{"id":723,"date":"2023-07-01T11:43:25","date_gmt":"2023-07-01T02:43:25","guid":{"rendered":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/?page_id=723"},"modified":"2023-12-19T15:39:27","modified_gmt":"2023-12-19T06:39:27","slug":"achievement-2023","status":"publish","type":"page","link":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/en\/achievement-2023\/","title":{"rendered":"Achievement-2023"},"content":{"rendered":"\n<h2 class=\"wp-block-heading blue-line\">Original Papers<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Kadoya N, Kimura Y, Tozuka R, Tanaka S, Arai K, Katsuta Y, Shimizu H, Sugai Y, Yamamoto T, Umezawa R, Jingu K, &#8220;Evaluation of deep learning-based deliverable VMAT plan generated by prototype software for automated planning for prostate cancer patients&#8221;, J Radiat Res. 2023 Aug 22<\/li>\n\n\n\n<li>Tanaka S,&nbsp;Kadoya N,&nbsp;Ishizawa M,&nbsp;Katsuta Y, Arai K, Takahashi H, Xiao Y, Takahashi N, Sato K, Takeda K, Jingu K, &#8221; Evaluation of Unity 1.5 T MR-linac plan quality in patients with prostate cancer&#8221;, J Appl Clin Med Phys. 2023 Aug 10;e14122.<\/li>\n\n\n\n<li>Abe K,&nbsp;Kadoya N, Ito K, Tanaka S, Nakajima Y, Hashimoto S, Suda Y, Uno T, Jingu K, &#8220;Evaluation of the MVCT-based radiomic features as prognostic factor in patients with head and neck squamous cell carcinoma&#8221;, BMC Med Imaging<a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/?sort=date&amp;term=%22BMC+Med+Imaging%22%5Bjour%5D&amp;sort_order=desc\"><\/a><a href=\"https:\/\/www.ncbi.nlm.nih.gov\/nlmcatalog?sort=date&amp;term=%22BMC+Med+Imaging%22%5BTitle+Abbreviation%5D\"><\/a><a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/37528392\/#\"><\/a>.&nbsp;2023 Aug 1;23(1):102.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u30fbTozuka R, Kadoya N, Tomori S, Kimura Y, Kajikawa T, Sugai Y, Xiao Y, Jingu K, \u201cImprovement of deep learning prediction model in patient-specific QA for VMAT with MLC leaf position map and patient&#8217;s dose distribution\u201d, J Appl Clin Med Phys. 2023 Jun 1;e14055<\/li>\n\n\n\n<li>\u30fbKimura Y, Kadoya N, Oku Y, Jingu K, \u201cDevelopment of a deep learning-based error detection system without error dose maps in the patient-specific quality assurance of volumetric modulated arc therapy\u201d, J Radiat Res. 2023 May 12<\/li>\n\n\n\n<li>\u30fbIto K, Ishikawa Y, Teramura S, Kadoya N, Katsuta Y, Tanaka S, Takeda K, Jingu K, Yamada T, \u201cDevelopment of a collapsed cone convolution\/superposition dose calculation algorithm with a mass density-specific water kernel for magnetic resonance-guided radiotherapy\u201d, J Radiat Res. 2023 May 25;64(3):496-508<\/li>\n\n\n\n<li>\u30fbIkeda R, Kadoya N, Nakajima Y, Ishii S, Shibu T, Jingu K, \u201cImpact of CT scan parameters on deformable image