{"id":16,"date":"2024-06-13T15:27:47","date_gmt":"2024-06-13T06:27:47","guid":{"rendered":"https:\/\/www.clg.niigata-u.ac.jp\/~sichikawa\/?page_id=16"},"modified":"2026-03-17T11:37:41","modified_gmt":"2026-03-17T02:37:41","slug":"publication","status":"publish","type":"page","link":"https:\/\/www.clg.niigata-u.ac.jp\/~sichikawa\/publication\/","title":{"rendered":"Publications"},"content":{"rendered":"\n

\u6700\u65b0\u60c5\u5831\u306f\u3053\u3061\u3089\u304b\u3089
https:\/\/researchmap.jp\/sichikawa<\/a><\/p>\n\n\n\n

\u6b27\u6587\u5b66\u8853\u8ad6\u6587<\/strong>\uff08*: corresponding author)<\/h2>\n\n\n\n

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  1. Fukunaga M, Ichikawa S<\/strong>, Ichijiri K, Ito O, Moriya T, Yamaguchi Y. Contrast dose determination using effective diameter in patients of unknown weight for dynamic computed tomography of the upper abdomen: a feasibility study. Radiol Phys Technol. 2026. doi: https:\/\/doi.org\/10.1007\/s12194-025-00995-y. Epub ahead of print. [Link<\/a>]<\/li>\n\n\n\n
  2. Takahashi S, Ichikawa S<\/strong>, Watanabe K, Ueda H, Arima H, Yamato Y, Takeuchi T, Hosogane N, Okamoto M, Umezu M, Oba H, Kondo Y, Seki S. Development and Validation of Transformer- and Convolutional Neural Network-Based Deep Learning Models to Predict Curve Progression in Adolescent Idiopathic Scoliosis. J. Clin. Med. 2025;14(20):7216. [Link<\/a>]<\/li>\n\n\n\n
  3. Umezu M, Kondo Y, Ichikawa S<\/strong>, Sasaki Y, Kaneko K, Ozaki T, Koizumi N, Seki H. Artificial intelligence-based prediction of breast cancer recurrence using preoperative contrast-enhanced computed tomography and clinical information. Journal of Health Sciences of Niigata University. 2025;22. (in press) <\/li>\n\n\n\n
  4. Ichikawa S<\/strong>*, Kondo Y, Yokoyama S. Time series-derived fractal dimension of CT perfusion in acute ischemic stroke: a promising marker for hypoperfused tissue quantification. Int J Comput Assist Radiol Surg. 2025. doi: 10.1007\/s11548-025-03500-3. Epub ahead of print. [Link<\/a>]<\/li>\n\n\n\n
  5. Umezu M, Kondo Y, Ichikawa S<\/strong>, Sasaki Y, Kaneko K, Ozaki T, Koizumi N, Seki H. Recurrence prediction of invasive ductal carcinoma from preoperative contrast-enhanced computed tomography using deep convolutional neural network. Biomed Phys Eng Express. 2025;11(4):045025. [Link<\/a>]<\/li>\n\n\n\n
  6. Ichikawa S<\/strong>*, Kondo Y, Okamoto M, Kondo T, Takahashi N. Radiomic Fingerprints: Automated Personal Identification in Mass Disasters Using Shape-Based Features of Thoracic Vertebral Bodies on CT. J Imaging Inform Med. 2025. doi:10.1007\/s10278-025-01571-x. Epub ahead of print. [Link<\/a>]<\/li>\n\n\n\n
  7. Yamada T, Yoshimura T, Ichikawa S<\/strong>, Sugimori H. Improving Cerebrovascular Imaging with Deep Learning: Semantic Segmentation for Time-of-Flight Magnetic Resonance Angiography Maximum Intensity Projection Image Enhancement. Applied Sciences. 2025;15(6):3034. [Link<\/a>]<\/li>\n\n\n\n
  8. Ichikawa S<\/strong>*, Kondo Y, Okamoto M, Kondo T, Takahashi N. Automated Prediction of Thoracic Vertebral Body Diameters from Computed Tomography Scans Using Deep Learning. Journal of Health Sciences of Niigata University. 2025;21:10-20. [Link<\/a>]<\/li>\n\n\n\n
