{"id":693,"date":"2023-07-01T11:32:06","date_gmt":"2023-07-01T02:32:06","guid":{"rendered":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/?page_id=693"},"modified":"2023-07-13T14:40:19","modified_gmt":"2023-07-13T05:40:19","slug":"achievement-2018","status":"publish","type":"page","link":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/en\/achievement-2018\/","title":{"rendered":"Achievement-2018"},"content":{"rendered":"\n<h2 class=\"wp-block-heading blue-line\">Original Papers<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Chan MK, Lee VW, Kadoya N, Chiang CL, Wong MY, Leung RW, Cheung S, Blanck O, \u201cSingle fraction computed tomography-guided high-dose-rate brachytherapy or stereotactic body radiotherapy for primary and metastatic lung tumors?\u201d, J Contemp Brachytherapy. 2018 Oct;10(5):446-453<\/li>\n\n\n\n<li>Otsuka M, Monzen H, Matsumoto K, Tamura M, Inada M, Kadoya N, Nishimura Y, \u201cEvaluation of lung toxicity risk with computed tomography ventilation image for thoracic cancer patients\u201d, PLoS One. 2018 Oct 3;13(10):e0204721<\/li>\n\n\n\n<li>Kajikawa T, Kadoya N, Ito K, Takayama Y, Chiba T, Tomori S, Takeda K, Jingu K, \u201cAutomated prediction of dosimetric eligibility of patients with prostate cancer undergoing intensity-modulated radiation therapy using a convolutional neural network\u201d, Radiol Phys Technol. 2018 Sep;11(3):320-327<\/li>\n\n\n\n<li>Tomori S, Kadoya N, Takayama Y, Kajikawa T, Shima K, Narazaki K, Jingu K, \u201cA deep learning-based prediction model for gamma evaluation in patient-specific quality assurance\u201d, Med Phys. 2018 Jul 31.<\/li>\n\n\n\n<li>Takagi H, Ota H, Umezawa R, Kimura T, Kadoya N, Higuchi S, Sun W, Nakajima Y, Saito M, Komori Y, Jingu K, Takase K, \u201cLeft Ventricular T1 Mapping during Chemotherapy-Radiation Therapy: Serial Assessment of Participants with Esophageal Cancer\u201d, Radiology. 2018 Nov;289(2):347-354<\/li>\n\n\n\n<li>Kanai T, Kadoya N, Nakajima Y, Miyasaka Y, Ieko Y, Kajikawa T, Ito K, Yamamoto T, Dobashi S, Takeda K, Nemoto K, Jingu K, \u201cEvaluation of functionally weighted dose-volume parameters for thoracic stereotactic ablative radiotherapy (SABR) using CT ventilation\u201d, Phys Med. 2018 May;49:47-51<\/li>\n\n\n\n<li>Ishizawa Y, Dobashi S, Kadoya N, Ito K, Chiba T, Takayama Y, Sato K, Takeda K, \u201cA photon source model based on particle transport in a parameterized accelerator structure for Monte Carlo dose calculations\u201d, Med Phys. 2018 Jul;45(7):2937-2946<\/li>\n\n\n\n<li>Katsuta Y, Kadoya N, Fujita Y, Shimizu E, Majima K, Matsushita H, Takeda K, Jingu K, \u201cLog file-based patient dose calculations of double-arc VMAT for head-and-neck radiotherapy\u201d, Phys Med. 2018 Apr;48:6-10<\/li>\n\n\n\n<li>Kadoya N, Kon Y, Takayama