Yoshinori Funama 研究室

主宰者：Yoshinori Funama

熊本大学

AI 要約（直近 5 年の研究成果）

本研究室は、医療用コンピュータ断層撮影（CT）画像の質向上と患者被曝線量の低減を両立させる技術開発を主要なテーマとしています。深層学習を応用した画像再構成法や新型の光子計数型検出器といった最新の撮像技術を組み合わせることで、より少ない放射線量やより低い造影剤投与量でも診断に適した高品質な画像を得るための方法を研究しています。特に小児患者への応用を意識した研究が多く、年齢や体格に応じた最適な撮影パラメータの設定方法も開発しています。心臓や血管疾患の診断を目的としたCT検査を対象に、造影剤の投与量や注入速度を患者個別の特性に基づいて最適化するシステムの構築にも取り組んでいます。機械学習を用いて患者の体格や心機能といった個人差を反映した造影パラメータを自動的に決定する手法を開発し、従来の体重ベースの方法と比べてより効率的な造影が可能なことを示しています。さらに、画像解析の自動化・支援を目指した研究も展開しており、大規模言語モデルを医学教育や臨床診断支援に応用する可能性も検証しています。これらの研究を通じて、より安全でかつ診断精度が高いCT検査の実現を目指しています。

※ 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（77 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] 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] 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] 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
[2025] Image Quality Variation with Gantry Rotation Time and Reconstruction Algorithm in Ultra-high-resolution CT
DOI: https://doi.org/10.1016/j.acra.2025.09.027
[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] 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] Image quality with reduced radiation dose and contrast medium of cardiac computed tomography in infants with congenital heart disease using 70 KVP and DEEP-learning reconstruction with 256-detector CT
DOI: https://doi.org/10.1093/rpd/ncaf152

続きを表示（残り 67 件）

[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] Usefulness of contrast injection methods with the implanted port system during dynamic hepatic computed tomography scan
DOI: https://doi.org/10.1016/j.radi.2025.102976
[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] 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
[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] Diagnostic performance of unenhanced electrocardiogram-gated cardiac CT for detecting myocardial edema
DOI: https://doi.org/10.1097/md.0000000000038295
[2024] Iodine contrast volume reduction in preoperative transcatheter aortic valve implantation computed tomography: Comparison with 64- and 256-multidetector row computed tomography
DOI: https://doi.org/10.1016/j.radi.2023.12.017
[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] Evaluation of four computed tomography reconstruction algorithms using a coronary artery phantom
DOI: https://doi.org/10.21037/qims-23-1204
[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] 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] 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] 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] 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] 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] 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
[2023] Effect of iodine concentration and body size on iodine subtraction in virtual non-contrast imaging: A phantom study
DOI: https://doi.org/10.1016/j.radi.2023.03.003
[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] 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 computed tomography angiography with and without bolus tracking methods in infants with congenital heart disease
DOI: https://doi.org/10.1093/rpd/ncad295
[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] 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] 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] 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] 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] Image-quality characteristics in the longitudinal direction from different image-reconstruction algorithms during single-rotation volume acquisition on head computed tomography: A phantom study
DOI: https://doi.org/10.1177/20584601231168986
[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] 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] 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] Comparison of Lower Extremity Computed Tomography Angiography by Using Different Subtraction Techniques
DOI: https://doi.org/10.6009/jjrt.2023-1211
[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] 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] 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] Dose Reduction and Low-Contrast Detectability Using Iterative CBCT Reconstruction Algorithm for Radiotherapy
DOI: https://doi.org/10.1177/15330338211067312
[2022] Coronary Artery Visualization by Using the 64-row MDCT in Pediatric Patients
DOI: https://doi.org/10.6009/jjrt.2022-1194
[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] 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] 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] 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] Iterative reconstruction with multifrequency signal recognition technology to improve low-contrast detectability: A phantom study
DOI: https://doi.org/10.1177/20584601221109919
[2021] Myocardial Tissue Characterization by Combining Extracellular Volume Fraction and T2 Mapping
DOI: https://doi.org/10.1016/j.jcmg.2021.02.029
[2021] USE OF VACUUM MATTRESSES CAN REDUCE THE ABSORBED DOSE DURING PEDIATRIC CT
DOI: https://doi.org/10.1093/rpd/ncab102
[2021] Diagnostic performance of computed tomography digital subtraction angiography of the lower extremities during haemodialysis in patients with suspected peripheral artery disease
DOI: https://doi.org/10.1016/j.radi.2021.02.007
[2021] Machine learning to identify lymph node metastasis from thyroid cancer in patients undergoing contrast-enhanced CT studies
DOI: https://doi.org/10.1016/j.radi.2021.03.001
[2021] Image quality improvement with deep learning‐based reconstruction on abdominal ultrahigh‐resolution CT: A phantom study
DOI: https://doi.org/10.1002/acm2.13318
[2021] Effects of tube voltage and iodine contrast medium on radiation dose of whole-body CT
DOI: https://doi.org/10.1177/02841851211001539
[2021] Accuracy of dose calculation on iterative CBCT for head and neck radiotherapy
DOI: https://doi.org/10.1016/j.ejmp.2021.05.027
[2021] Conditional generative adversarial networks to generate pseudo low monoenergetic CT image from a single-tube voltage CT scanner
DOI: https://doi.org/10.1016/j.ejmp.2021.02.015
[2021] The combined application of the contrast-to-noise index and 80 kVp for cardiac CTA scanning before atrial fibrillation ablation reduces radiation dose exposure
DOI: https://doi.org/10.1016/j.radi.2021.01.003
[2021] Effect of image quality on myocardial extracellular volume quantification using cardiac computed tomography: a phantom study
DOI: https://doi.org/10.1177/0284185120986938
[2021] Assessment of cardiac implantable electric device lead perforation using a metal artifact reduction algorithm in cardiac computed tomography
DOI: https://doi.org/10.1016/j.ejrad.2021.109530
[2021] Enhancement rate of venous phase to portal venous phase computed tomography and its correlation with ultrasound elastography determination of liver fibrosis
DOI: https://doi.org/10.1016/j.radi.2021.10.008
[2021] Efficacy of the spiral flow generating extended tube during paediatric CCTA
DOI: https://doi.org/10.1016/j.radi.2021.10.007
[2021] Deep Learning–based Reconstruction for Lower-Dose Pediatric CT: Technical Principles, Image Characteristics, and Clinical Implementations
DOI: https://doi.org/10.1148/rg.2021210105
[2021] Influencia del realce de contraste al inyectar un medio de contraste en el brazo o la pierna en pacientes neonatos y lactantes durante la angiografía por cardiotomografía
DOI: https://doi.org/10.1016/j.rx.2021.07.009
[2021] Deep learning with convolutional neural network for estimation of the characterisation of coronary plaques: Validation using IB-IVUS
DOI: https://doi.org/10.1016/j.radi.2021.07.024
[2021] Comparison of visibility of in-stent restenosis between conventional- and ultra-high spatial resolution computed tomography: coronary arterial phantom study
DOI: https://doi.org/10.1007/s11604-021-01200-x
[2021] Effect of injection duration on contrast enhancement during cardiac computed tomography angiography in newborns and infants
DOI: https://doi.org/10.1016/j.radi.2021.11.002
[2021] Virtual magnetic resonance lumbar spine images generated from computed tomography images using conditional generative adversarial networks
DOI: https://doi.org/10.1016/j.radi.2021.10.006

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AI 要約（直近 5 年の研究成果）

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研究成果（77 件）

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