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呼吸モデルに基づく胸部CT 画像の位置合わせ方法の検討 Study on Chest CT image registration based on respiration model 今村, 貴志 Imamura, Takashi 永井, 優一 Nagai, Yuuichi 武尾, 英哉 Takeo, Hideya Chest CT image Respiration simulation application/pdf We have developed the respiration model to estimate position change of chest skeleton structure by respiration, using 2 chest CT images (learning image) consist of different air-intake volume of same patient. Average distances between real chest skeleton structures and virtual chest skeleton structures obtained by performing respiration simulation using another patient's chest CT images (test image) were 1.43cm at simple expansion model and 0.84cm at proposal respiration model. We have also developed the automatically and rapid chest skeleton structure extraction algorithm and performed same simulation. Using this algorithm, average distances were 1.13cm at simple expansion model and 0.80cm at proposal respiration model on learning image, and 1.57cm at simple expansion model and 0.92cm at proposal respiration model on test image. Average distances between truth feature-points of chest skeleton structure and automatically extracted feature-points (8 feature-points a image) were 1.41cm on learning image and 1.01cm on test image, and required processing times for extracting feature-points were 1.01sec on learning image (512x512x36lpixel) and 1.28sec on test image (512x512x431 pixel). 神奈川工科大学 2010-03-20 jpn departmental bulletin paper VoR https://doi.org/10.34411/00001044 http://hdl.handle.net/10368/21400 https://kait.repo.nii.ac.jp/records/1051 10.34411/00001044 AN10074179 09161902 神奈川工科大学研究報告.B,理工学編 34 1 8 https://kait.repo.nii.ac.jp/record/1051/files/kkb-034-001.pdf application/pdf 2.8 MB 2012-03-01