Girouard L M, Pouliot J, Maldague X, Zaccarin A
Department of Radiation Oncology, Centre Hospitalier Universitaire de Québec, Canada.
Med Phys. 1998 Jul;25(7 Pt 1):1180-5. doi: 10.1118/1.598296.
The purpose of this work was to develop a fully automatic tool for the detection of setup deviation for small pelvic field using, in external beam radiotherapy, an electronic portal imaging device (EPID). The algorithm processes electronic portal images of prostate cancer patients. No fiducial points or user interventions are needed. Deviation measurements are based on bone edge detection performed with Laplacian of a Gaussian (LoG) operator. Two bone edge images are then correlated, one of which is a reference image taken as the first fraction image for the purpose of this study. The electronic portal images (EPI) also show band artefacts which are removed using the morphological top-hat transform. The algorithm was first validated with 59 phantom images acquired in clinical treatment conditions with known displacements. The algorithm was then validated with 79 clinical images where bone contours were delineated manually. For the phantom images, the setup deviations were measured with a absolute mean error of 0.59 mm and 0.47 mm with a standard deviation of 0.64 mm and 0.42 mm, horizontally and vertically, respectively. A second validation was performed using clinical prostate cancer images. The measured patient displacements have an absolute mean error of 0.48 mm and 1.41 mm with a standard deviation of 0.58 mm and 1.30 mm in the X and Y directions, respectively. The algorithm execution time on a SUN workstation is 5 s. This algorithm shows good potential as a setup deviation measurement tool in clinical practice. The possibility of using this algorithm combined with decision rules based on statistical observations is very promising.
这项工作的目的是开发一种全自动工具，用于在外照射放射治疗中使用电子门静脉成像设备（EPID）检测小盆腔野的摆位偏差。该算法处理前列腺癌患者的电子门静脉图像。无需基准点或用户干预。偏差测量基于使用高斯拉普拉斯算子（LoG）进行的骨边缘检测。然后将两个骨边缘图像进行关联，其中一个是为本研究目的作为首次分次照射图像获取的参考图像。电子门静脉图像（EPI）还显示带状伪影，使用形态学顶帽变换将其去除。该算法首先用在已知位移的临床治疗条件下获取的59幅体模图像进行验证。然后用79幅手动勾勒出骨轮廓的临床图像对该算法进行验证。对于体模图像，水平和垂直方向的摆位偏差测量的绝对平均误差分别为0.59 mm和0.47 mm，标准差分别为0.64 mm和0.42 mm。使用临床前列腺癌图像进行了第二次验证。在X和Y方向上，测量的患者位移的绝对平均误差分别为0.48 mm和1.41 mm，标准差分别为0.58 mm和1.30 mm。该算法在SUN工作站上的执行时间为5秒。该算法在临床实践中作为摆位偏差测量工具显示出良好的潜力。将该算法与基于统计观察的决策规则相结合的可能性非常有前景。
