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一种用于下肢串联式 SPECT/CT 混合成像的非刚性配准方法。

A non-rigid registration method for serial lower extremity hybrid SPECT/CT imaging.

机构信息

Department of Diagnostic Radiology, Yale University, New Haven, CT, USA.

出版信息

Med Image Anal. 2011 Feb;15(1):96-111. doi: 10.1016/j.media.2010.08.002. Epub 2010 Sep 24.

Abstract

Small animal X-ray computed tomographic (microCT) imaging of the lower extremities permits evaluation of arterial growth in models of hindlimb ischemia, and when applied serially can provide quantitative information about disease progression and aid in the evaluation of therapeutic interventions. The quantification of changes in tissue perfusion and concentration of molecular markers concurrently obtained using nuclear imaging requires the ability to non-rigidly register the microCT images over time, a task made more challenging by the potentially large changes in the positions of the legs due to articulation. While non-rigid registration methods have been extensively used in the evaluation of individual organs, application in whole body imaging has been limited, primarily because the scale of possible displacements and deformations is large resulting in poor convergence of most methods. In this paper we present a new method based on the extended demons algorithm that uses a level-set representation of the body contour and skeletal structure as an input. The proposed serial registration method reflects the natural physical moving combination of mouse anatomy in which the movement of bones is the framework for body movements, and the movement of skin constrains the detailed movements of the specific segmented body regions. We applied our method to both the registration of serial microCT mouse images and the quantification of microSPECT component of the serially hybrid microCT-SPECT images demonstrating improved performance as compared to existing registration techniques.

摘要

小动物 X 射线计算机断层(microCT)成像可用于评估后肢缺血模型中的动脉生长,并且当连续应用时,可以提供有关疾病进展的定量信息,并有助于评估治疗干预措施。使用核成像同时获得的组织灌注变化和分子标记物浓度的定量需要能够在时间上对 microCT 图像进行非刚性配准,由于关节运动,腿部位置可能发生很大变化,这使得该任务更加具有挑战性。虽然非刚性配准方法已广泛应用于单个器官的评估,但在全身成像中的应用受到限制,主要是因为可能的位移和变形的范围很大,导致大多数方法的收敛性较差。在本文中,我们提出了一种新的基于扩展 demons 算法的方法,该方法将身体轮廓和骨骼结构的水平集表示作为输入。所提出的串行配准方法反映了老鼠解剖结构的自然物理运动组合,其中骨骼的运动是身体运动的框架,皮肤的运动限制了特定分段身体区域的详细运动。我们将我们的方法应用于串行 microCT 小鼠图像的配准和串行杂交 microCT-SPECT 图像的 microSPECT 分量的定量,与现有的配准技术相比,该方法的性能得到了提高。

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