registration accuracy using deformable thorax phantom\u201d, J Appl Clin Med Phys. 2023 May;24(5):e13917<\/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\u60a0\u8f1d\u767b\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u65b0\u4e95\u4e00\u5f18\u3001\u7530\u4e2d\u7965\u5e73\u3001\u52dd\u7530\u7fa9\u4e4b\u3001\u6728\u6751\u667a\u572d\u3001\u4f50\u85e4\u6e05\u548c\u3001\u6238\u585a\u51cc\u592a\u3001\u9ad8\u6a4b\u7d00\u5584\u3001\u795e\u5bae\u5553\u4e00.&#8221;Unity\u3067\u306eMRgRT\u306b\u304a\u3051\u308b\u6cbb\u7642\u8a08\u753bMR\u753b\u50cf\u3092\u7528\u3044\u305f\u30d5\u30a1\u30f3\u30c8\u30e0\u5b9f\u9a13\u3067\u306e\u816b\u760d\u63cf\u51fa\u7cbe\u5ea6\u306e\u8a55\u4fa1&#8221;.\u7b2c36\u56de\u65e5\u672c\u653e\u5c04\u7dda\u816b\u760d\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2023. 11 \u6a2a\u6d5c<\/li>\n\n\n\n<li>\u6238\u585a\u51cc\u592a\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u7530\u4e2d\u7965\u5e73\u3001\u6728\u6751\u7950\u5229\u3001\u52dd\u7530\u7fa9\u4e4b\u3001\u65b0\u4e95\u4e00\u5f18\u3001\u5c71\u672c\u8cb4\u4e5f\u3001\u6885\u6fa4\u73b2\u3001\u795e\u5bae\u5553\u4e00. \u201c\u524d\u7acb\u817aVMAT\u306b\u304a\u3051\u308b\u6df1\u5c64\u5b66\u7fd2\u3092\u7528\u3044\u305f\u7167\u5c04\u53ef\u80fd\u306a\u81ea\u52d5\u8a08\u753b\u306e\u521d\u671f\u691c\u8a0e\u201d. \u7b2c36\u56de\u65e5\u672c\u653e\u5c04\u7dda\u816b\u760d\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2023. 11 \u6a2a\u6d5c<\/li>\n\n\n\n<li>\u6797\u5343\u8389\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u6238\u585a\u51cc\u592a\u3001\u52dd\u7530\u7fa9\u4e4b\u3001\u65b0\u4e95\u4e00\u5f18\u3001\u7530\u4e2d\u7965\u5e73\u3001\u4f50\u85e4\u6e05\u548c\u3001\u795e\u5bae\u5553\u4e00.&#8221;\u6df1\u5c64\u5b66\u7fd2\u30d9\u30fc\u30b9\u81ea\u52d5\u8f2a\u90ed\u62bd\u51fa\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u306e\u81e8\u5e8a\u7684\u7cbe\u5ea6\u8a55\u4fa1&#8221;.\u7b2c36\u56de\u65e5\u672c\u653e\u5c04\u7dda\u816b\u760d\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2023. 11 \u6a2a\u6d5c<\/li>\n\n\n\n<li>\u6238\u585a\u51cc\u592a\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u65b0\u4e95\u4e00\u5f18\u3001\u4f50\u85e4\u6e05\u548c\u3001\u795e\u5bae \u5553\u4e00. \u201c1.5T MR-linac\u306b\u304a\u3051\u308b\u6df1\u5c64\u5b66\u7fd2\u3092\u4f7f\u7528\u3057\u305f\u65b0\u305f\u306a\u60a3\u8005QA\u30b7\u30b9\u30c6\u30e0\u306e\u958b\u767a\u201c. \u7b2c51\u56de\u65e5\u672c\u653e\u5c04\u7dda\u6280\u8853\u5b66\u4f1a\u79cb\u5b63\u5b66\u8853\u5927\u4f1a. 2023.10 \u540d\u53e4\u5c4b<\/li>\n\n\n\n<li>HiromiFooXiaoMei\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u6238\u585a\u51cc\u592a\u3001\u661f\u91ce\u5927\u5730\u3001\u7530\u4e2d\u7965\u5e73\u3001WingyiLee\u3001\u52dd\u7530\u7fa9\u4e4b\u3001\u65b0\u4e95\u4e00\u5f18\u3001\u795e\u5bae\u5553\u4e00.