.2022.01.007
[2022] Usefulness of the patient-specific contrast enhancement optimizer simulation software during the whole-body computed tomography angiography
DOI: https://doi.org/10.1007/s00380-022-02024-z
[2022] Radiation dose reduction method combining the ECG-Edit function and high helical pitch in retrospectively-gated CT angiography
DOI: https://doi.org/10.1016/j.radi.2022.03.004
[2022] Radiation dose optimization potential of deep learning-based reconstruction for multiphase hepatic CT: A clinical and phantom study
DOI: https://doi.org/10.1016/j.ejrad.2022.110280
[2022] Detection of ventricular thrombi via electron density imaging in non-contrast spectral computed tomography performed to exclude pneumonia: a case report
DOI: https://doi.org/10.1093/ehjcr/ytac148
[2022] Spectral imaging with dual-layer spectral detector computed tomography for the detection of perfusion defects in acute coronary syndrome
DOI: https://doi.org/10.1007/s00380-021-02019-2
[2022] Whole Tumor Radiomics Analysis for Risk Factors Associated With Rapid Growth of Vestibular Schwannoma in Contrast-Enhanced T1-Weighted Images
DOI: https://doi.org/10.1016/j.wneu.2022.07.058
[2022] Evaluating of the Quality of Hepatic Diffusion Weighted Imaging Using Multiband Imaging With Variable-Rate Selective Excitation
DOI: https://doi.org/10.1097/rct.0000000000001357
[2022] Applying patient characteristics, stent-graft selection, and pre-operative computed tomographic angiography data to a machine learning algorithm: Is endoleak prediction possible?
DOI: https://doi.org/10.1016/j.radi.2022.06.004
[2022] Lung-Optimized Deep-Learning-Based Reconstruction for Ultralow-Dose CT
DOI: https://doi.org/10.1016/j.acra.2022.04.025
[2022] Performance of Machine Learning Methods Based on Multi-Sequence Textural Parameters Using Magnetic Resonance Imaging and Clinical Information to Differentiate Malignant and Benign Soft Tissue Tumors
DOI: https://doi.org/10.1016/j.acra.2022.04.007
[2022] Evaluation of coronavirus-2019-related arterial thrombosis in noncontrast spectral computed tomography with electron density imaging
DOI: https://doi.org/10.1016/j.radcr.2022.09.085
[2021] Non-contrast mDixon MR angiography of the neck
DOI: https://doi.org/10.1097/md.0000000000028351
[2021] Can myocardial susceptibility quantification be an imaging biomarker for cardiac amyloidosis?
DOI: https://doi.org/10.1007/s11604-021-01228-z
[2021] Non-contrast mDixon MR angiography of the neck: Comparison with time-of-flight MR angiography in normal subjects.
DOI: https://doi.org/10.1097/md.0000000000028351

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AI 要約（直近 5 年の研究成果）

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