Y, Matsumoto T, Hayashi N, Katsuta Y, Ito K, Chiba T, Dobashi S, Takeda K, Jingu K, \u201cQuantifying the performance of two different types of commercial software programs for 3D patient dose reconstruction for prostate cancer patients: Machine log files vs. machine log files with EPID images\u201d, Phys Med. 2018 Jan;45:170-176<\/li>\n\n\n\n<li>Abe K, Kadoya N, Sato S, Hashimoto S, Nakajima Y, Miyasaka Y, Ito K, Umezawa R, Yamamoto T, Takahashi N, Takeda K, Jingu K, \u201cImpact of a commercially available model-based dose calculation algorithm on treatment planning of high-dose-rate brachytherapy in patients with cervical cancer\u201d, J Radiat Res. 2018 Mar 1;59(2):198-206<\/li>\n\n\n\n<li>Kawamoto T, Nihei K, Nakajima Y, Kito S, Sasai K, Karasawa K, \u201cComparison of xerostomia incidence after three-dimensional conformal radiation therapy and contralateral superficial lobe parotid-sparing intensity-modulated radiotherapy for oropharyngeal and hypopharyngeal cancer.\u201d, Auris Nasus Larynx. 2018 Oct 45(5):1073-1079<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading blue-line\">Domestic Conference<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u7530\u4e2d\u7965\u5e73\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u4e2d\u6839\u548c\u662d\u3001\u68b6\u5ddd\u667a\u535a\u3001\u677e\u7530\u5320\u5e73\u3001\u571f\u6a4b\u5353\u3001\u6b66\u7530\u8ce2\u3001\u795e\u5bae\u5553\u4e00. \u201cCT\u753b\u50cf\u306b\u304a\u3051\u308b\u80ba\u764c\u60a3\u8005\u306e\u4e88\u5f8c\u4e88\u6e2c\u89e3\u6790-\u30db\u30e2\u30ed\u30b8\u30fc\u306b\u3088\u308b\u65b0\u305f\u306a\u6311\u6226\u201d. \u7b2c139\u56de\u65e5\u672c\u533b\u5b66\u653e\u5c04\u7dda\u5b66\u4f1a\u5317\u65e5\u672c\u5730\u65b9\u4f1a. 2018. 10 \u4ed9\u53f0<\/li>\n\n\n\n<li>\u7530\u4e2d\u7965\u5e73\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u4f50\u85e4\u614e\u54c9\u3001\u68b6\u5ddd\u667a\u535a\u3001\u677e\u7530\u5320\u5e73\u3001\u6b66\u7530\u8ce2\u3001\u571f\u6a4b\u5353\u3001\u795e\u5bae\u5553\u4e00. \u201c\u80f8\u90e8\u9818\u57df\u306eCT-based radiomics\u306b\u304a\u3051\u308b\u65bd\u8a2d\u6bce\u306e\u30ed\u30d0\u30b9\u30c8\u306aradiomic\u7279\u5fb4\u91cf\u306e\u65b0\u305f\u306a\u7d5e\u308a\u8fbc\u307f\u6cd5\u306e\u958b\u767a\u201d. \u7b2c46\u56de\u65e5\u672c\u653e\u5c04\u7dda\u6280\u8853\u5b66\u4f1a\u79cb\u5b63\u5b66\u8853\u5927\u4f1a. 2018. 10 \u4ed9\u53f0<\/li>\n\n\n\n<li>\u6839\u672c\u5149\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u68b6\u5ddd\u667a\u535a\u3001\u4e2d\u5cf6\u7950\u4e8c\u6717\u3001\u5bb6\u5b50\u7fa9\u6717\u3001\u6b66\u7530\u8ce2\u3001\u795e\u5bae\u5553\u4e00. \u201c4D-CBCT\u3092\u7528\u3044\u305f\u7d4c\u6642\u7684\u306a\u80ba\u6a5f\u80fd\u5909\u5316\u306b\u57fa\u3065\u304f\u653e\u5c04\u7dda\u6cbb\u7642\u6cd5\u306e\u958b\u767a\u306b\u5411\u3051\u305f\u521d\u671f\u691c\u8a0e\u201d. \u7b2c46\u56de\u65e5\u672c\u653e\u5c04\u7dda\u6280\u8853\u5b66\u4f1a\u79cb\u5b63\u5b66\u8853\u5927\u4f1a. 2018. 