{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T04:05:53Z","timestamp":1785557153973,"version":"3.56.0"},"reference-count":28,"publisher":"Japanese Society of Radiological Technology","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nippon Hoshasen Gijutsu Gakkai Zasshi","Jpn. J. Radiol. Technol."],"published-print":{"date-parts":[[2021]]},"DOI":"10.6009\/jjrt.2021_jsrt_77.6.581","type":"journal-article","created":{"date-parts":[[2021,6,19]],"date-time":"2021-06-19T18:09:26Z","timestamp":1624126166000},"page":"581-588","source":"Crossref","is-referenced-by-count":0,"title":["Accuracy of Classification of Cerebral Blood Flow Reduction Patterns Using Statistical Analysis Images Generated with Simulated SPECT Datasets via Deep Learning","\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u30c7\u30fc\u30bf\u3092\u7528\u3044\u305f\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u306b\u3088\u308b\u8133\u8840\u6d41 SPECT \u7d71\u8a08\u89e3\u6790\u753b\u50cf\u306e\u8840\u6d41\u4f4e\u4e0b\u30d1\u30bf\u30fc\u30f3\u306e\u5206\u985e\u7cbe\u5ea6"],"prefix":"10.6009","volume":"77","author":[{"given":"Hoshio","family":"Uwabe","sequence":"first","affiliation":[{"name":"Department of Radiology, Shimane University Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasushi","family":"Yamamoto","sequence":"additional","affiliation":[{"name":"Department of Radiology, Shimane University Faculty of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nobuhiro","family":"Yada","sequence":"additional","affiliation":[{"name":"Department of Radiology, Shimane University Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuki","family":"Hino","sequence":"additional","affiliation":[{"name":"Department of Radiology, Shimane University Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Koya","family":"Yamane","sequence":"additional","affiliation":[{"name":"Department of Radiology, Shimane University Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshinori","family":"Miyahara","sequence":"additional","affiliation":[{"name":"Department of Radiology, Shimane University Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hajime","family":"Kitagaki","sequence":"additional","affiliation":[{"name":"Department of Radiology, Shimane University Faculty of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4040","reference":[{"key":"1","unstructured":"1) \u4e2d\u56fd\u7dcf\u5408\u7814\u7a76\u30fb\u3055\u304f\u3089\u30b5\u30a4\u30a8\u30f3\u30b9\u30bb\u30f3\u30bf\u30fc\uff0e\u4e2d\u56fd\u306b\u304a\u3051\u308b\u4eba\u5de5\u77e5\u80fd\u958b\u767a\u306e\u73fe\u72b6\u3068\u52d5\u5411\uff0e\u79d1\u5b66\u6280\u8853\u632f\u8208\u6a5f\u69cb\uff0c2018."},{"key":"2","unstructured":"2) \u5e73\u7530\u5065\u53f8\uff0c\u85e4\u5f8c\u5ec9\uff0c\u5c0f\u5ddd\u8cb4\u5f18\uff0c\u4ed6\uff0e\u6838\u533b\u5b66\u306b\u304a\u3051\u308b\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u3092\u7528\u3044\u305f\u753b\u50cf\u8a3a\u65ad\uff0c\u753b\u50cf\u51e6\u7406\uff0eINNERVISION 2019; 34(7): 60-63."},{"key":"3","doi-asserted-by":"crossref","unstructured":"3) Choi H, Ha S, Im HJ, et al. Refining diagnosis of Parkinson\u02bcs disease with deep learning-based interpretation of dopamine transporter imaging. Neuroimage Clin 2017; 16: 586-594.","DOI":"10.1016\/j.nicl.2017.09.010"},{"key":"4","doi-asserted-by":"crossref","unstructured":"4) Wenzel M, Milletari F, Kr\u00fcger J, et al. Automatic classification of dopamine transporter SPECT: deep convolutional neural networks can be trained to be robust with respect to variable image characteristics. Eur J Nucl Med Mol Imaging 2019; 46 (13): 2800-2811.","DOI":"10.1007\/s00259-019-04502-5"},{"key":"5","unstructured":"5) Minoshima S, Frey KA, Koeppe RA, et al. A diagnostic approach in Alzheimer\u02bcs disease using three-dimensional stereotactic surface projections of fluorine-18-FDG PET. J Nucl Med 1995; 36(7): 1238-1248."