Updated on 2024/02/02

写真a

 
MATSUURA Ryutaro
 
Organization
Faculty of Health Sciences Assistant Professor
Position
Assistant Professor
External link

Degree

  • 保健学博士 ( 岡山大学 )

  • 保健学修士 ( 岡山大学 )

  • 保健学学士 ( 岡山大学 )

Research Interests

  • 放射線技術学

  • Radiological technology

Research Areas

  • Life Science / Radiological sciences

Education

  • 岡山大学大学院   保健学研究科   放射線技術科学分野

    2015.4 - 2018.3

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    Country: Japan

    Notes: 博士後期課程

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  • 岡山大学大学院   保健学研究科   放射線技術科学分野

    2009.4 - 2011.3

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    Country: Japan

    Notes: 博士前期課程

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  • Okayama University   医学部   保健学科

    2002.4 - 2006.3

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    Country: Japan

    Notes: 放射線技術科学専攻

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Research History

  • - Assistant Professor,Graduate School of Health Sciences,Okayama University

    2015

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  • - 岡山大学保健学研究科 助教

    2015

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Professional Memberships

 

Papers

  • Proposal for diagnosis using FLAIR image aimed for pediatric MELAS with recurrent stroke-like episodes on MRI system cannot take ASL imaging

    Makoto Shimada, Tae Ikeda, Ryohei Fukui, Katsuhiro Kida, Ryutaro Matsuura, Takuya Akagawa, Sachiko Goto

    Egyptian Pediatric Association Gazette   71 ( 1 )   2023.11

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    Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media LLC  

    Abstract

    Background

    Arterial spin-labeling (ASL) imaging is currently the most useful method for diagnosing mitochondrial encephalomyopathy, lactic acidosis, and stroke-like attack syndrome (MELAS). However, ASL is often an optional feature of standard MRI systems. Therefore, not all MRI systems can perform ASL imaging. In contrast, fluid-attenuated inversion recovery (FLAIR) imaging is one of the common sequences in brain MRI because FLAIR imaging can be performed regardless of the specifications of the equipment. This study aimed to compare the diagnostic performance of quantitative analysis of signal intensity obtained from fluid-attenuated inversion recovery (FLAIR) images with ASL images for MELAS with recurrent stroke-like episodes (SLEs). A total of 68 cases with normal magnetic resonance imaging findings and 25 cases diagnosed MELAS with recurrent SLEs were included. We evaluated the frontal lobe and cuneus as target areas and compared the regional cerebral blood flow (rCBF) values obtained from ASL images with the normalized signal intensity (nSI) obtained from FLAIR images.

    Results

    The sensitivity and specificity for diagnosing MELAS from linear discriminant analysis (LDA) obtained from the rCBF values were 0.84 and 0.941, respectively, and those of nSI were 0.8 and 0.897, respectively. The area under the ROC curves (AUC) calculated from the receiver operating characteristic (ROC) curve analysis using rCBF values and nSI were 0.889 and 0.804, respectively.

    Conclusion

    Quantitative analysis using the signal intensity of the FLAIR image could have a diagnostic performance equivalent to that of rCBF values obtained from ASL images.

    DOI: 10.1186/s43054-023-00232-4

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    Other Link: https://link.springer.com/article/10.1186/s43054-023-00232-4/fulltext.html

  • CT透視ガイド下針穿刺ロボットの自動化のための医師の手技中における針の軌道修正の調査

    亀川 哲志, 高山 和真, 松野 隆幸, 平木 隆夫, 櫻井 淳, 小牧 稔幸, 松浦 龍太郎, 佐々木 崇了, 五福 明夫

    日本コンピュータ外科学会誌   22 ( 1 )   14 - 20   2020.1

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    Language:Japanese   Publisher:(一社)日本コンピュータ外科学会  