10 \u4ed9\u53f0<\/li>\n\n\n\n<li>\u963f\u90e8\u5e78\u592a\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u4e2d\u5cf6\u7950\u4e8c\u6717\u3001\u6a4b\u672c\u614e\u5e73\u3001\u7530\u4e2d \u7965\u5e73\u3001\u5510\u6fa4\u514b\u4e4b\u3001\u795e\u5bae\u5553\u4e00. \u201cMVCT\u3092\u7528\u3044\u305fradiomics\u89e3\u6790\u306b\u5411\u3051\u305f\u30ed\u30d0\u30b9\u30c8\u306a\u7279\u5fb4\u91cf\u306e\u691c\u8a0e: kVCT vs. MVCT\u201d. \u7b2c31\u56de\u65e5\u672c\u653e\u5c04\u7dda\u816b\u760d\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2018.10 \u4eac\u90fd<\/li>\n\n\n\n<li>\u6c60\u7530\u9f8d\u592a\u90ce\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u4f50\u85e4\u6e05\u548c\u3001\u77f3\u6fa4\u5100\u6a39\u3001\u4f0a\u85e4\u8b19\u543e\u3001\u5343\u8449\u8cb4\u4ec1\u3001\u9ad8\u5c71\u4f73\u6a39\u3001\u571f\u6a4b\u5353\u3001\u6b66\u7530\u8ce2\u3001\u795e\u5bae\u5553\u4e00. \u201c\u30d9\u30f3\u30c0\u30fc\u63d0\u4f9b\u30c7\u30fc\u30bf\u3092\u4f7f\u7528\u3057\u305f\u7570\u306a\u308b2\u793e\u306eLinac\u306e3\u53f0\u540c\u6642\u7acb\u3061\u4e0a\u3052\u201d. \u7b2c31\u56de\u65e5\u672c\u653e\u5c04\u7dda\u816b\u760d\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2018.10 \u4eac\u90fd<\/li>\n\n\n\n<li>\u6839\u672c\u5149\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u68b6\u5ddd\u667a\u535a\u3001\u4e2d\u5cf6\u7950\u4e8c\u6717\u3001\u5bb6\u5b50\u7fa9\u6717\u3001\u4f50\u85e4\u6e05\u548c\u3001\u6c60\u7530\u9f8d\u592a\u90ce\u3001\u677e\u7530\u5320\u5e73\u3001\u4f0a\u85e4\u8b19\u543e\u3001\u571f\u6a4b\u5353\u3001\u6b66\u7530\u8ce2\u3001\u795e\u5bae\u5553\u4e00. \u201c4DCBCT\u3092\u7528\u3044\u305f\u7d4c\u6642\u7684\u306a\u80ba\u6a5f\u80fd\u5909\u5316\u306b\u57fa\u3065\u304f\u653e\u5c04\u7dda\u6cbb\u7642\u6cd5\u306e\u958b\u767a\u306b\u5411\u3051\u305f\u521d\u671f\u691c\u8a0e\u201d. \u7b2c31\u56de\u65e5\u672c\u653e\u5c04\u7dda\u816b\u760d\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2018.10 \u4eac\u90fd<\/li>\n\n\n\n<li>\u7530\u4e2d\u7965\u5e73\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u4f50\u85e4\u614e\u54c9\u3001\u68b6\u5ddd\u667a\u535a\u3001\u677e\u7530\u5320\u5e73\u3001\u6b66\u7530\u8ce2\u3001\u571f\u6a4b\u5353\u3001\u795e\u5bae\u5553\u4e00. \u201c\u80f8\u90e8\u9818\u57df\u306eCT-based radiomics\u306b\u304a\u3051\u308b\u65b0\u305f\u306a\u30ed\u30d0\u30b9\u30c8\u306aradiomic\u7279\u5fb4\u91cf\u306e\u7d5e\u308a\u8fbc\u307f\u6cd5\u201d. \u7b2c31\u56de\u65e5\u672c\u653e\u5c04\u7dda\u816b\u760d\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2018.10 \u4eac\u90fd<\/li>\n\n\n\n<li>\u5bae\u5742\u53cb\u4f91\u4e5f\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u4f0a\u85e4\u8b19\u543e\u3001\u6885\u6fa4\u73b2\u3001\u4e45\u4fdd\u5712\u6b63\u6a39\u3001\u5c71\u672c\u8cb4\u4e5f\u3001\u4e2d\u5cf6\u7950\u4e8c\u6717\u3001\u9f4b\u85e4\u6b63\u82f1\u3001\u9ad9\u5c71\u4f73\u6a39\u3001\u6839\u672c\u5efa\u4e8c\u3001\u5ca9\u4e95\u5cb3\u592b\u3001\u795e\u5bae\u5553\u4e00.