&#8221;Development of homology-based prediction method of radiation pneumonitis using CT ventilation image of lungs&#8221;.\u7b2c126\u56de\u65e5\u672c\u533b\u5b66\u7269\u7406\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2023.9 \u5e83\u5cf6<\/li>\n\n\n\n<li>WingyiLee\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u52dd\u7530\u7fa9\u4e4b\u3001\u7530\u4e2d\u7965\u5e73\u3001\u65b0\u4e95\u4e00\u5f18\u3001\u661f\u91ce\u5927\u5730\u3001\u795e\u5bae\u5553\u4e00.&#8221;Radiation pneumonitis predictive model using Radiomics and Dosiomics:Whole lung CT analysis&#8221;.\u7b2c126\u56de\u65e5\u672c\u533b\u5b66\u7269\u7406\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2023.9 \u5e83\u5cf6<\/li>\n\n\n\n<li>\u6797\u5343\u8389\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u6238\u585a\u51cc\u592a\u3001\u52dd\u7530\u7fa9\u4e4b\u3001\u65b0\u4e95\u4e00\u5f18\u3001\u7530\u4e2d\u7965\u5e73\u3001\u4f50\u85e4\u6e05\u548c\u3001\u795e\u5bae\u5553\u4e00.&#8221;\u524d\u7acb\u817a\u764c\u60a3\u8005\u306b\u5bfe\u3059\u308b\u5546\u7528\u306eAI\u30d9\u30fc\u30b9\u81ea\u52d5\u8f2a\u90ed\u62bd\u51fa\u306e\u7cbe\u5ea6\u8a55\u4fa1&#8221;.\u7b2c126\u56de\u65e5\u672c\u533b\u5b66\u7269\u7406\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2023.9 \u5e83\u5cf6<\/li>\n\n\n\n<li>\u8fd1\u85e4\u6b63\u8f1d\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u6238\u585a\u51cc\u592a\u3001\u7530\u4e2d\u7965\u5e73\u3001\u52dd\u7530\u7fa9\u4e4b\u3001\u65b0\u4e95\u4e00\u5f18\u3001\u795e\u5bae\u5553\u4e00.&#8221;\u6df1\u5c64\u5b66\u7fd2\u3092\u7528\u3044\u305f\u982d\u981a\u90e8VMAT\u306b\u304a\u3051\u308b\u81ea\u52d5\u6cbb\u7642\u8a08\u753b\u306e\u7cbe\u5ea6\u8a55\u4fa1&#8221;.\u7b2c126\u56de\u65e5\u672c\u533b\u5b66\u7269\u7406\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2023.9 \u5e83\u5cf6<\/li>\n\n\n\n<li>\u7530\u4e2d\u7965\u5e73\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u77f3\u6fa4\u7f8e\u512a\u3001\u52dd\u7530\u7fa9\u4e4b\u3001\u65b0\u4e95\u4e00\u5f18\u3001\u9ad8\u6a4b\u6625\u5948\u3001XiaoYushan\u3001\u9ad8\u6a4b\u7d00\u5584\u3001\u4f50\u85e4\u6e05\u548c\u3001\u6b66\u7530\u8ce2\u3001\u795e\u5bae\u5553\u4e00.&#8221;1.5TMR-Linac\u306b\u304a\u3051\u308b\u30d3\u30fc\u30e0\u6570\u3068\u30bb\u30b0\u30e1\u30f3\u30c8\u6570\u306e\u9055\u3044\u304c\u30d7\u30e9\u30f3\u306e\u8cea\u306b\u4e0e\u3048\u308b\u5f71\u97ff&#8221;.\u7b2c126\u56de\u65e5\u672c\u533b\u5b66\u7269\u7406\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2023.9 \u5e83\u5cf6<\/li>\n\n\n\n<li>\u7530\u4e2d\u60a0\u8f1d\u767b\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u65b0\u4e95\u4e00\u5f18\u3001\u7530\u4e2d\u7965\u5e73\u3001\u52dd\u7530\u7fa9\u4e4b\u3001\u6728\u6751\u667a\u572d\u3001\u4f50\u85e4\u6e05\u548c\u3001\u6238\u585a\u51cc\u592a\u3001\u9ad8\u6a4b\u7d00\u5584\u3001\u795e\u5bae\u5553\u4e00. &#8220;MRgRT\u306b\u304a\u3051\u308b\u6a2a\u9694\u819c\u540c\u671fMR\u753b\u50cf\u3092\u7528\u3044\u305f\u6a21\u64ec\u816b\u760d\u306e\u63cf\u51fa\u7cbe\u5ea6\u306e\u8a55\u4fa1\u201d. \u7b2c2\u56de\u65e5\u672cMR\u753b\u50cf\u8a98\u5c0e\u9069\u5fdc\u653e\u5c04\u7dda\u6cbb\u7642\u7814\u7a76\u4f1a. 