  9. Inomata S, Yoshimura T, Tang M, Ichikawa S<\/strong>, Sugimori H. Automatic Aortic Valve Extraction Using Deep Learning with Contrast-enhanced Cardiac CT Images. J Cardiovasc Dev Dis. 2025;12(1):3. [Link<\/a>]<\/li>\n\n\n\n
  10. Ichikawa H, Ichikawa S<\/strong>*, Sawane Y. Machine learning-based estimation of patient body weight from radiation dose metrics in computed tomography. J Appl Clin Med Phys. 2024:e14467. [Link<\/a>]<\/li>\n\n\n\n
  11. Sakaida M, Yoshimura T, Tang M, Ichikawa S<\/strong>, Sugimori H, Hirata K, Kudo K. The Effectiveness of Semi-Supervised Learning Techniques in Identifying Calcifications in X-ray Mammography and the Impact of Different Classification Probabilities. Applied Sciences. 2024;14(14):5968. [Link<\/a>]<\/li>\n\n\n\n
  12. Ichikawa S<\/strong>*, Ozaki M, Itadani H, Sugimori H, Kondo Y. Deep learning-based correction for time truncation in cerebral computed tomography perfusion. Radiol Phys Technol. 2024;17(3):666-678. [Link<\/a>]<\/li>\n\n\n\n
  13. Moriya R, Yoshimura T, Tang M, Ichikawa S<\/strong>, Sugimori H. Development of a Method for Estimating the Angle of Lumbar Spine X-ray Images Using Deep Learning with Pseudo X-ray Images Generated from Computed Tomography. Applied Sciences. 2024;14(9):3794. [Link<\/a>]<\/li>\n\n\n\n
  14. Ichikawa S<\/strong>, Sugimori H. Estimating Body Weight From Measurements From Different Single-Slice Computed Tomography Levels: An Evaluation of Total Cross-Sectional Body Area Measurements and Deep Learning. J Comput Assist Tomogr. 2024;48(3):424-431. [Link<\/a>]<\/li>\n\n\n\n
  15. Ozaki M, Ichikawa S<\/strong>, Fukunaga M, Yamamoto H. Improving the depiction of small intracranial vessels in head computed tomography angiography: a comparative analysis of deep learning reconstruction and hybrid iterative reconstruction. Radiol Phys Technol. 2024;17(1):329-336. [Link<\/a>]<\/li>\n\n\n\n
  16. Sakaida M, Yoshimura T, Tang M, Ichikawa S<\/strong>, Sugimori H. Development of a Mammography Calcification Detection Algorithm Using Deep Learning with Resolution-Preserved Image Patch Division. Algorithms. 2023;16(10):483. [Link<\/a>]<\/li>\n\n\n\n
  17. Inomata S, Yoshimura T, Tang M, Ichikawa S<\/strong>, Sugimori H. Estimation of Left and Right Ventricular Ejection Fractions from cine-MRI Using 3D-CNN. Sensors (Basel). 2023;23(14):6580. [Link<\/a>]<\/li>\n\n\n\n
  18. Ichikawa S<\/strong>, Itadani H, Sugimori H. Deep learning-based body weight from scout images can be an alternative to actual body weight in CT radiation dose management. J Appl Clin Med Phys. 2023;24(8):e14080. [Link<\/a>]<\/li>\n\n\n\n
  19. Usui K, Yoshimura T, Ichikawa S<\/strong>, Sugimori H. Development of Chest X-ray Image Evaluation Software Using the Deep Learning Techniques. Applied Sciences. 2023;13(11):6695. [Link<\/a>]<\/li>\n\n\n\n
  20. Ichikawa S<\/strong>, Sugimori H, Ichijiri K, Yoshimura T, Nagaki A. Acquisition time reduction in pediatric 99m<\/sup> Tc-DMSA planar imaging using deep learning. J Appl Clin Med Phys. 2023;24(6):e13978. [Link<\/a>]<\/li>\n\n\n\n