    著者らは、術者の被ばく量低減を目的として、遠隔操作型のCT透視ガイド下針穿刺ロボット(Zerobot)の研究開発を開始している。今回、動物実験のCT画像をもとに術者である医師がロボットを遠隔操作してブタの体内にある標的に対して穿刺を行う場合に、針と標的との位置関係によってどのように針の軌道修正の作業を行うのか調査した。Zerobotを用いて、X軸、Y軸、Z軸の三つの直動軸で三次元方向の並進運動を行い、針の位置を決定した。A軸、B軸の二つの回転軸で回転運動を行い、針の姿勢を決定した。調査に使用したデータはブタを用いた穿刺精度試験で得られたものであった。ロボットを医師が遠隔操作することによる穿刺において、針が標的に近づくにつれ修正回数は指数的に増加していることが分かった。なお、残りの穿刺深さが約10mmとなるのをピークに、それ以降はA軸とB軸の修正回数は減少し、一方で刺し直し回数が増加している様子が観察された。合計60回の穿刺のうち、A軸の修正は110回、B軸の修正は66回、刺し直しは37回であった。自動穿刺シミュレーションの結果、穿刺経路と針との距離と角度がゼロに収束しており、針先を三次元的な穿刺経路に追従させることができることを確認した。針が標的に近づくにしたがって穿刺速度が減速していることが示された。

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    Other Link: https://search.jamas.or.jp/index.php?module=Default&action=Link&pub_year=2020&ichushi_jid=J03729&link_issn=&doc_id=20200207210002&doc_link_id=10.5759%2Fjscas.22.14&url=https%3A%2F%2Fdoi.org%2F10.5759%2Fjscas.22.14&type=J-STAGE&icon=https%3A%2F%2Fjk04.jamas.or.jp%2Ficon%2F00007_2.gif

  • Measurement of Needle Trajectory Correction in Doctor’s Procedure for Automation of CT-guided Needle Insertion Robot

    Tetsushi Kamegawa, Kazuma Takayama, Takayuki Matsuno, Takao Hiraki, Jun Sakurai, Toshiyuki Komaki, Ryutaro Matsuura, Takanori Sasaki, Akio Gofuku

    Journal of Japan Society of Computer Aided Surgery   22 ( 1 )   14 - 20   2020

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    Publishing type:Research paper (scientific journal)   Publisher:The Japan Society of Computer Aided Surgery  

    DOI: 10.5759/jscas.22.14

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  • Robotic CT-guided out-of-plane needle insertion: comparison of angle accuracy with manual insertion in phantom and measurement of distance accuracy in animals. Reviewed

    Komaki T, Hiraki T, Kamegawa T, Matsuno T, Sakurai J, Matsuura R, Yamaguchi T, Sasaki T, Mitsuhashi T, Okamoto S, Uka M, Matsui Y, Iguchi T, Gobara H, Kanazawa S

    European radiology   30 ( 3 )   1342 - 1349   2019.11

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:SPRINGER  

    Objectives To evaluate the accuracy of robotic CT-guided out-of-plane needle insertion in phantom and animal experiments. Methods A robotic system (Zerobot), developed at our institution, was used for needle insertion. In the phantom experiment, 12 robotic needle insertions into a phantom at various angles in the XY and YZ planes were performed, and the same insertions were manually performed freehand, as well as guided by a smartphone application (SmartPuncture). Angle errors were compared between the robotic and smartphone-guided manual insertions using Student's t test. In the animal experiment, 6 robotic out-of-plane needle insertions toward targets of 1.0 mm in diameter placed in the kidneys and hip muscles of swine were performed, each with and without adjustment of needle orientation based on reconstructed CT images during insertion. Distance accuracy was calculated as the distance between the needle tip and the target center. Results In the phantom experiment, the mean angle errors of the robotic, freehand manual, and smartphone-guided manual insertions were 0.4 degrees, 7.0 degrees, and 3.7 degrees in the XY plane and 0.6 degrees, 6.3 degrees, and 0.6 degrees in the YZ plane, respectively. Robotic insertions in the XY plane were significantly (p < 0.001) more accurate than smartphone-guided insertions. In the animal experiment, the overall mean distance accuracy of robotic insertions with and without adjustment of needle orientation was 2.5 mm and 5.0 mm, respectively. Conclusion Robotic CT-guided out-of-plane needle insertions were more accurate than smartphone-guided manual insertions in the phantom and were also accurate in the in vivo procedure, particularly with adjustment during insertion.

