Koichiro Yasaka 研究室

主宰者：Koichiro Yasaka

東京大学・University of Tokyo Hospital

AI 要約（直近 5 年の研究成果）

本研究室は、医療画像診断の質向上と診療支援の自動化を目指して、二つの主要な研究領域に取り組んでいます。第一の領域は、深層学習を活用した医療画像の画質改善に関する研究です。特に、超解像度アルゴリズムを組み込んだ深層学習画像再構成法を用いて、CT・MRI検査画像のノイズ低減と空間分解能の向上を実現し、脳動脈瘤や脳梗塞、すい臓病変、脊椎疾患など様々な臓器の病変検出精度を高める取り組みを行っています。また、金属製医療機器が装着された患者の撮影や動きアーティファクトの除去など、臨床的な課題への対応も進めています。第二の領域は、自然言語処理を用いた放射線診断報告書からの情報抽出と診療支援システムの構築です。微調整された大規模言語モデルを活用して、非構造化テキストの診断報告書から膵臓がん・急性脳梗塞・骨転移といった重要な臨床情報を自動抽出したり、CT検査プロトコルの選択支援を行う研究を進めています。深層学習に基づく画像解析と自然言語処理技術を組み合わせることで、放射線科医の診断業務の効率化と精度向上に貢献する応用を目指しています。

※ 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（72 件）

[2026] Image Distortion Correction for Diffusion MR Imaging Using a Transformer-based U-Net
DOI: https://doi.org/10.2463/mrms.mp.2024-0149
[2026] Improved delineation of the cystic artery using super-resolution deep learning reconstruction in contrast-enhanced abdominal computed tomography
DOI: https://doi.org/10.1007/s12194-026-01066-6
[2026] Super-resolution deep learning reconstruction enhances visualization of cerebral aneurysms on magnetic resonance angiography
DOI: https://doi.org/10.1007/s00234-026-03965-2
[2026] Clinical Value of Bronchiectasis and Airspace Enlargement Depicted on Iodine Map in Patients With Interstitial Lung Disease: A Pilot Study
DOI: https://doi.org/10.7759/cureus.102934
[2025] Diagnostic value of a second-generation super-resolution deep learning–based reconstruction combined with a metal artifact reduction algorithm for pelvic CT
DOI: https://doi.org/10.1007/s00256-025-05080-4
[2025] Impact of Super-Resolution Deep Learning Reconstruction on Low-Dose CT in Patients with Central Venous Catheter or Central Venous Port
DOI: https://doi.org/10.1007/s10278-025-01726-w
[2025] Fine-tuned large language model for classifying CT-guided interventional radiology reports
DOI: https://doi.org/10.1177/02841851251349495
[2025] Pituitary neuroendocrine tumor: evaluation with super resolution deep learning reconstruction
DOI: https://doi.org/10.1007/s00234-025-03819-3
[2025] Fine-Tuned Large Language Model for Extracting Pretreatment Pancreatic Cancer According to Computed Tomography Radiology Reports
DOI: https://doi.org/10.1007/s10278-025-01637-w
[2025] Reducing motion artifacts in the aorta: super-resolution deep learning reconstruction with motion reduction algorithm
DOI: https://doi.org/10.1007/s11604-025-01849-8