  21. Ichikawa S<\/strong>, Itadani H, Sugimori H. Prediction of body weight from chest radiographs using deep learning with a convolutional neural network. Radiol Phys Technol. 2023;16(1):127-134. [Link<\/a>]<\/li>\n\n\n\n
  22. Ichikawa S<\/strong>, Itadani H, Sugimori H. Toward automatic reformation at the orbitomeatal line in head computed tomography using object detection algorithm. Phys Eng Sci Med. 2022;45(3):835-845. [Link<\/a>]<\/li>\n\n\n\n
  23. Ichikawa S<\/strong>, Hamada M, Sugimori H. A deep-learning method using computed tomography scout images for estimating patient body weight. Sci Rep. 2021;11(1):15627. [Link<\/a>]<\/li>\n\n\n\n
  24. Ichikawa S<\/strong>*, Yamamoto H, Morita T. Comparison of a Bayesian estimation algorithm and singular value decomposition algorithms for 80-detector row CT perfusion in patients with acute ischemic stroke. Radiol Med. 2021;126(6):795-803. [Link<\/a>]<\/li>\n\n\n\n
  25. Ichikawa S<\/strong>*, Hamada M, Watanabe D, Ito O, Moriya T, Yamamoto H. Optimal slice thickness of brain computed tomography using a hybrid iterative reconstruction algorithm for identifying hyperdense middle cerebral artery sign of acute ischemic stroke. Emerg Radiol. 2021;28(2):309-315. [Link<\/a>]<\/li>\n\n\n\n
  26. Fukunaga M, Matsubara K, Ichikawa S<\/strong>, Mitsui H, Yamamoto H, Miyati T. CT dose management of adult patients with unknown body weight using an effective diameter. Eur J Radiol. 2021;135:109483. [Link<\/a>]<\/li>\n\n\n\n
  27. Ichikawa S<\/strong>*, Yamamoto H, Ito O, Fukunaga M. Pulmonary Artery\/Vein Separation Using Single-Phase Computed Tomography: Feasibility and the Influence of Patient Characteristics on Vessel Enhancement. J Thorac Imaging. 2020;35(3):173-178. [Link<\/a>]<\/li>\n\n\n\n
  28. Kato K, Yasojima N, Tamura K, Ichikawa S<\/strong>, Sutherland K, Kato M, Fukae J, Tanimura K, Tanaka Y, Okino T, Lu Y, Kamishima T. Detection of Fine Radiographic Progression in Finger Joint Space Narrowing Beyond Human Eyes: Phantom Experiment and Clinical Study with Rheumatoid Arthritis Patients. Sci Rep. 2019;9(1):8526. [Link<\/a>]<\/li>\n\n\n\n
  29. Kobayashi Y, Kamishima T, Sugimori H, Ichikawa S<\/strong>, Noguchi A, Kono M, Iiyama T, Sutherland K, Atsumi T. Quantification of hand synovitis in rheumatoid arthritis: Arterial mask subtraction reinforced with mutual information can improve accuracy of pixel-by-pixel time-intensity curve shape analysis in dynamic MRI. J Magn Reson Imaging. 2018;48(3);687-694. [Link<\/a>]<\/li>\n\n\n\n
  30. Okino T, Kamishima T, Lee Sutherland K, Fukae J, Narita A, Ichikawa S<\/strong>, Tanimura K. Radiographic temporal subtraction analysis can detect finger joint space narrowing progression in rheumatoid arthritis with clinical low disease activity. Acta Radiol. 2018;59(4):460-467. [Link<\/a>]<\/li>\n\n\n\n
  31. Kato K, Kamishima T, Kondo E, Onodera T, Ichikawa S<\/strong>. Quantitative knee cartilage measurement at MR imaging of patients with anterior cruciate ligament tear. Radiol Phys Technol. 2017;10(4):431-438. [Link<\/a>]<\/li>\n\n\n\n
