Automated and robust estimation framework for lung tumor location in kilovolt cone-beam computed tomography images for target-based patient positioning in lung stereotactic body radiotherapy
Automated and robust estimation framework for lung tumor location in kilovolt cone-beam computed tomography images for target-based patient positioning in lung stereotactic body radiotherapy
复制标题
千伏锥形束计算机断层扫描图像中肺部肿瘤位置的自动稳健估计框架，用于肺部立体定向放射治疗中基于目标的患者定位
DOI：
10.11318/mii.35.48
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Hiroshi Honda
中科院分区：
文献类型：
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作者：
Satoshi Yoshidome;Hidetaka Arimura;Kotaro Terashima;Masakazu Hirakawa;Taka-aki Hirose;Junichi Fukunaga;Yasuhiko Nakamura;Hiroshi Honda
使用千伏锥束计算机断层扫描（kV-CBCT）图像的图像引导放射治疗（IGRT）系统通常用于肺立体定向体部放射治疗（SBRT）中的高精度患者定位。然而，目前的IGRT程序是基于骨结构和主观矫正。因此，本研究的目的是研究kV-CBCT图像中肺肿瘤位置的自动化和鲁棒估计框架，以改善肺SBRT的基于靶点的患者定位。使用来自40例SBRT治疗的临床病例的160 kV-CBCT图像。所提出的框架包括四个步骤，即，确定的搜索区域，提取的肿瘤模板，预处理增强的肿瘤区域，和估计的肿瘤位置的模板匹配技术。通过基于每种形式的预处理的肿瘤区域的增强获得原始、边缘增强和肿瘤增强图像，并用于模板匹配。原始图像、边缘增强图像和肿瘤增强图像定位误差的平均欧氏距离分别为1.2±0.7 mm、5.5±10.1 mm和2.7±4.4 mm。这些发现表明，所提出的自动化框架可能是稳健的，用于估计肺SBRT的kV-CBCT图像中肺肿瘤的位置。
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, ie, 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.