\u201d\u5b50\u5bae\u9838\u764c\u306b\u5bfe\u3059\u308bCT-based3\u6b21\u5143\u753b\u8a98\u5c0e\u5c0f\u7dda\u6e90\u6cbb\u7642\u4e2d\u306eintra-fractional variation\u304c\u3082\u305f\u3089\u3059\u7dda\u91cf\u5909\u52d5\u306e\u8a55\u4fa1\u201d\u65e5\u672c\u653e\u5c04\u7dda\u816b\u760d\u5b66\u4f1a\u5c0f\u7dda\u6e90\u90e8\u4f1a\u3000\u7b2c20\u56de\u5b66\u8853\u5927\u4f1a. 2018.6. \u3064\u304f\u3070<\/li>\n\n\n\n<li>Tomori S, Kadoya N, Takayama Y, Kajikawa T, Shima K, Narazaki K, Jingu K, \u201cDevelopment of deep learning neural network based prediction of patient-specific QA result\u201d, \u7b2c115\u56de\u65e5\u672c\u533b\u5b66\u7269\u7406\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2018.4 \u6a2a\u6d5c<\/li>\n\n\n\n<li>Sato S, Kadoya N, Takeda K, Kajikawa T, Yamamoto T, Takeda K, Jingu K, \u201cPrediction of cancer prognosis by the CT-based radiomic signature in lung cancer patients with SBRT\u201d, \u7b2c115\u56de\u65e5\u672c\u533b\u5b66\u7269\u7406\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2018.4 \u6a2a\u6d5c<\/li>\n\n\n\n<li>Matsumoto T, Kadoya N, Kon Y, Takayama Y, Sato K, Ito K, Chiba T, Dobashi S, Takeda K, Jingu K, \u201cImpact of rectal gas on the EPID-based in-vivo dosimetry system for IMRT prostate cancer patient\u201d, \u7b2c115\u56de\u65e5\u672c\u533b\u5b66\u7269\u7406\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2018.4 \u6a2a\u6d5c<\/li>\n\n\n\n<li>Matsuda S, Kadoya N, Ikeda R, Kajikawa T, Ito K, Chiba T, Takayama Y, Dobashi S, Takeda K, Jingu K, \u201cEvaluation of DIR accuracy between MR images in prostate cancer patients for MR-guided radiotherapy\u201d, \u7b2c115\u56de\u65e5\u672c\u533b\u5b66\u7269\u7406\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2018.4 \u6a2a\u6d5c<\/li>\n\n\n\n<li>Katsuta Y, Kadoya N, Fujita Y, Shimizu E, Majima K, Matsushita H, Jingu K. \u201c\u60a3\u8005\u306b\u6295\u4e0e\u3055\u308c\u308b\u6cbb\u7642\u30d3\u30fc\u30e0\u3092\u57fa\u306b\u3057\u3066\u4e88\u5f8c\u3092\u9ad8\u7cbe\u5ea6\u306b\u628a\u63e1\u3059\u308b\u624b\u6cd5\u306e\u958b\u767a\u201d, \u7b2c115\u56de\u65e5\u672c\u533b\u5b66\u7269\u7406\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2018.4 \u6a2a\u6d5c<\/li>\n\n\n\n<li>Kajikawa T, Kadoya N, Ito K, Chiba T, Takayama Y, Dobashi S, Takeda K, Jingu K. \u201cAutomated analysis of radiotherapy treatment planning with convolutional neural network for determination of optimal prescribed dose for prostate IMRT