摘要
目的实现同一个体屏住呼吸状态下呼气相与吸气相的肺部高分辨率CT体积图像配准。方法采集3个被试者两个屏住呼吸状态下的胸部高分辨率CT序列,共3对。利用序列分割算法提取肺组织,并分开存储左右两肺。对单侧肺的呼气与吸气图像进行配准。首先,基于解剖标志面寻找全局仿射变换参数,用此变换重采样呼气相体积图像;其次,利用"Demons"算法对两体积图像进行非刚性配准。结果两肺的轮廓及内部结构均获得较好的配准效果。配准前,两图像的平均体积重合度为0.7982,经全局仿射变换后提高到0.8936,经"Demons"非刚性配准后增至0.9544。均方根误差值平均下降率为:19.83%(全局仿射变换之后),49.43%(Demons非刚性配准之后)。结论本文所采用的同体肺部图像的配准方法可以有效地配准两个大形变肺部体积图像,为进一步分析肺的呼吸功能奠定了良好的基础。
Objective To register two breath-hold lung volumes image from one subject with deep expiration and deep inspiration. Methods Three pairs of thoracic high resolution CT serial from three subjects were collected under two breath-hold respiration stages. The lung parenchyma of every serial was segmented using the serial segmentation algorithm. Left and right lungs were stored separately. Expiration and inspiration volume images of single lung were registered. Firstly, affine transformation parameters were found based on the anatomic flag surfaces and expiration image volume was re-sampled with affine transformation. Secondly, ＂Demons＂ algorithm was employed to register two image volumes non-rigidly. Results Two lung surfaces and the inner structures have a nice registration. The average volume overlap of two images before registration is 0. 7982. After global affine transformation, it improves to 0. 8936. After ＂Demons＂, it is up to 0. 9544. The average descending percentage of root mean square errors is 19.83% （after the global affine transformation） and 49.43% （after the ＂Demons＂ non-rigid registration）. Conclusion The intra-subject registration between two lung image volumes with large deformations described here has an effective registration result. It offers a good base to analyze the lung respiration function.
出处
《航天医学与医学工程》
CAS
CSCD
北大核心
2009年第3期198-204,共7页
Space Medicine & Medical Engineering
基金
国家自然科学基金(60771077)
关键词
高分辨率CT
同体图像配准
仿射变换
非刚性配准
肺
high resolution CT
intra-subject image registration
affine transformation
non-rigid registration
lung