    DOI: 10.1007/s00330-019-06477-1

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  • A survey of nurses’ and physicians’ safety awareness in CT/MRI examinations Reviewed

    Manae Watanabe, Ryutaro Matsuura, Shihoko Namba

    Okayama Igakkai Zasshi (Journal of Okayama Medical Association)   130 ( 3 )   161 - 166   2018.12

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    Publishing type:Research paper (scientific journal)   Publisher:Okayama Medical Association  

    DOI: 10.4044/joma.130.161

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  • Robotic Insertion of Various Ablation Needles Under Computed Tomography Guidance: Accuracy in Animal Experiments. Reviewed International journal

    Hiraki T, Matsuno T, Kamegawa T, Komaki T, Sakurai J, Matsuura R, Yamaguchi T, Sasaki T, Iguchi T, Matsui Y, Gobara H, Kanazawa S

    European journal of radiology   105   162 - 167   2018.8

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    Language:English   Publishing type:Research paper (scientific journal)  

    OBJECTIVE: To evaluate the accuracy of robotic insertion of various ablation needles at various locations under computed tomography (CT) guidance in swine. MATERIALS AND METHODS: The robot was used for CT-guided insertion of four ablation needles, namely a single internally cooled radiofrequency ablation (RFA) needle (Cool-tip), a multi-tined expandable RFA needle (LeVeen), a cryoablation needle (IceRod), and an internally cooled microwave ablation needle (Emprint). One author remotely operated the robot with the operation interface in order to orient and insert the needles under CT guidance. Five insertions of each type of ablation needle towards 1.0-mm targets in the liver, kidney, lung, and hip muscle were attempted on the plane of an axial CT image in six swine. Accuracy of needle insertion was evaluated as the three-dimensional length between the target centre and needle tip. The accuracy of needle insertion was compared according to the type of needle used and the location using one-way analysis of variance. RESULTS: The overall mean accuracy of all four needles in all four locations was 2.8 mm. The mean accuracy of insertion of the Cool-tip needle, LeVeen needle, IceRod needle, and Emprint needle was 2.8 mm, 3.1 mm, 2.5 mm, and 2.7 mm, respectively. The mean accuracy of insertion into the liver, kidney, lung, and hip muscle was 2.7 mm, 2.9 mm, 2.9 mm, and 2.5 mm, respectively. There was no significant difference in insertion accuracy among the needles (P = .38) or the locations (P = .53). CONCLUSION: Robotic insertion of various ablation needles under CT guidance was accurate regardless of type of needle or location in swine.

    DOI: 10.1016/j.ejrad.2018.06.006

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  • Image Evaluation of Free-breathing Navigator Echo and Triggered Cardiac-gated Delayed Myocardial Enhancement Magnetic Resonance Imaging in Sedated Infants Reviewed

    Ryutaro Matsuura, Sachiko Goto, Shuhei Sato, Noriaki Akagi, Seiji Tahara

    2018.6

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Books

  • 臨床心臓CT学 : 基礎と実践マネージメント

    小山, 靖史, 鈴木, 諭貴( Role: Contributor)

    中外医学社  2016.10  ( ISBN:9784498136465

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    Total pages:xiii, 643p   Language:Japanese

    CiNii Books

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MISC

  • シリーズ:医療現場での放射線管理 第1 回 医用X 線CT 装置使用に対する作業 環境管理と医療被ばく・職業被ばくの管理

    松浦 龍太郎

    2020.6

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  • ロボット下IVRの現状と将来展望 経皮的冠動脈インターベンション(PCI)支援ロボットの現状と将来展望

