Institute Biomedical Technology, MEDISIP, Ghent University-iMinds, Ghent, Belgium.
PLoS One. 2013 Jul 1;8(7):e68449. doi: 10.1371/journal.pone.0068449. Print 2013.
The aim of this study is to investigate whether reliable and accurate 3D geometrical models of the murine aortic arch can be constructed from sparse-view data in vivo micro-CT acquisitions. This would considerably reduce acquisition time and X-ray dose. In vivo contrast-enhanced micro-CT datasets were reconstructed using a conventional filtered back projection algorithm (FDK), the image space reconstruction algorithm (ISRA) and total variation regularized ISRA (ISRA-TV). The reconstructed images were then semi-automatically segmented. Segmentations of high- and low-dose protocols were compared and evaluated based on voxel classification, 3D model diameters and centerline differences. FDK reconstruction does not lead to accurate segmentation in the case of low-view acquisitions. ISRA manages accurate segmentation with 1024 or more projection views. ISRA-TV needs a minimum of 256 views. These results indicate that accurate vascular models can be obtained from micro-CT scans with 8 times less X-ray dose and acquisition time, as long as regularized iterative reconstruction is used.
本研究旨在探讨是否可以从稀疏视角的体内 micro-CT 采集数据中构建可靠且精确的小鼠主动脉弓三维几何模型。这将大大减少采集时间和 X 射线剂量。使用传统的滤波反投影算法（FDK）、图像空间重建算法（ISRA）和全变分正则化 ISRA（ISRA-TV）对体内对比增强 micro-CT 数据集进行重建。然后对重建图像进行半自动分割。基于体素分类、3D 模型直径和中心线差异比较和评估高剂量和低剂量方案的分割。在低视角采集的情况下，FDK 重建不会导致准确的分割。ISRA 使用 1024 个或更多投影视图可以实现准确的分割。ISRA-TV 需要至少 256 个视图。这些结果表明，只要使用正则化迭代重建，就可以从 X 射线剂量和采集时间减少 8 倍的 micro-CT 扫描中获得精确的血管模型。