},{"key":"6","unstructured":"6) \u5c71\u672c\u667a\u6717\uff0e\u6838\u533b\u5b66\u306b\u304a\u3051\u308b\u81e8\u5e8a\u89e3\u6790\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u306e\u57fa\u790e\u3068\u5fdc\u7528\uff0e\u4eac\u90fd\uff1a\u65e5\u672c\u653e\u5c04\u7dda\u6280\u8853\u5b66\u4f1a\uff0c2011: 9-15."},{"key":"7","unstructured":"7) \u6842\u5ddd\u8302\u5f66\uff0e6 \u7ae0\u00a0\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u652f\u63f4\u8a3a\u65ad(CAD)\uff0e\u8a3a\u7642\u653e\u5c04\u7dda\u6280\u8853\u9078\u66f8 \u533b\u7528\u753b\u50cf\u60c5\u5831\u5b66\u6539\u8a02 3 \u7248\uff0e\u5357\u5c71\u5802\uff0c\u6771\u4eac\uff0c2014: 210-211."},{"key":"8","doi-asserted-by":"crossref","unstructured":"8) Liu M, Zhang D, Shen D, et al. Hierarchical fusion of features and classifier decisions for Alzheimer\u02bcs disease diagnosis. Hum Brain Mapp 2014; 35(4): 1305-1319.","DOI":"10.1002\/hbm.22254"},{"key":"9","doi-asserted-by":"crossref","unstructured":"9) Suk HI, Lee SW, Shen D, et al. Hierarchical feature representation and multimodal fusion with deep learning for AD\/MCI diagnosis. Neuroimage 2014; 101: 569-582.","DOI":"10.1016\/j.neuroimage.2014.06.077"},{"key":"10","doi-asserted-by":"crossref","unstructured":"10) Ding Y, Sohn JH, Kawczynski MG, et al. A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain. Radiology 2019; 290(2): 456-464.","DOI":"10.1148\/radiol.2018180958"},{"key":"11","doi-asserted-by":"crossref","unstructured":"11) Iizuka T, Fukasawa M, Kameyama M. Deep-learning-based imaging-classification identified cingulate island sign in dementia with Lewy bodies. Sci Rep 2019; 9(1): 8944.","DOI":"10.1038\/s41598-019-45415-5"},{"key":"12","doi-asserted-by":"crossref","unstructured":"12) Wada A, Tsuruta K, Irie R, et al. Differentiating Alzheimer\u02bcs disease from dementia with Lewy bodies using a deep learning technique based on structural brain connectivity. Magn Reson Med Sci 2019; 18(3): 219-224.","DOI":"10.2463\/mrms.mp.2018-0091"},{"key":"13","doi-asserted-by":"crossref","unstructured":"13) Yada N, Onishi H, Miyai M, et al. Does applying resolution recovery to normal databases confer an advantage over conventional 3D-stereotactic surface projection techniques? Radiol Phys Technol 2017; 10(2): 240-248.","DOI":"10.1007\/s12194-017-0391-8"},{"key":"14","doi-asserted-by":"crossref","unstructured":"14) \u5c71\u672c\u6cf0\u53f8\uff0c\u5c0f\u91ce\u53e3\u660c\u4e45\uff0c\u548c\u7530\u662d\u5f66\uff0c\u4ed6\uff0e123I-IMP Graph Plot \u6cd5\u691c\u67fb\u6642\u9593\u77ed\u7e2e\u30d7\u30ed\u30c8\u30b3\u30fc\u30eb\u306e\u691c\u8a0e\uff0e\u65e5\u653e\u6280\u5b66\u8a8c 2011; 67(5): 524-533.","DOI":"10.6009\/jjrt.67.524"},{"key":"15","unstructured":"15) Yada N, Onishi H. Validation of computed tomography-based attenuation correction of deviation between theoretical and actual values in four computed tomography scanners. Asia Ocean J Nucl Med Biol 2016; 4(2): 81-89."},{"key":"16","doi-asserted-by":"crossref","unstructured":"16) Minoshima S, Foster NL, Sima AA, et al. Alzheimer\u02bcs disease versus dementia with Lewy bodies: cerebral metabolic distinction with autopsy confirmation. Ann Neurol 2001; 50(3): 358-365.","DOI":"10.1002\/ana.1133"},{"key":"17","doi-asserted-by":"crossref","unstructured":"17) Shimizu S, Hanyu H, Kanetaka H, et al. Differentiation of dementia with Lewy bodies from Alzheimer\u02bcs disease using brain SPECT. Dement Geriatr Cogn Disord 2005; 20(1): 25-30.","DOI":"10.1159\/000085070"},{"key":"18","unstructured":"18) \u77f3\u4e95\u4e00\u6210\uff0e\u8a8d\u77e5\u75c7\u8a3a\u65ad\u306b\u304a\u3051\u308b CAD \u30b7\u30b9\u30c6\u30e0\uff0eMed Imag Tech 2010; 28(1): 14-18."