    松浦 龍太郎, 渡邊 彰吾, 廣畑 聡

    2019.4

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  • Robotically Driven CT-guided Needle Insertion: Preliminary Results in Phantom and Animal Experiments

    Takao Hiraki, Tetsushi Kamegawa, Takayuki Matsuno, Jun Sakurai, Yasuzo Kirita, Ryutaro Matsuura, Takuya Yamaguchi, Takanori Sasaki, Toshiharu Mitsuhashi, Toshiyuki Komaki, Yoshihisa Masaoka, Yusuke Matsui, Hiroyasu Fujiwara, Toshihiro Iguchi, Hideo Gobara, Susumu Kanazawa

    RADIOLOGY   285 ( 2 )   454 - 461   2017.11

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    Language:English   Publisher:RADIOLOGICAL SOC NORTH AMERICA  

    Purpose: To evaluate the accuracy of the remote-controlled robotic computed tomography (CT)-guided needle insertion in phantom and animal experiments.
    Materials and Methods: In a phantom experiment, 18 robotic and manual insertions each were performed with 19-gauge needles by using CT fluoroscopic guidance for the evaluation of the equivalence of accuracy of insertion between the two groups with a 1.0-mm margin. Needle insertion time, CT fluoroscopy time, and radiation exposure were compared by using the Student t test. The animal experiments were approved by the institutional animal care and use committee. In the animal experiment, five robotic insertions each were attempted toward targets in the liver, kidneys, lungs, and hip muscle of three swine by using 19-gauge or 17-gauge needles and by using conventional CT guidance. The feasibility, safety, and accuracy of robotic insertion were evaluated.
    Results: The mean accuracies of robotic and manual insertion in phantoms were 1.6 and 1.4 mm, respectively. The 95% confidence interval of the mean difference was 20.3 to 0.6 mm. There were no significant differences in needle insertion time, CT fluoroscopy time, or radiation exposure to the phantom between the two methods. Effective dose to the physician during robotic insertion was always 0 mSv, while that during manual insertion was 5.7 mSv on average (P&lt;.001). Robotic insertion was feasible in the animals, with an overall mean accuracy of 3.2 mm and three minor procedure-related complications.
    Conclusion: Robotic insertion exhibited equivalent accuracy as manual insertion in phantoms, without radiation exposure to the physician. It was also found to be accurate in an in vivo procedure in animals. (C) RSNA, 2017

    DOI: 10.1148/radiol.2017162856

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Presentations

  • Evaluation of Motion Artifact in Readout Segmented DWI with multiple b-value

    Ryutaro Matsuura, Sachiko Goto, Yumi Orihara, Yuka Tanaka, Yoshiharu Azuma, Toshi Matsushita, Seiji Tahara

    SMRT 26th ANNUAL MEETING  2017.4 

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  • 臨床実習前技能習得度演習におけるタグ機能付き映像アノテーションシステムの導入

    松浦龍太郎, 後藤佐知子, 丸山敏則, 井内洋介, 東義晴

    第12回中四国放射線医療技術フォーラム  2016.11 

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  • Combination ECG Triggered and Navigator Echo Triggered for Sedated Infants

    Ryutaro Matsuura, Sachiko Goto, Tomoki Ii, Yoshiharu Azuma, Noriaki Akagi, Seiji Tahara, Shuhei Sato

    SMRT 25th ANNUAL MEETING  2016.5 

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  • Image Quality and Dose Performance in Pediatric Heart Computed Tomography Diagnosis Using a High Pitch Double Spiral Scan Technique

    Ryutarou Matsuura, Sachiko Goto, Yoshiharu Azuma, Noriaki Akagi, Tomoki Ii, Seiji Tahara, Shuhei Sato

    RSNA2015  2015.11 

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  • Clinical Validation of Using Free Breathing Navigator Echo and Triggered Cardiac Gated Delayed Myocardial Enhancement MR Imaging in Sedated Infants

