Baiker Martin, Staring Marius, Löwik Clemens W G M, Reiber Johan H C, Lelieveldt Boudewijn P F
Div. of Image Processing, Leiden University Medical Center, The Netherlands.
Med Image Comput Comput Assist Interv. 2011;14(Pt 2):516-23. doi: 10.1007/978-3-642-23629-7_63.
In vivo MicroCT imaging of disease models at multiple time points is of great importance for preclinical oncological research, to monitor disease progression. However, the great postural variability between animals in the imaging device complicates data comparison. In this paper we propose a method for automated registration of whole-body MicroCT follow-up datasets of mice. First, we register the skeleton, the lungs and the skin of an articulated animal atlas (Segars et al. 2004) to MicroCT datasets, yielding point correspondence of these structures over all time points. This correspondence is then used to regularize an intensity-based B-spline registration. This two step approach combines the robustness of model-based registration with the high accuracy of intensity-based registration. We demonstrate our approach using challenging whole-body in vivo follow-up MicroCT data and obtain subvoxel accuracy for the skeleton and the skin, based on the Euclidean surface distance. The method is computationally efficient and enables high resolution whole-body registration in approximately 17 minutes with unoptimized code, mostly executed single-threaded.
在多个时间点对疾病模型进行体内微型计算机断层扫描（MicroCT）成像对于临床前肿瘤学研究以监测疾病进展非常重要。然而，成像设备中动物之间巨大的姿势变异性使数据比较变得复杂。在本文中，我们提出了一种用于小鼠全身MicroCT随访数据集自动配准的方法。首先，我们将一个关节动物图谱（Segars等人，2004年）的骨骼、肺部和皮肤配准到MicroCT数据集，从而在所有时间点上得到这些结构的点对应关系。然后，这种对应关系被用于正则化基于强度的B样条配准。这种两步法将基于模型的配准的稳健性与基于强度的配准的高精度结合起来。我们使用具有挑战性的全身体内随访MicroCT数据演示了我们的方法，并基于欧几里得表面距离获得了骨骼和皮肤的亚体素精度。该方法计算效率高，使用未优化代码且大多单线程执行时，大约17分钟就能实现高分辨率的全身配准。
