Abstract: To acquire an accurate patient positioning in image-guided radiotherapy, an improved Demons deformable registration method was developed. The FDK algorithm was adopted to reconstruct Cone Beam CT (CBCT) and the reconstruction result was visualized by a volume rendering method with Visualization ToolKit (VTK). Based on the Insight segmentation and registration ToolKit (ITK), the Demons algorithm was completed incorporating the gradient information of fixed image and floating image by the concept of symmetric gradient, and a new formula of Demons force was demonstrated. Registrion experiments were carried out using medical images both from single modality and multi-modality. The results show that the improved Demons algorithm achieves a faster convergence speed and a higher precision compared with the original demons algorithm, which indicates that the Demons algorithm based on symmetric gradient is more suitable for the registration of CBCT reconstruction image and CT plan image in image-guided radiotherapy.

Key words: image-guided radiotherapy, image registration, Demons algorithm, medical image, three-dimensional reconstruction, symmetric gradient

摘要： 为了确定病人的摆位误差,实现精确放疗,提出一种改进的Demons弹性配准算法。采用FDK算法对锥形束CT(CBCT)图像进行三维重建,利用可视化工具包 (VTK)体绘制法可视化重建结果;在分割与配准工具包 (ITK)基础上实现Demons算法,并基于对称梯度的思想,将参考图像和浮动图像的梯度场信息加入到Demons算法中,给出新的Demons形变力公式。分别使用单模态和多模态医学图像进行配准实验,结果显示改进的Demons算法与原始Demons算法相比,配准速度更快、精度更高。基于对称梯度的Demons算法更适用于图像引导放射治疗中CBCT重建图像与CT计划图像间的配准。

关键词: 图像引导放射治疗, 图像配准, Demons算法, 医学影像, 三维重建, 对称梯度