变参数active demons算法下的多通道弥散张量图像配准  

Multi-channel diffusion tensor imaging registration method based on active demons algorithm by using variable parameters

作　　者：赵杰[1,2,3] 徐晓莹 刘敬 杜宇航 Zhao Jie;Xu Xiaoying;Liu Jing;Du Yuhang(College of Electronic and Information Engineering,Hebei University,Baoding 071000,China;Key Laboratory of Digital Medical Engineering of Hebei Province,Baoding 071000,China;Machine Vision Engineering Technology Research Center of Hebei Province,Baoding 071000,China)

机构地区：[1]河北大学电子信息工程学院,保定071000 [2]河北省数字医疗工程重点实验室,保定071000 [3]河北省机器视觉工程技术研究中心,保定071000

出　　处：《中国图象图形学报》2019年第1期103-114,共12页Journal of Image and Graphics

基　　金：河北省自然科学基金项目(F2016201187);河北大学"一省一校"专项经费~~

摘　　要：目的弥散张量图像(DTI)配准不仅要保证配准前后图像解剖结构的一致性,还要保持张量方向的一致性。demons算法下的多通道DTI配准方法可充分利用张量的信息,改善配准质量,但大形变区域配准效果不理想,收敛速度慢。active demons算法能够加快收敛速度,但图像的拓扑结构容易改变。由此提出一种变参数active demons算法下的多通道DTI配准方法。方法综合active demons算法中平衡系数能加快收敛速度、均化系数能提高DTI配准精度的优点,手动选择一个均化系数,并在算法收敛过程中随着高斯核的减小动态调整平衡系数。在配准开始时采用较小的平衡系数获得较快的收敛速度,随着收敛的加深逐渐增大平衡系数获得较小的配准误差。结果active demons方法能改善DTI大形变区域的配准问题,但均化系数太小会改变图像拓扑结构。固定均化系数,引入单一的平衡系数能加快收敛速度,但会导致拓扑结构改变。变参数active demons方法有效提高了配准的收敛速度,明显改善大形变区域的配准效果,同时能保持图像拓扑结构不变。变参数active demons配准后的10组数据均获得最小均方差(MSE)和最大特征值特征向量对重叠率(OVL),配准精度最高。在0. 05的配对样本t检验水平下,变参数active demons和active demons方法配准后的MSE、OVL的差异均有统计学意义;变参数active demons和demons方法配准后的MSE、OVL的差异均有统计学意义(p <0. 05)。结论变参数active demons算法下的多通道DTI配准方法明显提高了配准精度和速度,改善了demons方法不能有效配准大形变区域的问题,同时能够保持配准前后图像的拓扑结构,尤其适合个体间形变较大的DTI配准。Objective Diffusion tensor imaging (DTI) is widely recognized as the most attractive non-invasive magnetic resonance imaging method. DTI is sensitive to subtle differences in the orientation of white matter fiber and diffuse anisotropy. Hence, it is a powerful method studying brain diseases and group research, such as Alzheimer's disease, Parkinson's disease, and multiple sclerosis. DTI registration is a prerequisite for these studies, and its effect will directly affect the reliability and completeness of the follow-up medical research and clinical diagnosis. DT images contain many information about the direction of brain white matter fibers. DTI registration not only requires the consistency of the anatomy between the reference and the moving image after registration but also demands consistency between the diffusion tensor direction and the anatomic structure. The DTI registration based on demons algorithm, which uses the six independent components of the tensor as inputs, can fully use the direction information of the diffusion tensor data and improve the quality of registration. However, this algorithm does not perform well in the large deformation area, and its convergence speed is slow. The active demons algorithm can accelerate the convergence to some extent, but the internal structure of the moving image is prone to being teared, deformed, and folded due to the presence of false demons force, which can alter the topological structure of the moving image. To solve these problems, this paper proposes a multi-channel DTI registration method based on active demons algorithm by using variable parameters. Method The active demons algorithm is introduced into the multi-channel DTI registration. By analyzing the influence of the homogeneous and the balance coefficient in the active demons algorithm on the DTI registration and combining the advantages of the balance coefficient of improving the convergence speed and that of homogeneous coefficient of enhancing the accuracy of the multi-channel DTI registration, a

关 键 词：弥散张量成像 DTI配准 DEMONS算法 ACTIVE 张量重定向 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]