patients\u201d, \u7b2c115\u56de\u65e5\u672c\u533b\u5b66\u7269\u7406\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2018.4 \u6a2a\u6d5c<\/li>\n\n\n\n<li>Abe K, Kadoya N, Nemoto H, Sato K, Ito K, Takayama Y, Chiba T, Dobashi S, Takeda K, Jingu K. \u201cEvaluation of 3D-printed patient specific head and neck phantom for IMRT QA using RADModeler\u201d, \u7b2c115\u56de\u65e5\u672c\u533b\u5b66\u7269\u7406\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2018.4 \u6a2a\u6d5c<\/li>\n\n\n\n<li>\u4f50\u85e4\u614e\u54c9\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u6b66\u7530\u4e00\u4e5f\u3001\u68b6\u5ddd\u667a\u535a\u3001\u5c71\u672c\u8cb4\u4e5f\u3001\u6b66\u7530\u8ce2\u3001\u795e\u5bae\u5553\u4e00. \u201c\u80baSBRT\u60a3\u8005\u306b\u304a\u3051\u308bCT-based radiomics\u3092\u7528\u3044\u305f\u4e88\u5f8c\u4e88\u6e2c\u306e\u6709\u7528\u6027\u306e\u691c\u8a0e\u201d. \u7b2c31\u56de\u65e5\u672c\u9ad8\u7cbe\u5ea6\u653e\u5c04\u7dda\u5916\u90e8\u7167\u5c04\u90e8\u4f1a. 2018.2 \u5927\u962a<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading blue-line\">International Conference<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Kajikawa T, Kadoya N, Ito K, Takayama Y, Chiba T, Tomori S, Takeda K, Jingu K, \u201cA deep convolutional neural network approach for IMRT dose distribution prediction in prostate cancer patients\u201d, 18th Asia-Oceania Congress of Medical Physics (AOCMP), 2018. 11 Kuala Lumpur, Malaysia<\/li>\n\n\n\n<li>Nemoto H, Kadoya N, Kajikawa T, Nakajima Y, Ieko Y, Ikeda R, Takeda K, Jingu K, \u201cDevelopment of 4DCBCT-based lung ventilation for adaptive ventilation-guided radiotherapy\u201d, 18th Asia-Oceania Congress of Medical Physics (AOCMP), 2018. 11 Kuala Lumpur, Malaysia<\/li>\n\n\n\n<li>Tanaka S, Kadoya N, Nakane K, Sato S, Kajikawa T, Abe K, Jingu K, \u201cHomology as a novel radiomic feature for prediction of the prognosis of lung cancer based on CT-based radiomics\u201d, 18th Asia-Oceania Congress of Medical Physics (AOCMP), 2018. 11 Kuala Lumpur, Malaysia<\/li>\n\n\n\n<li>Kadoya N, Abe Y, Ito K, Yamamoto T, Chiba T, Takayama Y, Kato K, Kikuchi Y, Jingu K, \u201cDosimetric impact of automated non-coplanar treatment planning using stereotactic radiosurgery for multiple cranial metastases: Comparison between HyperArc and Cyberknife dose distributions\u201d,60th annual meeting of ASTRO, 2018. 10. San Antonio<\/li>\n\n\n\n<li>Tomori S, Kadoya N, Takayama Y, Kajikawa T, Shima K, Narazaki K, Jingu K, \u201cA deep learning-based prediction model for gamma evaluation in patient-specific quality assurance\u201d, 60th annual meeting of AAPM, 2018.7. Nashville<\/li>\n\n\n\n<li>Kajikawa T, Kadoya N, Ito K, Takayama Y, Chiba T, Tomori S, Takeda K, Jingu K, \u201cA deep convolutional neural network approach for IMRT dose distribution prediction in prostate cancer