  32. Ichikawa S<\/strong>, Kamishima T, Sutherland K, Fukae J, Katayama K, Aoki Y, Okubo T, Okino T, Kaneda T, Takagi S, Tanimura K. Computer-Based Radiographic Quantification of Joint Space Narrowing Progression Using Sequential Hand Radiographs: Validation Study in Rheumatoid Arthritis Patients from Multiple Institutions. J Digit Imaging. 2017;30(5):648-656. [Link<\/a>]<\/li>\n\n\n\n
  33. Fujimori M, Nakamura S, Hasegawa K, Ikeno K, Ichikawa S<\/strong>, Sutherland K, Kamishima T. Cartilage quantification using contrast-enhanced MRI in the wrist of rheumatoid arthritis: cartilage loss is associated with bone marrow edema. Br J Radiol. 2017;90(1077):20170167. [Link<\/a>]<\/li>\n\n\n\n
  34. Ichikawa S<\/strong>, Kamishima T, Sutherland K, Kasahara H, Shimizu Y, Fujimori M, Yasojima N, Ono Y, Kaneda T, Koike T. Semi-Automated Quantification of Finger Joint Space Narrowing Using Tomosynthesis in Patients with Rheumatoid Arthritis. J Digit Imaging. 2017;30(3):369-375. [Link<\/a>]<\/li>\n\n\n\n
  35. Hatano K, Kamishima T, Sutherland K, Kato M, Nakagawa I, Ichikawa S<\/strong>, Kawauchi K, Saitou S, Mukai M. A reliability study using computer-based analysis of finger joint space narrowing in rheumatoid arthritis patients. Rheumatol Int. 2017;37(2):189-195. [Link<\/a>]<\/li>\n\n\n\n
  36. Ichikawa S<\/strong>, Kamishima T, Sutherland K, Okubo T, Katayama K. Radiographic quantifications of joint space narrowing progression by computer-based approach using temporal subtraction in rheumatoid wrist. Br J Radiol. 2016;89(1057):20150403. [Link<\/a>]<\/li>\n\n\n\n
  37. Ichikawa S<\/strong>, Kamishima T, Sutherland K, Okubo T, Katayama K. Performance of computer-based analysis using temporal subtraction to assess joint space narrowing progression in rheumatoid patients. Rheumatol Int. 2016;36(1):101-8. [Link<\/a>]<\/li>\n<\/ol>\n\n\n\n

    \u548c\u6587\u5b66\u8853\u8ad6\u6587<\/strong><\/h2>\n\n\n\n
      \n
    1. \u5e02\u5ddd \u7fd4\u592a<\/strong>, \u5c3e\u5d0e \u8aa0, \u677f\u8c37 \u82f1\u6a39, \u6749\u68ee \u535a\u884c, \u8fd1\u85e4 \u4e16\u7bc4. RPT\u8a8c\u571f\u4e95\u8cde\u53d7\u8cde\u8ad6\u6587\uff1a\u6df1\u5c64\u5b66\u7fd2\u306b\u57fa\u3065\u304f\u8133CT\u704c\u6d41\u753b\u50cf\u306e\u6642\u9593\u7684\u30c8\u30e9\u30f3\u30b1\u30fc\u30b7\u30e7\u30f3\u88dc\u6b63, \u533b\u5b66\u7269\u7406, 2025;45(1):3. [Link<\/a>]<\/li>\n\n\n\n
    2. \u4f50\u85e4 \u5145, \u661f\u91ce \u6d0b\u6e80, \u6e05\u6c34 \u6b63\u6319, \u5927\ufa11 \u6d0b\u5145, \u5c0f\u5009 \u654f\u88d5, \u5e02\u5ddd \u7fd4\u592a<\/strong>, \u8fd1\u85e4 \u9054\u4e5f, \u5ca1\u672c \u660c\u58eb. \u8907\u5408\u73fe\u5b9f\u88c5\u7f6e\u3092\u7528\u3044\u305f\u60a3\u8005\u3078\u306e\u653e\u5c04\u6027\u533b\u85ac\u54c1\u8aa4\u6295\u4e0e\u9632\u6b62\u306e\u305f\u3081\u306e\u60a3\u8005\u2013\u653e\u5c04\u6027\u533b\u85ac\u54c1\u7167\u5408\u30b7\u30b9\u30c6\u30e0\u306e\u958b\u767a\u2014\u8907\u5408\u73fe\u5b9f\u88c5\u7f6e\u642d\u8f09\u30ab\u30e1\u30e9\u3067\u53d6\u5f97\u3057\u305f\u6620\u50cf\u3092\u57fa\u306b\u3057\u305f\u6df1\u5c64\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u958b\u767a\u2014, RADIOISOTOPES, 2025;74(1):25-37. [Link<\/a>]<\/li>\n\n\n\n
