Li Jun, Tang Xiao-Bin, Zhang Xi-Zhi, Zhang Xian-Wen, Ge Yun, Chen Da, Chai Lei
Department of Nuclear Science & Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, P. R. China.
Radiotherapy Center, Subei People's Hospital of Jiangsu province, Yangzhou, P. R. China.
J Xray Sci Technol. 2016 Apr 7;24(4):521-30. doi: 10.3233/XST-160568.
This study aimed to investigate the feasibility of efficiently using a rigid image registration (RIR) algorithm or a deformable image registration (DIR) algorithm to match medical images and evaluate the impact of setup errors on intensity modulated radiation therapy of lung cancer patients.
Ten lung cancer patients were chosen randomly each day and were subjected to image-guided radiotherapy. The clinical registration between cone-beam computed tomography (CBCT) images and treatment planning system CT images was performed by applying both RIR and DIR; the clinical registration was evaluated on the basis of the contour index, including dice similarity coefficient, sensitivity, and positive predictive value; the optimal scheme of image registration was selected to ensure that the actual irradiation isocenter was consistent with the treatment planning isocenter. In each patient, the translational errors in the right-left (x), superior-inferior (y), and anterior-posterior (z) directions and the rotational errors in the u, υ, and w directions formed by the x, y, and z directions were calculated and analyzed daily in the whole course of treatment; margins were calculated according to this equation: M = 2.5∑+ 0.7δ.
The tumors and the surrounding soft tissues of the patients are shown more clearly in the CBCT images than in the CT images. DIR can be applied more efficiently than RIR to determine the morphological and positional changes in the organs shown in the images with the same or different modalities in the different period. The setup errors in translation in the x, y and z axes were 0.05±0.16, 0.09±0.32 and -0.02±0.13 cm, respectively; by contrast, the setup errors in rotation in u, υ and w directions were (0.41±0.64)°, (-0.08±0.57)° and (-0.03±0.62)°, respectively. The setup errors in the x, y and z axes of the patients indicated that the margins expansions were 0.82, 1.15 and 0.72 cm, respectively.
CBCT with DIR can measure and correct the setup errors online; as a result, setup errors in lung cancer treatments can be significantly reduced and the accuracy of radiotherapy can be enhanced.
本研究旨在探讨有效使用刚性图像配准（RIR）算法或可变形图像配准（DIR）算法匹配医学图像的可行性，并评估摆位误差对肺癌患者调强放射治疗的影响。
每天随机选择10例肺癌患者并进行图像引导放射治疗。通过应用RIR和DIR对锥束计算机断层扫描（CBCT）图像与治疗计划系统CT图像进行临床配准；基于轮廓指数评估临床配准，轮廓指数包括骰子相似系数、灵敏度和阳性预测值；选择图像配准的最佳方案以确保实际照射等中心与治疗计划等中心一致。在每位患者的整个治疗过程中，每天计算并分析其在左右（x）、上下（y）和前后（z）方向的平移误差以及由x、y和z方向形成的u、υ和w方向的旋转误差；根据以下公式计算边界：M = 2.5∑ + 0.7δ。
与CT图像相比，CBCT图像能更清晰地显示患者的肿瘤及周围软组织。在确定不同时期相同或不同模态图像中所示器官的形态和位置变化方面，DIR比RIR应用更高效。x、y和z轴的平移摆位误差分别为0.05±0.16、0.09±0.32和 -0.02±0.13 cm；相比之下，u、υ和w方向的旋转摆位误差分别为（0.41±0.64）°、（-0.08±0.57）°和（-0.03±0.62）°。患者x、y和z轴的摆位误差表明边界扩展分别为0.82、1.15和0.72 cm。
采用DIR的CBCT可在线测量并校正摆位误差；因此，可显著减少肺癌治疗中的摆位误差并提高放射治疗的准确性。