patients\u201d, 60th annual meeting of AAPM, 2018.7. Nashville<\/li>\n\n\n\n<li>Kadoya N, Abe K, Nemoto H, Sato K, Ieko Y, Ito K, Takayama Y, Chiba T, Dobashi S, Takeda K, Jingu K, \u201cDevelopment of novel heterogeneous anthropomorphic head and neck phantom using 3D printer for IMRT patient specific quality assurance\u201d, 60th annual meeting of AAPM, 2018.7. Nashville<\/li>\n\n\n\n<li>Matsumoto T, Kadoya N, Kon Y, Takayama Y, Ito K, Chiba T, Sato K, Dobashi S, Takeda K, Jingu K, \u201cQuantifying the performance of two different types of 3D patient dose reconstruction: machine log-file vs. machine log-file with EPID image\u201d, IUPESM 2018 World Congress on Med Phys Bio Eng, 2018.6. Prague<\/li>\n\n\n\n<li>Matsuda S, Kadoya N, Ikeda R, Kajikawa T, Ito K, Chiba T, Takayama Y, Dobashi S, Takeda K, Jingu K, \u201cDetermination of optimal similarity metric for deformable image registration between T2-weighted MR images at different time-points in prostate cancer patient\u201d, IUPESM 2018 World Congress on Med Phys Bio Eng, 2018.6. Prague<\/li>\n\n\n\n<li>Nakajima Y, Kadoya N, Kanai T, Saito M, Kito S, Karasawa K, Jingu K, \u201cEvaluation of user-guided deformable image registration for thoracic images\u201d, IUPESM 2018 World Congress on Med Phys Bio Eng, 2018.6. Prague<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading blue-line\">Patent<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u89d2\u8c37\u502b\u4e4b\u3001\u4e2d\u6839\u548c\u662d\u3001\u68b6\u5ddd\u667a\u535a\u3001\u7530\u4e2d\u7965\u5e73\u3001\u795e\u5bae\u5553\u4e00\u201c\u4f4d\u76f8\u5e7e\u4f55\u5b66\u3092\u7528\u3044\u305f\u9ad8\u7cbe\u5ea6\u306a\u653e\u5c04\u7dda\u6cbb\u7642\u4e88\u5f8c\u4e88\u60f3\u30b7\u30b9\u30c6\u30e0\u201d \u7279\u9858\uff12\uff10\uff11\uff18\uff0d\uff11\uff18\uff15\uff12\uff12\uff18<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading blue-line\">Invited talk &amp; Symposium<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u89d2\u8c37\u502b\u4e4b. \u201cDIR\u306e\u6982\u8981\u201d. \u65e5\u672c\u533b\u5b66\u7269\u7406\u58eb\u4f1a\u533b\u5b66\u7269\u7406\u58eb\u30bb\u30df\u30ca\u30fcDIR\u5b9f\u6280\u8b1b\u7fd2\u4f1a. 2018.9 \u6771\u4eac<\/li>\n\n\n\n<li>\u89d2\u8c37\u502b\u4e4b. \u201c\u8ee2\u79fb\u6027\u8133\u816b\u760d\u306b\u5bfe\u3059\u308bHyperArc\u306e\u6709\u52b9\u6027\u306e\u691c\u8a0e\u201d. Varian Seminar. 2018.7 \u6771\u4eac<\/li>\n\n\n\n<li>\u89d2\u8c37\u502b\u4e4b. \u201cCT-MRI\u9593\u306eDIR\u7cbe\u5ea6\u8a55\u4fa1\u201d. \u7b2c7\u56de MIM Maestro \u30e6\u30fc\u30b6\u30fc\u30ba\u30df\u30fc\u30c6\u30a3\u30f3\u30b0. 