Li Dengwang, Yin Yong
College of Physics and Electronics, Shandong Normal University, China.
Department of Radiation Oncology, Shandong Tumor Hospital and Institute, China, jinan.
Med Phys. 2012 Jun;39(6Part7):3672-3673. doi: 10.1118/1.4734923.
In order to register 4DCT efficiently, we propose an improved deformable registration algorithm based on improved multi-resolution demons strategy to improve the efficiency of the algorithm.
4DCT images of lung cancer patients are collected from a General Electric Discovery ST CT scanner from our cancer hospital. All of the images are sorted into groups and reconstructed according to their phases, and eachrespiratory cycle is divided into 10 phases with the time interval of 10%. Firstly, in our improved demons algorithm we use gradients of both reference and floating images as deformation forces and also redistribute the forces according to the proportion of the two forces. Furthermore, we introduce intermediate variable to cost function for decreasing the noise in registration process. At the same time, Gaussian multi-resolution strategy and BFGS method for optimization are used to improve speed and accuracy of the registration. To validate the performance of the algorithm, we register the previous 10 phase-images. We compared the difference of floating and reference images before and after registered where two landmarks are decided by experienced clinician. We registered 10 phase-images of 4D-CT which is lung cancer patient from cancer hospital and choose images in exhalationas the reference images, and all other images were registered into the reference images.
This method has a good accuracy demonstrated by a higher similarity measure for registration of 4D-CT and it can register a large deformation precisely. Finally, we obtain the tumor target achieved by the deformation fields using proposed method, which is more accurately than the internal margin (IM) expanded by the Gross Tumor Volume (GTV). Furthermore, we achieve tumor and normal tissue tracking and dose accumulation using 4DCT data.
An efficient deformable registration algorithm was proposed by using multi-resolution demons algorithm for 4DCT.
为了高效地对4DCT进行配准，我们提出一种基于改进的多分辨率 demons 策略的改进型可变形配准算法，以提高算法效率。
从我院癌症医院的通用电气 Discovery ST CT 扫描仪收集肺癌患者的4DCT图像。所有图像按相位分组并重建，每个呼吸周期分为10个相位，时间间隔为10%。首先，在我们改进的 demons 算法中，我们使用参考图像和浮动图像的梯度作为变形力，并根据两种力的比例重新分配力。此外，我们在代价函数中引入中间变量以减少配准过程中的噪声。同时，使用高斯多分辨率策略和 BFGS 优化方法来提高配准的速度和准确性。为了验证算法的性能，我们对前10个相位图像进行配准。我们比较了由经验丰富的临床医生确定两个地标点的浮动图像和参考图像在配准前后的差异。我们对我院癌症医院的一名肺癌患者的4D-CT的10个相位图像进行配准，并选择呼气时的图像作为参考图像，将所有其他图像配准到参考图像中。
该方法通过对4D-CT配准的更高相似性度量证明具有良好的准确性，并且能够精确地配准大变形。最后，我们使用所提出的方法获得了通过变形场实现的肿瘤靶点，其比由大体肿瘤体积（GTV）扩展的内部边界（IM）更准确。此外，我们使用4DCT数据实现了肿瘤和正常组织的跟踪以及剂量累积。
提出了一种使用多分辨率 demons 算法对4DCT进行高效可变形配准的算法。
