医用画像情報学会雑誌
Online ISSN : 1880-4977
Print ISSN : 0910-1543
最新号
選択された号の論文の3件中1~3を表示しています
依頼総説(特別講演 I)
依頼総説(特別講演 II)
原著論文
  • Satoshi Yoshidome, Hidetaka Arimura, Kotaro Terashima, Masakazu Hira ...
    2018 年 35 巻 3 号 p. 48-54
    発行日: 2018/09/28
    公開日: 2018/09/29
    ジャーナル 認証あり

    Image-guided radiotherapy (IGRT) systems using kilovolt cone-beam computed tomography (kV-CBCT) images are being commonly used for highly accurate patient positioning in lung stereotactic body radiotherapy (SBRT). However, current IGRT procedures are based on bone structure and subjective correction. Therefore, the purpose of this study was to investigate an automated and robust estimation framework for lung tumor location in kV-CBCT images to improve target-based patient positioning for lung SBRT. One-hundred-and-sixty kV-CBCT images from 40 clinical cases treated with SBRT were used. The proposed framework comprised four steps, i.e., determination of a search region,extraction of a tumor template, preprocessing for enhancement of the tumor region, and estimation of the tumor location by a template-matching technique. Original, edge enhancement, and tumor enhancement images were obtained by enhancement of a tumor region based on each form of preprocessing and were used for template matching. The mean Euclidean distances of location errors for original, edge enhancement, and tumor enhancement images were 1.2 ± 0.7 mm, 5.5 ± 10.1 mm and 2.7 ± 4.4 mm, respectively. These findings suggested that the proposed automated framework may be robust for estimating the location of lung tumors in kV-CBCT images for lung SBRT.

feedback
Top