2018.6 \u6771\u4eac<\/li>\n\n\n\n<li>\u89d2\u8c37\u502b\u4e4b. \u201c\u69d8\u3005\u306a\u533b\u7528\u753b\u50cf\u306b\u5bfe\u3059\u308bDIR\u3068\u81ea\u52d5\u8f2a\u90ed\u62bd\u51fa\u201d. \u7b2c31\u56de\u65e5\u672c\u9ad8\u7cbe\u5ea6\u653e\u5c04\u7dda\u5916\u90e8\u7167\u5c04\u90e8\u4f1a. 2018.2 \u5927\u962a<\/li>\n\n\n\n<li>\u89d2\u8c37\u502b\u4e4b. \u201cDIR\u306e\u6982\u8981\u201d. \u65e5\u672c\u533b\u5b66\u7269\u7406\u58eb\u4f1a\u533b\u5b66\u7269\u7406\u58eb\u30bb\u30df\u30ca\u30fcDIR\u5b9f\u6280\u8b1b\u7fd2\u4f1a. 2018.2 \u6771\u4eac<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading blue-line\">Grant<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u79d1\u5b66\u7814\u7a76\u8cbb\u88dc\u52a9\u91d1\u3000\u82e5\u624b\u7814\u7a76 \u300cMRI-Linac\u7528NonlinearCCC\u7dda\u91cf\u8a08\u7b97\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u958b\u767a\u300d(2018\u5e744\u6708\uff5e2020\u5e743\u6708\u3001\u7814\u7a76\u4ee3\u8868\u8005\u3000\u4f0a\u85e4\u8b19\u543e 351\u4e07\u5186\uff09<\/li>\n\n\n\n<li>\u516c\u76ca\u8ca1\u56e3\u6cd5\u4eba\u304c\u3093\u7814\u7a76\u632f\u8208\u8ca1\u56e3\u304c\u3093\u7814\u7a76\u52a9\u6210\u91d1\u3000(2018\u5e741\u6708\uff5e2018\u5e7412\u6708\u3001\u7814\u7a76\u4ee3\u8868\u8005\u3000\u9ad8\u5c71\u4f73\u6a39 50\u4e07\u5186\uff09<\/li>\n\n\n\n<li>\u516c\u76ca\u8ca1\u56e3\u6cd5\u4eba\u304c\u3093\u7814\u7a76\u632f\u8208\u8ca1\u56e3\u304c\u3093\u7814\u7a76\u52a9\u6210\u91d1\u3000(2018\u5e744\u6708\uff5e2019\u5e743\u6708\u3001\u7814\u7a76\u4ee3\u8868\u8005\u3000\u52dd\u7530\u7fa9\u4e4b 50\u4e07\u5186\uff09<\/li>\n\n\n\n<li>\u516c\u76ca\u8ca1\u56e3\u6cd5\u4eba\u304c\u3093\u7814\u7a76\u632f\u8208\u8ca1\u56e3\u6d77\u5916\u6d3e\u9063\u7814\u7a76\u52a9\u6210\u3000(2018\u5e74\u3001\u89d2\u8c37\u502b\u4e4b 20\u4e07\u5186\uff09<\/li>\n\n\n\n<li>\u4ed9\u53f0\u533b\u7642\u30bb\u30f3\u30bf\u30fc\u9662\u5185\u7814\u7a76\u52a9\u6210\u3000(2018\u5e744\u6708\uff5e2019\u5e743\u6708\u3001\u7814\u7a76\u4ee3\u8868\u8005\u3000\u6238\u68ee\u6e05\u6cbb 50\u4e07\u5186\uff09<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Original Papers Domestic Conference International Conference Patent Invited talk &amp; Symposium Grant<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-achievement.php","meta":{"_locale":"en_US","_original_post":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/?page_id=439","footnotes":""},"class_list":["post-693","page","type-page","status-publish","hentry","en-US"],"_links":{"self":[{"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/pages\/693","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/comments?post=693"}],"version-history":[{"count":3,"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/pages\/693\/revisions"}],"predecessor-version":[{"id":1245,"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/pages\/693\/revisions\/1245"}],"wp:attachment":[{"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/media?parent=693"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}