Henri C J, Peters T M
Department of Radiology and Medical Physics Unit, Montreal General Hospital, Quebec, Canada.
Med Phys. 1996 Feb;23(2):197-204. doi: 10.1118/1.597704.
In this paper we examine the few-view reconstruction problem as it applies to imaging vascular trees. A fully automated reconstruction algorithm is described that circumvents the traditional "correspondence problem," using only notions of consistency and connectivity. It is assumed that the vascular tree is a connected structure and that its centerlines have been identified in three or more images. The first of three steps in the procedure involves generating a connected structure that represents the multiplicity of solutions that are consistent with any two (different) projections. The second step assigns to each branch in this structure a measure of agreement based on its relationship with one or more additional views of the vasculature. The problem then becomes one of propagating this information, via connectivity relationships and consistency checks, throughout the above structure to distinguish between the branches comprising the imaged structure and the accompanying artifacts. In this paper we present the theory and methodology of the technique, while in a companion paper we address the issue of validation via simulations and experiments. Together, these papers shed some light on why ambiguities arise and often lead to errors in the few-view reconstruction problem. Strategies to handle these errors are described and results are presented that demonstrate the ability to obtain adequate reconstructions with as few as three distinct views.
在本文中，我们研究了适用于血管树成像的少视图重建问题。描述了一种全自动重建算法，该算法仅使用一致性和连通性概念，规避了传统的“对应问题”。假设血管树是一个连通结构，并且其中心线已在三张或更多图像中被识别。该过程的三个步骤中的第一步涉及生成一个连通结构，该结构表示与任意两个（不同）投影一致的多个解。第二步根据该结构中每个分支与血管系统的一个或多个其他视图的关系，为其分配一个一致性度量。然后，问题就变成了通过连通性关系和一致性检查，在上述结构中传播此信息，以区分构成成像结构的分支和伴随的伪影。在本文中，我们介绍了该技术的理论和方法，而在一篇配套论文中，我们通过模拟和实验解决了验证问题。这两篇论文共同揭示了在少视图重建问题中模糊性为何会出现并常常导致错误。文中描述了处理这些错误的策略，并给出了结果，这些结果表明仅用三个不同视图就能获得足够的重建效果。