2023.7 \u4ed9\u53f0<\/li>\n\n\n\n<li>\u661f\u91ce\u5927\u5730\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u52dd\u7530\u7fa9\u4e4b\u3001\u65b0\u4e95\u4e00\u5f18\u3001\u7530\u4e2d\u7965\u5e73\u3001\u4f50\u85e4\u6e05\u548c\u3001\u9ad8\u6a4b\u7d00\u5584\u3001\u795e\u5bae\u5553\u4e00. \u201cMRgRT\u306b\u304a\u3051\u308b\u524d\u7acb\u817a\u764c\u60a3\u8005\u306eMR-to-MR DIR\u306e\u7cbe\u5ea6\u8a55\u4fa1\u201c. \u7b2c2\u56de\u65e5\u672cMR\u753b\u50cf\u8a98\u5c0e\u9069\u5fdc\u653e\u5c04\u7dda\u6cbb\u7642\u7814\u7a76\u4f1a. 2023.7 \u4ed9\u53f0<\/li>\n\n\n\n<li>\u6238\u585a\u51cc\u592a\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u65b0\u4e95\u4e00\u5f18\u3001\u4f50\u85e4\u6e05\u548c\u3001\u795e\u5bae \u5553\u4e00. \u201c\u6df1\u5c64\u5b66\u7fd2\u3092\u4f7f\u7528\u3057\u305f\u5373\u6642\u9069\u5fdc\u653e\u5c04\u7dda\u6cbb\u7642\u8a08\u753b\u306b\u304a\u3051\u308b\u65b0\u305f\u306a\u60a3\u8005QA\u30b7\u30b9\u30c6\u30e0\u306e\u958b\u767a \u201c. \u7b2c2\u56de\u65e5\u672cMR\u753b\u50cf\u8a98\u5c0e\u9069\u5fdc\u653e\u5c04\u7dda\u6cbb\u7642\u7814\u7a76\u4f1a. 2023.7 \u4ed9\u53f0<\/li>\n\n\n\n<li>\u52dd\u7530\u7fa9\u4e4b\u3001\u6885\u6fa4\u73b2\u3001\u65b0\u4e95\u4e00\u5f18\u3001\u9ad8\u6a4b\u7d00\u5584\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u7530\u4e2d\u7965\u5e73\u3001\u795e\u5bae\u5553\u4e00. \u201c\u81b5\u304c\u3093SRT\u306b\u5bfe\u3059\u308bMRgRT\u6cbb\u7642\u8a08\u753b\u201d. \u7b2c2\u56de\u65e5\u672cMR\u753b\u50cf\u8a98\u5c0e\u9069\u5fdc\u653e\u5c04\u7dda\u6cbb\u7642\u7814\u7a76\u4f1a. 2023.7 \u4ed9\u53f0<\/li>\n\n\n\n<li>\u6238\u585a\u51cc\u592a. \u201c\u9ad8\u7cbe\u5ea6\u653e\u5c04\u7dda\u6cbb\u7642\u306b\u304a\u3051\u308b\u6df1\u5c64\u5b66\u7fd2\u30d9\u30fc\u30b9\u81ea\u52d5\u6cbb\u7642\u8a08\u753b\u306e\u521d\u671f\u691c\u8a0e\u201d. \u7b2c5\u56de\u65e5\u672c\u30e1\u30c7\u30a3\u30ab\u30ebAI\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2023. 6 \u6771\u4eac<\/li>\n\n\n\n<li>\u661f\u91ce\u5927\u5730\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u52dd\u7530\u7fa9\u4e4b\u3001\u65b0\u4e95\u4e00\u5f18\u3001\u7530\u4e2d\u7965\u5e73\u3001\u77f3\u6fa4\u7f8e\u512a\u3001\u4f50\u85e4\u6e05\u548c\u3001\u6b66\u7530\u8ce2\u3001\u9ad8\u57ce\u4e45\u9053\u3001\u795e\u5bae \u5553\u4e00. \u201c\u524d\u7acb\u817a\u764c\u60a3\u8005\u306b\u5bfe\u3059\u308bMRgRT\u3067\u306e\u8f2a\u90ed\u4f1d\u642c\u306e\u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9DIR\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u3092\u7528\u3044\u305f\u691c\u8a0e\u201c. \u7b2c125\u56de\u65e5\u672c\u533b\u5b66\u7269\u7406\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2023.4 \u6a2a\u6d5c<\/li>\n\n\n\n<li>\u6238\u585a\u51cc\u592a\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u6238\u68ee\u8056\u6cbb\u3001\u6728\u6751\u7950\u5229\u3001\u68b6\u5ddd\u667a\u535a\u3001\u83c5\u4e95\u88d5\u6597\u3001\u8096\u7389\u6749\u3001\u795e\u5bae \u5553\u4e00. \u201cDevelopment of a deep-learning system that instantly provides patient-specific QA results using dose distribution in patient body and MLC information \u201c. \u7b2c125\u56de\u65e5\u672c\u533b\u5b66\u7269\u7406\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. 2023.4 \u6a2a\u6d5c<\/li>\n\n\n\n<li>\u661f\u91ce\u5927\u5730\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u52dd\u7530\u7fa9\u4e4b\u3001\u65b0\u4e95\u4e00\u5f18\u3001\u7530\u4e2d\u7965\u5e73\u3001\u77f3\u6fa4\u7f8e\u512a\u3001\u4f50\u85e4\u6e05\u548c\u3001\u795e\u5bae\u5553\u4e00.&nbsp;\u201cMRgRT\u306b\u304a\u3051\u308b\u9aa8\u76e4\u9818\u57df\u306e\u8f2a\u90ed\u30d7\u30ed\u30d1\u30b2\u30fc\u30b7\u30e7\u30f3\u306e\u8a55\u4fa1\uff1a3\u7a2e\u985e\u306e\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u306e\u6bd4\u8f03\u201d.&nbsp;\u7b2c36\u56de\u9ad8\u7cbe\u5ea6\u653e\u5c04\u7dda\u5916\u90e8\u7167\u5c04\u90e8\u4f1a\u5b66\u8853\u5927\u4f1a. 2023. 3&nbsp;\u5343\u8449<\/li>\n\n\n\n<li>\u7530\u4e2d\u60a0\u8f1d\u767b\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u65b0\u4e95\u4e00\u5f18\u3001\u7530\u4e2d\u7965\u5e73\u3001\u52dd\u7530\u7fa9\u4e4b\u3001\u6728\u6751\u667a\u572d\u3001\u4f50\u85e4\u6e05\u548c\u3001\u6238\u585a\u51cc\u592a\u3001\u9ad8\u6a4b\u7d00\u5584\u3001\u795e\u5bae\u5553\u4e00. &#8220;MR-Linac\u306b\u3088\u308b\u6a2a\u9694\u819c\u540c\u671fMR\u753b\u50cf\u3092\u7528\u3044\u305f\u816b\u760d\u63cf\u51fa\u306e\u7cbe\u5ea6\u8a55\u4fa1\u306e\u691c\u8a0e&#8221;.&nbsp;\u7b2c36\u56de\u9ad8\u7cbe\u5ea6\u653e\u5c04\u7dda\u5916\u90e8\u7167\u5c04\u90e8\u4f1a\u5b66\u8853\u5927\u4f1a. 2023.3&nbsp;\u5343\u8449<\/li>\n\n\n\n<li>\u6238\u585a\u51cc\u592a\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u7530\u4e2d\u7965\u5e73\u3001\u6728\u6751\u7950\u5229\u3001\u52dd\u7530\u7fa9\u4e4b\u3001\u65b0\u4e95\u4e00\u5f18\u3001\u5c71\u672c\u8cb4\u4e5f\u3001\u6885\u6fa4\u73b2\u3001\u795e\u5bae\u5553\u4e00.&nbsp;\u201c\u524d\u7acb\u817aVMAT\u306b\u304a\u3051\u308b\u6df1\u5c64\u5b66\u7fd2\u30d9\u30fc\u30b9\u306e\u81ea\u52d5\u8a08\u753b\u306e\u521d\u671f\u691c\u8a0e\u201d.&nbsp;\u7b2c36\u56de\u9ad8\u7cbe\u5ea6\u653e\u5c04\u7dda\u5916\u90e8\u7167\u5c04\u90e8\u4f1a\u5b66\u8853\u5927\u4f1a. 2023. 