    Ryutaro Matsuura, Y Omura, N Akagi, S Goto, Y Azuma, S Sato, S Tahara

    RSNA2014  2014.12.4 

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  • An Attempt to Bring Forward the Start Time of Scan of Delayed Myocardial Enhancement

    Ryutaro Matsuura, Y Omura, S Goto, Y Azuma, N Shimada, S Sato, S Tahara

    RSNA2014  2014.12.1 

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  • An Attempt to Bring Forward the Start Time of Scan of Delayed Myocardial Enhancement

    SMRT 23rd ANNUAL MEETING  2014 

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Research Projects

  • ロボットを用いた画像ガイド下骨穿刺の実現:自動穿刺アルゴリズムの構築

    Grant number:22H03028  2022.04 - 2025.03

    日本学術振興会  科学研究費助成事業 基盤研究(B)  基盤研究(B)

    平木 隆夫, 馬越 紀行, 櫻井 淳, 松宮 潔, 松野 隆幸, 松井 裕輔, 亀川 哲志, 松浦 龍太郎

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    Grant amount:\17030000 ( Direct expense: \13100000 、 Indirect expense:\3930000 )

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Class subject in charge

  • Medical Image Analysis and Diagnosis (2023academic year) Prophase  - 木7

  • Medical Safety Management science (2023academic year) Third semester  - 金5~6

  • Risk Management (2023academic year) Late  - 木7

  • Experiment of Medical Image Informatics (2023academic year) Second semester  - 月1~6

  • Experiments in Radiochemistry (2023academic year) 3rd and 4th semester  - 木4~6

  • Experiment of Radiographic Imaging (2023academic year) Second semester  - 月5~7,水5~7

  • Experiment of Radiation Equipment Engineering (2023academic year) 3rd and 4th semester  - [第3学期]火5~8,金5~8, [第4学期]火5~8,水5~8

  • Human Macroscopic Anatomy of Radiology (2023academic year) 1st semester  - 木1~2

  • Experiments in Radiation Detection and Measurement (2023academic year) 3rd and 4th semester  - 月5~7

  • Nuclear Medicine Technology I (2023academic year) Second semester  - 月2~3

  • Human Macroscopic Anatomy (2023academic year) 1st semester  - 木1~2

  • Clinical Practice (Special) (2023academic year) Fourth semester  - 月4~6,水4~6

  • 臨床実習Ⅱ (2023academic year) 1~3学期

  • Practice in Clincal RadiologyI (2023academic year) special  - その他

  • Clinical Skills Exercise (2023academic year) Second semester  - 金7~8

  • Radiographic Technology Practices (2023academic year) 3rd and 4th semester  - [第3学期]火1~4,金1~4, [第4学期]火1~4,水1~4

  • Radiographic Technology IV (2023academic year) Third semester  - 水7~8

  • Medical Image Analysis and Diagnosis (2022academic year) Prophase  - 木7

  • Medical Safety Management science (2022academic year) Third semester  - 金5~6

  • Risk Management (2022academic year) Late  - 木7

  • Experiment of Medical Image Informatics (2022academic year) Second semester  - 月1~6

  • Experiments in Radiochemistry (2022academic year) 3rd and 4th semester  - 木5~7

  • Experiment of Radiographic Imaging (2022academic year) Second semester  - 月5~7,水5~7

  • Experiment of Radiation Equipment Engineering (2022academic year) 3rd and 4th semester  - [第3学期]火5~8,金5~8, [第4学期]火5~8,水5~8

  • Experiments in Radiation Detection and Measurement (2022academic year) 3rd and 4th semester  - 月5~7

  • Nuclear Medicine Technology I (2022academic year) Second semester  - 月2~3

  • Human Macroscopic Anatomy (2022academic year) 1st semester  - 木1~2

  • Clinical Practice (Special) (2022academic year) Fourth semester  - 月4~6,水4~6

  • 臨床実習Ⅱ (2022academic year) 1~3学期

  • Practice in Clincal RadiologyI (2022academic year) special  - その他

  • Simulation-based Clinical-Skills Examination (2022academic year) Second semester  - 金7~8

