Hall P, Ngan M, Andreae P
Department of Computer Science, University of Wales, Cardiff, UK.
IEEE Trans Med Imaging. 1997 Dec;16(6):919-29. doi: 10.1109/42.650888.
Reconstructing vasculature in three dimensions is a challenging problem. Early approaches concentrated on coronary vasculature in X-ray images, recent work uses magnetic resonance imagery of cerebral vasculature. In both cases a priori information has been used, and often the way this is represented has proven limiting to the scope of applications supported. For example, a particular representation may be useful only for X-ray images. This paper addresses two issues: 1) representing a collection of vasculature and 2) the reconstruction of individual vasculature from images. Our representation learns the variations in branching structures and vessel shapes that occur between individuals. It supports a vascular catalogue containing three-dimensional (3-D) anatomical models. The representation is task independent; here we use it to reconstruct vasculature from images. Our algorithm has four features to which we draw attention: 1) it is not premised wholly upon X-ray images (though that is our focus here); 2) it produces several feasible solutions rather than one; 3) it can generalize from the catalogue to reconstruct instances not yet learned; 4) it exhibits polynomial time complexity, reasonable memory consumption, and is reliable. Both our representation and reconstruction algorithm are new and useful approaches. In support of these claims, we present results gathered from X-rays of both simulated and real vasculature.
三维重建血管系统是一个具有挑战性的问题。早期方法主要集中于X射线图像中的冠状动脉系统，而近期工作则使用脑血管系统的磁共振成像。在这两种情况下，都使用了先验信息，而且通常这种信息的表示方式已被证明限制了所支持应用的范围。例如，一种特定表示可能仅对X射线图像有用。本文讨论两个问题：1）表示血管系统的集合；2）从图像重建单个血管系统。我们的表示方法学习个体之间出现的分支结构和血管形状的变化。它支持一个包含三维（3-D）解剖模型的血管目录。该表示与任务无关；在这里我们用它从图像重建血管系统。我们的算法有四个值得关注的特性：1）它并非完全基于X射线图像（尽管这是我们这里的重点）；2）它产生多个可行解而非一个；3）它可以从目录进行推广以重建尚未学习的实例；4）它具有多项式时间复杂度、合理的内存消耗且可靠。我们的表示方法和重建算法都是新颖且有用的方法。为支持这些说法，我们展示了从模拟和真实血管系统的X射线中收集的结果。