3&nbsp;\u5343\u8449<\/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>Inoue K, Kadoya N, Takagi H, Tozuka R, Jingu K. \u201cDevelopment of a Automatic Segmentation Using Multi-Label Method with Modified Dice Similarity Coefficient Loss Function in Pelvic Region\u201d. 65th annual meeting of AAPM, 2023.7 Houston, USA<\/li>\n\n\n\n<li>Tozuka R, Kadoya N, Arai K, Sato K, Jingu K. \u201cFeasibility of a Novel Method for Patient-Specific Quality Assurance for Stereotactic MR-Guided Adaptive Radiotherapy with Deep Learning\u201d. 65th annual meeting of AAPM, 2023.7 Houston, USA<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading blue-line\">Award<\/h2>\n\n\n\n<p>\u6238\u585a\u51cc\u592a, \u7b2c51\u56de\u65e5\u672c\u653e\u5c04\u7dda\u6280\u8853\u5b66\u4f1a\u79cb\u5b63\u5b66\u8853\u5927\u4f1a\u3000\u5ea7\u9577\u63a8\u85a6\u512a\u79c0\u7814\u7a76\u767a\u8868<\/p>\n\n\n\n<p>\u6238\u585a\u51cc\u592a,&nbsp;\u7b2c125\u56de\u65e5\u672c\u533b\u5b66\u7269\u7406\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a\u3000\u5927\u4f1a\u9577\u8cde&nbsp;(\u30b7\u30eb\u30d0\u30fc)<\/p>\n\n\n\n<h2 class=\"wp-block-heading blue-line\">Patent<\/h2>\n\n\n\n<p>\u5bb6\u5b50\u7fa9\u6717\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u6709\u8cc0\u4e45\u54f2. \u30d7\u30ed\u30b0\u30e9\u30e0\u3001\u753b\u50cf\u5909\u5f62\u65b9\u6cd5\u53ca\u3073\u753b\u50cf\u5909\u5f62\u88c5\u7f6e. \u7279\u98582023-046694<\/p>\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>Noriyuki Kadoya, \u201cDevelopment of AI-Based Technology for Automated Contouring, Treatment Planning, and Patient QA\u201d, AAPM KSMP-JSMP-KAMPiNA Joint Symposium, 65th annual meeting of AAPM, 2023.7 Houston, USA<\/li>\n\n\n\n<li>\u89d2\u8c37\u502b\u4e4b.&nbsp;\u201c\u653e\u5c04\u7dda\u6cbb\u7642\u3078\u306e\u753b\u50cf\u6280\u8853\u306e\u5fdc\u7528\u201d.\u3000\u7b2c82\u56de\u65e5\u672c\u533b\u5b66\u653e\u5c04\u7dda\u5b66\u4f1a\u7dcf\u4f1a\u3001\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u30012023.4&nbsp;\u6a2a\u6d5c<\/li>\n\n\n\n<li>\u89d2\u8c37\u502b\u4e4b. \u201c\u533b\u5b66\u7269\u7406\u306b\u304a\u3051\u308b\u653e\u5c04\u7dda\u6cbb\u7642\u6280\u8853\u306e\u9769\u65b0\u201d. \u7b2c125\u56de\u65e5\u672c\u533b\u5b66\u7269\u7406\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a. \u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u30012023.4 \u6a2a\u6d5c<\/li>\n\n\n\n<li>\u89d2\u8c37\u502b\u4e4b.&nbsp;\u201c\u533b\u7642\u73fe\u5834\u306b\u304a\u3051\u308b\u81ea\u52d5\u8f2a\u90ed\u62bd\u51fa\u306e\u6d3b\u7528\u306b\u3064\u3044\u3066\u201d.\u3000\u6771\u5317\u533b\u5b66\u7269\u7406\u30b9\u30ad\u30eb\u30a2\u30c3\u30d7\u7814\u4fee\u4f1a. 2023.5&nbsp;\u30aa\u30f3\u30e9\u30a4\u30f3<\/li>\n\n\n\n<li>\u89d2\u8c37\u502b\u4e4b. \u201cDIR\u3092\u5229\u7528\u3057\u305f\u8f2a\u90ed\u63cf\u51fa\u306e\u52b9\u7387\u5316\u201d.\u3000\u5c0f\u7dda\u6e90\u6cbb\u7642\u90e8\u4f1a\u7b2c25\u56de\u5b66\u8853\u5927\u4f1a\u30b7\u30f3\u30dd\u30b8\u30a6\u30e01. 