},{"key":"19","unstructured":"19) \u6839\u672c\u6e05\u8cb4\uff0c\u671d\u7530\u9686\uff0e\u8a8d\u77e5\u75c7\u81e8\u5e8a\u306b\u304a\u3051\u308b\u8133\u8840\u6d41 SPECT \u3068\u8133\u5f62\u614b MRI\uff0eMed Imag Tech 2010; 28(1): 8-13."},{"key":"20","unstructured":"20) \u5927\u5cf6\u3042\u307f\uff0c\u795e\u8c37\u76f4\u5e0c\uff0c\u7be0\u539f\u7bc4\u5145\uff0eAlexNet \u3092\u7528\u3044\u305f\u30de\u30f3\u30e2\u30b0\u30e9\u30d5\u30a3\u753b\u50cf\u306b\u304a\u3051\u308b\u4e73\u817a\u6fc3\u5ea6\u306e\u81ea\u52d5\u5206\u985e\uff0e\u533b\u7528\u753b\u50cf\u60c5\u5831\u4f1a\u8a8c 2019; 36(2): 59-63."},{"key":"21","unstructured":"21) \u5c71\u53e3\u4eae\u5e73\uff0c\u5d8b\u672c\u516c\u5fb3\uff0c\u6cb3\u6dfb\u60a6\u660c\uff0c\u4ed6\uff0e\u814e\u81d3\u7cf8\u7403\u4f53\u75c5\u7406\u753b\u50cf\u306e Deep Learning \u306b\u3088\u308b\u6240\u898b\u5206\u985e\u624b\u6cd5\u306e\u691c\u8a0e\uff0e\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u7814\u7a76\u4f1a\u8cc7\u6599\uff0c2017\uff0e"},{"key":"22","unstructured":"22) Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. NeurIPS 2012; 1097-1105."},{"key":"23","unstructured":"23) Diederik PK, Ba J. Adam: A method for stochastic optimization. arXiv preprint arXiv 2014; 1412: 6980."},{"key":"24","unstructured":"24) Sony Network Communications Inc., Neural Network Console Version1.20 \u53d6\u6271\u8aac\u660e\u66f8\uff0e"},{"key":"25","unstructured":"25) \u85e4\u7530\u5e83\u5fd7\uff0e\u533b\u7528\u753b\u50cf\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u5165\u9580\uff0e\u6771\u4eac\uff1a\u30aa\u30fc\u30e0\u793e\uff0c2019."},{"key":"26","doi-asserted-by":"crossref","unstructured":"26) Selvaraju RR, Cogswell M, Das A, et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. IEEE Int Conf Comput Vis Workshops 2017; 618-626.","DOI":"10.1109\/ICCV.2017.74"},{"key":"27","doi-asserted-by":"crossref","unstructured":"27) \u5c71\u672c\u6cf0\u53f8\uff0c\u5927\u897f\u82f1\u96c4\uff0c\u6751\u4e0a\u525b\uff0c\u4ed6\uff0e\u8133\u8840\u6d41 SPECT \u7d71\u8a08\u5b66\u7684\u753b\u50cf\u89e3\u6790\u306b\u304a\u3051\u308b\u7cbe\u5ea6\u30fb\u8a55\u4fa1\u306b\u95a2\u3059\u308b\u7814\u7a76\u73ed\u5831\u544a\uff0e\u65e5\u653e\u6280\u5b66\u8a8c 2008; 64(6): 752-765.","DOI":"10.6009\/jjrt.64.752"},{"key":"28","doi-asserted-by":"crossref","unstructured":"28) Onishi H, Hatazawa J, Nakagawara J, et al. Impact of injected dose and acquisition time on a normal database by use of 3D-SSP in SPECT images: quantitative simulation studies. Radiol Phys Technol 2015; 8(2): 224-231.","DOI":"10.1007\/s12194-015-0311-8"}],"container-title":["Japanese Journal of Radiological Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/jjrt\/77\/6\/77_2021_JSRT_77.6.581\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T03:48:18Z","timestamp":1785556098000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/jjrt\/77\/6\/77_2021_JSRT_77.6.581\/_article\/-char\/ja\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":28,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021]]}},"URL":"https:\/\/doi.org\/10.6009\/jjrt.2021_jsrt_77.6.581","relation":{},"ISSN":["0369-4305","1881-4883"],"issn-type":[{"value":"0369-4305","type":"print"},{"value":"1881-4883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"article-number":"2021_JSRT_77.6.581"}}