続きを表示（残り 62 件）

[2025] Deep learning reconstruction enhances image quality in contrast-enhanced CT venography for deep vein thrombosis
DOI: https://doi.org/10.1007/s10140-025-02366-x
[2025] Improved Visualization of Pancreatic Cystic Lesions on Magnetic Resonance Cholangiopancreatography Using Super-Resolution Deep Learning Reconstruction
DOI: https://doi.org/10.1007/s10278-025-01708-y
[2025] Efficacy of a large language model in classifying branch-duct intraductal papillary mucinous neoplasms
DOI: https://doi.org/10.1007/s00261-025-05062-z
[2025] Bronchiectasis and airspace enlargement surrounding the lung nodule in dual-energy CT pulmonary angiography: comparison between iodine map and monochromatic image
DOI: https://doi.org/10.1007/s12194-025-00920-3
[2025] Fine-tuned large Language model for extracting newly identified acute brain infarcts based on computed tomography or magnetic resonance imaging reports
DOI: https://doi.org/10.1007/s10140-025-02354-1
[2025] Super-Resolution Deep Learning Reconstruction for T2*-Weighted Images: Improvement in Microbleed Lesion Detection and Image Quality
DOI: https://doi.org/10.1007/s10278-025-01522-6
[2025] Super-resolution deep learning reconstruction to evaluate lumbar spinal stenosis status on magnetic resonance myelography
DOI: https://doi.org/10.1007/s11604-025-01787-5
[2025] Efficacy of Fine-Tuned Large Language Model in CT Protocol Assignment as Clinical Decision-Supporting System
DOI: https://doi.org/10.1007/s10278-025-01433-6
[2025] Iodine concentration in the lung parenchyma in relation to different inspiratory depths during CT pulmonary angiography
DOI: https://doi.org/10.1093/bjr/tqaf008
[2025] Super-resolution deep learning reconstruction improves brain MRI quality and detection of metastases
DOI: https://doi.org/10.1007/s11604-025-01921-3
[2024] High-angular resolution diffusion imaging generation using 3d u-net
DOI: https://doi.org/10.1007/s00234-024-03282-6
[2024] Fine-Tuned Large Language Model for Extracting Patients on Pretreatment for Lung Cancer from a Picture Archiving and Communication System Based on Radiological Reports
DOI: https://doi.org/10.1007/s10278-024-01186-8
[2024] Classification of Interventional Radiology Reports into Technique Categories with a Fine-Tuned Large Language Model
DOI: https://doi.org/10.1007/s10278-024-01370-w
[2024] Large multimodality model fine-tuned for detecting breast and esophageal carcinomas on CT: a preliminary study
DOI: https://doi.org/10.1007/s11604-024-01718-w
[2024] Deep Learning Reconstruction in Abdominopelvic Contrast-Enhanced CT for The Evaluation of Hemorrhages.
[2024] The Fine-Tuned Large Language Model for Extracting the Progressive Bone Metastasis from Unstructured Radiology Reports
DOI: https://doi.org/10.1007/s10278-024-01242-3
[2024] Effect of deep learning reconstruction on the assessment of pancreatic cystic lesions using computed tomography
DOI: https://doi.org/10.1007/s12194-024-00834-6
[2024] Changes after sterile inflammation caused by trabectedin infusion from central venous port: A case report
DOI: https://doi.org/10.1016/j.radcr.2024.07.047
[2024] Automated classification of brain MRI reports using fine-tuned large language models
DOI: https://doi.org/10.1007/s00234-024-03427-7
[2024] Iterative Motion Correction Technique with Deep Learning Reconstruction for Brain MRI: A Volunteer and Patient Study
DOI: https://doi.org/10.1007/s10278-024-01184-w
[2024] Super-resolution Deep Learning Reconstruction for 3D Brain MR Imaging: Improvement of Cranial Nerve Depiction and Interobserver Agreement in Evaluations of Neurovascular Conflict
DOI: https://doi.org/10.1016/j.acra.2024.06.010
[2024] New liver window width in detecting hepatocellular carcinoma on dynamic contrast-enhanced computed tomography with deep learning reconstruction
DOI: https://doi.org/10.1007/s12194-024-00817-7
[2024] Impact of rapid iodine contrast agent infusion on tracheal diameter and lung volume in CT pulmonary angiography measured with deep learning-based algorithm
DOI: https://doi.org/10.1007/s11604-024-01591-7
[2024] Super-resolution Deep Learning Reconstruction Cervical Spine 1.5T MRI: Improved Interobserver Agreement in Evaluations of Neuroforaminal Stenosis Compared to Conventional Deep Learning Reconstruction
DOI: https://doi.org/10.1007/s10278-024-01112-y
[2024] Transient interruption of contrast on CT pulmonary angiography: effect of mid-inspiratory vs. end-inspiratory respiration command
DOI: https://doi.org/10.1007/s10140-024-02227-z
[2024] Differences in Sterile Inflammation Incidence After Trabectedin Infusion With Two Central Venous Port Systems: A Retrospective Study
DOI: https://doi.org/10.7759/cureus.57507
[2024] Computed tomography-based prediction of pancreatitis following biliary metal stent placement with the convolutional neural network
DOI: https://doi.org/10.1055/a-2298-0147
[2024] Faster acquisition of magnetic resonance imaging sequences of the knee via deep learning reconstruction: a volunteer study
DOI: https://doi.org/10.1016/j.crad.2024.03.002
[2024] Deep learning reconstruction for high-resolution computed tomography images of the temporal bone: comparison with hybrid iterative reconstruction
DOI: https://doi.org/10.1007/s00234-024-03330-1
[2024] Effects of Intravenous Infusion of Iodine Contrast Media on the Tracheal Diameter and Lung Volume Measured with Deep Learning-Based Algorithm
DOI: https://doi.org/10.1007/s10278-024-01071-4
[2024] Deep-Learning Reconstruction of High-Resolution CT Improves Interobserver Agreement for the Evaluation of Pulmonary Fibrosis
DOI: https://doi.org/10.1177/08465371241228468
[2023] Deep learning reconstruction for improving the visualization of acute brain infarct on computed tomography