    3. \u5c71\u5d0e \u82b3\u88d5, \u5e02\u5ddd \u7fd4\u592a<\/strong>. \u5927\u5b66\u6559\u80b2\u6a5f\u95a2\u5185\u306b\u304a\u3051\u308b\u544a\u793a\u7814\u4fee\u306e\u5bfe\u5fdc\u306b\u3064\u3044\u3066\uff0e\u65e5\u672c\u653e\u5c04\u7dda\u6280\u5e2b\u6559\u80b2\u5b66\u4f1a\u8ad6\u6587\u8a8c\uff0c2024;14(1):27-31.<\/li>\n\n\n\n
    4. \u5e02\u5ddd \u8087 , \u4f0a\u85e4 \u60e0\u671b, \u677e\u539f \u5b5d\u7950, \u5e02\u5ddd \u7fd4\u592a<\/strong>, \u52a0\u85e4 \u8c4a\u5927, \u6fa4\u6839 \u5eb7\u88d5, \u52a0\u85e4 \u5927\u8cb4. \u5229\u7528\u76ee\u7684\u306e\u7570\u306a\u308bCT\u88c5\u7f6e\u306b\u304a\u3051\u308b\u30d5\u30a1\u30f3\u30c8\u30e0\u3092\u7528\u3044\u305fEffective Diameter\u304a\u3088\u3073Water Equivalent Diameter\u306e\u7cbe\u5ea6\u8a55\u4fa1. \u65e5\u672c\u653e\u5c04\u7dda\u6280\u8853\u5b66\u4f1a\u96d1\u8a8c, 2024;80(11):1115-1123. [Link<\/a>]<\/li>\n\n\n\n
    5. \u4f50\u85e4 \u5145, \u661f\u91ce \u6d0b\u6e80, \u6e05\u6c34 \u6b63\u6319, \u5927\ufa11 \u6d0b\u5145, \u5e02\u5ddd \u7fd4\u592a<\/strong>, \u8fd1\u85e4 \u9054\u4e5f, \u5ca1\u672c\u660c\u58eb. \u30b9\u30de\u30fc\u30c8\u30d5\u30a9\u30f3\u3092\u7528\u3044\u305f\u8aa4\u6295\u4e0e\u9632\u6b62\u306e\u305f\u3081\u306e\u60a3\u8005\u2013\u653e\u5c04\u6027\u533b\u85ac\u54c1\u7167\u5408\u30b7\u30b9\u30c6\u30e0\u306e\u958b\u767a\u2014\u5b9f\u73fe\u53ef\u80fd\u6027\u691c\u8a0e\u2014. RADIOISOTOPES, 2024;73(1):69-80. [Link<\/a>]<\/li>\n\n\n\n
    6. \u798f\u6c38 \u6b63\u660e, \u677e\u539f \u5b5d\u7950, \u7af9\u4e95 \u6cf0\u5b5d, \u5149\u4e95 \u82f1\u6a39, \u4e80\u4e95\u5c71 \u5f18\u6643, \u7530\u4e2d \u5b5d\u5c1a, \u5e02\u5ddd \u7fd4\u592a<\/strong>. \u79fb\u52d5\u578b\u6574\u5f62\u5916\u79d1\u7528\u30a4\u30e1\u30fc\u30b8\u30f3\u30b0\u88c5\u7f6e\u3092\u7528\u3044\u305f \u8170\u690e\u5f8c\u65b9\u56fa\u5b9a\u8853\u6642\u306b\u304a\u3051\u308b\u5ba4\u5185\u6563\u4e71\u7dda\u91cf\u5206\u5e03\u306e\u6e2c\u5b9a. \u65e5\u672c\u653e\u5c04\u7dda\u6280\u8853\u5b66\u4f1a\u96d1\u8a8c, 2020;76(6):572-578. [Link<\/a>]<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

      \u6700\u65b0\u60c5\u5831\u306f\u3053\u3061\u3089\u304b\u3089https:\/\/researchmap.jp\/sichikawa \u6b27\u6587\u5b66\u8853\u8ad6\u6587\uff08*: corresponding author) \u548c\u6587\u5b66\u8853\u8ad6\u6587<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"vkexunit_cta_each_option":"","footnotes":""},"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.clg.niigata-u.ac.jp\/~sichikawa\/wp-json\/wp\/v2\/pages\/16"}],"collection":[{"href":"https:\/\/www.clg.niigata-u.ac.jp\/~sichikawa\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.clg.niigata-u.ac.jp\/~sichikawa\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.clg.niigata-u.ac.jp\/~sichikawa\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.clg.niigata-u.ac.jp\/~sichikawa\/wp-json\/wp\/v2\/comments?post=16"}],"version-history":[{"count":41,"href":"https:\/\/www.clg.niigata-u.ac.jp\/~sichikawa\/wp-json\/wp\/v2\/pages\/16\/revisions"}],"predecessor-version":[{"id":473,"href":"https:\/\/www.clg.niigata-u.ac.jp\/~sichikawa\/wp-json\/wp\/v2\/pages\/16\/revisions\/473"}],"wp:attachment":[{"href":"https:\/\/www.clg.niigata-u.ac.jp\/~sichikawa\/wp-json\/wp\/v2\/media?parent=16"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}