Khamene Ali, Bloch Peter, Wein Wolfgang, Svatos Michelle, Sauer Frank
Imaging and Visualization Department, Siemens Corporate Research, Inc., 755 College Road East, Princeton, NJ 08540, USA.
Med Image Anal. 2006 Feb;10(1):96-112. doi: 10.1016/j.media.2005.06.002. Epub 2005 Sep 8.
The efficacy of radiation therapy treatment depends on the patient setup accuracy at each daily fraction. A significant problem is reproducing the patient position during treatment planning for every fraction of the treatment process. We propose and evaluate an intensity based automatic registration method using multiple portal images and the pre-treatment CT volume. We perform both geometric and radiometric calibrations to generate high quality digitally reconstructed radiographs (DRRs) that can be compared against portal images acquired right before treatment dose delivery. We use a graphics processing unit (GPU) to generate the DRRs in order to gain computational efficiency. We also perform a comparative study on various similarity measures and optimization procedures. Simple similarity measure such as local normalized correlation (LNC) performs best as long as the radiometric calibration is carefully done. Using the proposed method, we achieved better than 1mm average error in repositioning accuracy for a series of phantom studies using two open field (i.e., 41 cm2) portal images with 90 degrees vergence angle.
放射治疗的疗效取决于每日每次分割时患者摆位的准确性。一个重大问题是在治疗过程的每个分割的治疗计划期间重现患者位置。我们提出并评估一种基于强度的自动配准方法,该方法使用多个射野图像和治疗前CT容积。我们进行几何和辐射校准以生成高质量的数字重建射线照片(DRR),其可与就在治疗剂量交付前采集的射野图像进行比较。我们使用图形处理单元(GPU)来生成DRR以提高计算效率。我们还对各种相似性度量和优化程序进行了比较研究。只要仔细进行辐射校准,诸如局部归一化互相关(LNC)这样的简单相似性度量表现最佳。使用所提出的方法,对于一系列使用两个开放射野(即41平方厘米)、发散角为90度的射野图像的体模研究,我们在重新定位精度方面实现了优于1毫米的平均误差。