  • Radiographic Technology Practices (2022academic year) 3rd and 4th semester  - [第3学期]火1~4,金1~4, [第4学期]火1~4,水1~4

  • Radiographic Technology IV (2022academic year) Third semester  - 水3~4

  • CT撮影技術学 (2022academic year) 第3学期

  • チーム医療演習 (2021academic year) 1・2学期

  • Medical Image Analysis and Diagnosis (2021academic year) Prophase  - 木7

  • Topics in Medical Imaging (2021academic year) Prophase  - 水7

  • 医療安全管理学 (2021academic year) 第3学期

  • 卒業研究 (2021academic year) 1~3学期

  • Risk Management (2021academic year) Late  - 木7

  • Experiment of Medical Image Informatics (2021academic year) Second semester  - 月1~6

  • Experiments in Radiochemistry (2021academic year) 3rd and 4th semester  - 木5~7

  • Experiment of Radiographic Imaging (2021academic year) Second semester  - 月5~7,水5~7

  • Experiments in Radiation Detection and Measurement (2021academic year) 3rd and 4th semester  - 月5~7

  • Nuclear Medicine Technology I (2021academic year) Second semester  - 月2~3

  • 画像解剖学 (2021academic year) 第1学期

  • Clinical Practice (Special) (2021academic year) Fourth semester  - 月4~6,水4~6

  • 臨床実習Ⅱ (2021academic year) 1~3学期

  • 臨床実習I (2021academic year) 3・4学期

  • Simulation-based Clinical-Skills Examination (2021academic year) Summer concentration  - その他

  • Radiographic Technology Practices (2021academic year) 3rd and 4th semester  - [第3学期]火1~4,金1~4, [第4学期]火1~4,水1~4

  • Radiographic Technology IV (2021academic year) Third semester  - 水3~4

  • CT撮影技術学 (2021academic year) 第3学期

  • Medical Image Analysis and Diagnosis (2020academic year) Prophase  - 木7

  • Topics in Medical Imaging (2020academic year) Prophase  - 水7

  • Medical Safety Management science (2020academic year) Third semester  - 金5,金6

  • Risk Management (2020academic year) Late  - 木7

  • Experiments in Radiochemistry (2020academic year) 3rd and 4th semester  - 木5,木6,木7

  • Experiment of Radiographic Imaging (2020academic year) Second semester  - 月5,月6,月7,水5,水6,水7

  • Experiments in Radiation Detection and Measurement (2020academic year) 3rd and 4th semester  - 月5,月6,月7

  • Nuclear Medicine Technology I (2020academic year) Second semester  - 月2,月3

  • Simulation-based Clinical-Skills Examination (2020academic year) Summer concentration  - その他

  • Radiographic Technology I (2020academic year) Second semester  - 木1,木2

  • Radiographic Technology III (2020academic year) Second semester  - 金3,金4

  • Radiographic Technology IV (2020academic year) Third semester  - 水3,水4

  • Magnetic Resonance Imaging (2020academic year) Third semester  - 水7,水8

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Social Activities

  • 岡山県診療放射線技師会 出前講義

    Role(s):Lecturer

    岡山県 診療放射線技師会  倉敷中央高校  2023.12.15

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    Type:Visiting lecture

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  • 令和4年度 高校生向け公開講座 放射線画像解剖学~ラジエーションハウスへの誘い~

    Role(s):Lecturer

    岡山大学  2022.8.11

  • 岡山大学医学部保健学科放射線技術科学専攻オープンキャンパス

    Role(s):Organizing member

    岡山大学  2022.8.6

  • 高校生のための大学講座

    Role(s):Lecturer

    岡山大学  2021.11.20

  • 倉敷中央高校 出前講義

    Role(s):Lecturer

    岡山県 放射線技師会  2020.7 - 2020.8