京都大学 / 0048 / 新制・課程博士 / 博士(人間健康科学) / 甲第22837号 / 人健博第79号 / 新制||人健||6(附属図書館) / 京都大学大学院医学研究科人間健康科学系専攻 / (主査)教授 岡 昌吾, 教授 藤井 康友, 教授 萩原 正敏 / 学位規則第4条第1項該当 / Doctor of Human Health Sciences / Kyoto University / DGAM

Cancer is one of the leading causes of death in the world, but advances in research and development of treatment methods is constantly ongoing to reduce the number of deaths and the amount of suffering. One of many approaches is radiation therapy, which uses high doses of radiation to kill tumors. Radiation therapy requires advanced software in image analysis to create careful treatment plans, evaluate treatment responses and to perform dose accumulation, among other things. One important tool for this is deformable image registration (DIR) which is used to find a correspondence between the images. The aim with this master thesis is to improve the DIR method ANACONDA by automatically provide additional information to the algorithm before the registration is performed.This work focuses on the registration of internal tubular structures in lung and liver images (bronchial and vascular tree, respectively). Two challenges in registering lung images are the sliding motion of lung surfaces and large motion of small internal structures. Several DIR methods have been proposed for solving the challenging internal structures, however most of them do not take into account the alignment of surrounding tissues. DIR methods applied to the liver are published less frequently, but accurate registration of the liver is of high interest since, for example, knowledge of the anatomy of the vascular tree is essential when removing tumors through liver surgeries. In this work, corresponding (anatomical) points are automatically found in two images and added to the DIR algorithm. The points are found by extracting and comparing the tubular structures between the images, and with use of different distance requirements, nearby points are paired.The new method manages to achieve good registration of both internal structures and surrounding tissue. Mean target registration errors for the internal structures of lungs was 1.11 ± 0.75 and for liver 1.67 ± 1.15 mm.