2023.5 \u795e\u6238<\/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>\u6771\u5317\u5927\u5b66BIP\uff08\u30d3\u30b8\u30cd\u30b9\uff65\u30a4\u30f3\u30ad\u30e5\u30d9\u30fc\u30b7\u30e7\u30f3\uff65\u30d7\u30ed\u30b0\u30e9\u30e0\uff09\u300c\u300cAI\u00d7\u653e\u5c04\u7dda\u6cbb\u7642\u300d\u306b\u3088\u308b\u80ba\u304c\u3093\u64b2\u6ec5\u306b\u5411\u3051\u305f\u88fd\u54c1\u958b\u767a\u3068\u4e8b\u696d\u5316\u691c\u8a3c\u300d\u306e\u30d7\u30ed\u30c8\u30bf\u30a4\u30d7\u958b\u767a\u3068\u4e8b\u696d\u5316\u691c\u8a3c\u300d(2023\u5e744\u6708\uff5e2024\u5e743\u6708\u3001\u7814\u7a76\u4ee3\u8868\u8005\u3000\u52dd\u7530\u7fa9\u4e4b 500\u4e07\u5186<\/li>\n\n\n\n<li>\u79d1\u5b66\u7814\u7a76\u8cbb\u88dc\u52a9\u91d1\u3000\u57fa\u76e4C&nbsp;\u300c\u6df1\u5c64\u5f37\u5316\u5b66\u7fd2\u306b\u3088\u308b\u771f\u306e\u201c\u4eba\u5de5\u77e5\u80fd\u578b\u201d\u81ea\u52d5\u653e\u5c04\u7dda\u7167\u5c04\u8a08\u753b\u6cd5\u306e\u958b\u767a\u300d(2023\u5e744\u6708\uff5e2026\u5e743\u6708\u3001\u7814\u7a76\u4ee3\u8868\u8005&nbsp;\u89d2\u8c37\u502b\u4e4b&nbsp;468\u4e07\u5186<\/li>\n\n\n\n<li>\u79d1\u5b66\u7814\u7a76\u8cbb\u88dc\u52a9\u91d1\u3000\u57fa\u76e4C&nbsp;\u300c\u6df1\u5c64\u5b66\u7fd2\u6280\u8853\u306b\u3088\u308b\u7167\u5c04\u4e2d\u306e\u52d5\u304d\u3092\u8003\u616e\u3057\u305f\u672c\u5f53\u306e\u7dda\u91cf\u5206\u5e03\u306e\u4f5c\u6210\u300d(2023\u5e744\u6708\uff5e2028\u5e743\u6708\u3001\u7814\u7a76\u4ee3\u8868\u8005&nbsp;\u7530\u4e2d\u7965\u5e73&nbsp;481\u4e07\u5186\uff09<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Original Papers Domestic Conference International Conference Award \u6238\u585a\u51cc\u592a, \u7b2c51\u56de\u65e5\u672c\u653e\u5c04\u7dda\u6280\u8853\u5b66\u4f1a\u79cb\u5b63\u5b66\u8853\u5927\u4f1a\u3000\u5ea7\u9577\u63a8\u85a6\u512a\u79c0\u7814\u7a76\u767a\u8868 \u6238\u585a\u51cc\u592a,&nbsp;\u7b2c125\u56de\u65e5\u672c\u533b\u5b66\u7269\u7406\u5b66\u4f1a\u5b66\u8853\u5927\u4f1a\u3000\u5927\u4f1a\u9577\u8cde&nbsp;(\u30b7\u30eb\u30d0\u30fc) Patent \u5bb6\u5b50\u7fa9\u6717\u3001\u89d2\u8c37\u502b\u4e4b\u3001\u6709\u8cc0\u4e45\u54f2. \u30d7\u30ed\u30b0\u30e9\u30e0\u3001\u753b\u50cf\u5909\u5f62\u65b9\u6cd5\u53ca\u3073\u753b\u50cf\u5909\u5f62\u88c5\u7f6e. \u7279\u98582023-046694 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=721","footnotes":""},"class_list":["post-723","page","type-page","status-publish","hentry","en-US"],"_links":{"self":[{"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/pages\/723","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=723"}],"version-history":[{"count":11,"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/pages\/723\/revisions"}],"predecessor-version":[{"id":2368,"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/pages\/723\/revisions\/2368"}],"wp:attachment":[{"href":"https:\/\/www.rt-medphys.med.tohoku.ac.jp\/wp-json\/wp\/v2\/media?parent=723"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}