DOI: https://doi.org/10.1007/s00234-023-03251-5
[2023] Impact of deep learning on radiologists and radiology residents in detecting breast cancer on CT: a cross-vendor test study
DOI: https://doi.org/10.1016/j.crad.2023.09.022
[2023] Detection of Vertebral Mass and Diagnosis of Spinal Cord Compression in Computed Tomography With Deep Learning Reconstruction: Comparison With Hybrid Iterative Reconstruction
DOI: https://doi.org/10.1177/08465371231203508
[2023] Comparison of 1.5 T and 3 T magnetic resonance angiography for detecting cerebral aneurysms using deep learning-based computer-assisted detection software
DOI: https://doi.org/10.1007/s00234-023-03216-8
[2023] Feasibility of an abdominal thin-slice breath-hold single-shot FSE sequence processed using a deep learning-based noise-reduction approach
DOI: https://doi.org/10.58530/2022/4201
[2023] Deep Learning Reconstruction Plus Single-Energy Metal Artifact Reduction for Supra Hyoid Neck CT in Patients With Dental Metals
DOI: https://doi.org/10.1177/08465371231182904
[2023] Deep-learning reconstruction for the evaluation of lumbar spinal stenosis in computed tomography
DOI: https://doi.org/10.1097/md.0000000000033910
[2023] Effect of Deep Learning Reconstruction on Evaluating Cervical Spinal Canal Stenosis With Computed Tomography
DOI: https://doi.org/10.1097/rct.0000000000001490
[2023] Clinical Impact of Deep Learning Reconstruction in MRI
DOI: https://doi.org/10.1148/rg.220133
[2023] Assessing the Effects of Deep Learning Reconstruction on Abdominal CT Without Arm Elevation
DOI: https://doi.org/10.1177/08465371231169672
[2023] Effects of deep learning on radiologists’ and radiology residents’ performance in identifying esophageal cancer on CT
DOI: https://doi.org/10.1259/bjr.20220685
[2023] Comparison of Deep-Learning Image Reconstruction With Hybrid Iterative Reconstruction for Evaluating Lung Nodules With High-Resolution Computed Tomography
DOI: https://doi.org/10.1097/rct.0000000000001460
[2023] Deep learning reconstruction with single-energy metal artifact reduction in pelvic computed tomography for patients with metal hip prostheses
DOI: https://doi.org/10.1007/s11604-023-01402-5
[2023] Improving detection performance of hepatocellular carcinoma and interobserver agreement for liver imaging reporting and data system on CT using deep learning reconstruction
DOI: https://doi.org/10.1007/s00261-023-03834-z
[2023] Acceleration of knee magnetic resonance imaging using a combination of compressed sensing and commercially available deep learning reconstruction: a preliminary study
DOI: https://doi.org/10.1186/s12880-023-00962-2
[2022] Usefulness of deep learning-based noise reduction for 1.5 T MRI brain images
DOI: https://doi.org/10.1016/j.crad.2022.08.127
[2022] Motion correction in MR image for analysis of VSRAD using generative adversarial network
DOI: https://doi.org/10.1371/journal.pone.0274576
[2022] Deep learning reconstruction for the evaluation of neuroforaminal stenosis using 1.5T cervical spine MRI: comparison with 3T MRI without deep learning reconstruction
DOI: https://doi.org/10.1007/s00234-022-03024-6
[2022] Commercially Available Deep-learning-reconstruction of MR Imaging of the Knee at 1.5T Has Higher Image Quality Than Conventionally-reconstructed Imaging at 3T: A Normal Volunteer Study
DOI: https://doi.org/10.2463/mrms.mp.2022-0020
[2022] Feasibility of accelerated whole-body diffusion-weighted imaging using a deep learning-based noise-reduction technique in patients with prostate cancer
DOI: https://doi.org/10.1016/j.mri.2022.06.014
[2022] Clinical feasibility of an abdominal thin-slice breath-hold single-shot fast spin echo sequence processed using a deep learning-based noise-reduction approach
DOI: https://doi.org/10.1016/j.mri.2022.04.005
[2022] Deep learning reconstruction for 1.5 T cervical spine MRI: effect on interobserver agreement in the evaluation of degenerative changes
DOI: https://doi.org/10.1007/s00330-022-08729-z
[2022] Radiomics with 3-dimensional magnetic resonance fingerprinting: influence of dictionary design on repeatability and reproducibility of radiomic features
DOI: https://doi.org/10.1007/s00330-022-08555-3
[2022] Accelerated <scp>Two‐Point</scp> Dixon <scp>MR</scp> Angiography Improves Diagnostic Performance for Cervical Artery Diseases
DOI: https://doi.org/10.1002/jmri.28122
[2021] Impact of deep learning reconstruction on intracranial 1.5 T magnetic resonance angiography
DOI: https://doi.org/10.1007/s11604-021-01225-2
[2021] Breath-hold 3D magnetic resonance cholangiopancreatography at 1.5 T using a deep learning-based noise-reduction approach: Comparison with the conventional respiratory-triggered technique
DOI: https://doi.org/10.1016/j.ejrad.2021.109994
[2021] Effects of Gadolinium Deposition in the Brain on Motor or Behavioral Function: A Mouse Model
DOI: https://doi.org/10.1148/radiol.2021210892
[2021] Rib fracture detection in computed tomography images using deep convolutional neural networks
DOI: https://doi.org/10.1097/md.0000000000026024
[2021] Detectability of pancreatic lesions by low-dose unenhanced computed tomography using iterative reconstruction
DOI: https://doi.org/10.1016/j.ejrad.2021.109776
[2021] Texture Analysis in Brain Tumor MR Imaging
DOI: https://doi.org/10.2463/mrms.rev.2020-0159
[2021] Parkinson’s disease: deep learning with a parameter-weighted structural connectome matrix for diagnosis and neural circuit disorder investigation
DOI: https://doi.org/10.1007/s00234-021-02648-4

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AI 要約（直近 5 年の研究成果）

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研究成果（72 件）

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