This paper proposes a new discrete optimization framework for tomographic reconstruction and segmentation of CT volumes when only a few projection views are available. The problem has important clinical applications in coronary angiographic imaging. We first show that the limited view reconstruction and segmentation problem can be formulated as a "constrained" version of the metric labeling problem. This lays the groundwork for a linear programming framework that brings metric labeling classification and classical algebraic tomographic reconstruction (ART) together in a unified model. If the imaged volume is known to be comprised of a finite set of attenuation coefficients (a realistic assumption), given a regular limited view reconstruction, we view it as a task of voxels reassignment subject to maximally maintaining consistency with the input reconstruction and the objective of ART simultaneously. The approach can reliably reconstruct (or segment) volumes with several multiple contrast objects. We present evaluations using experiments on cone beam computed tomography.

中文翻译：

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通过约束度量标记进行有限视图 CT 重建和分割。

当只有少数投影视图可用时，本文提出了一种新的离散优化框架，用于 CT 体积的断层扫描重建和分割。该问题在冠状动脉造影成像中具有重要的临床应用。我们首先表明，有限视图重建和分割问题可以表述为度量标记问题的“约束”版本。这为线性规划框架奠定了基础，该框架将度量标记分类和经典代数断层扫描重建 (ART) 结合在一个统一模型中。如果已知成像体积由一组有限的衰减系数组成（一个现实的假设），给定一个规则的有限视图重建，我们将其视为体素重新分配的任务，同时最大程度地保持与输入重建和 ART 目标的一致性。该方法可以使用多个多个对比对象可靠地重建（或分割）体积。我们使用锥形束计算机断层扫描实验来进行评估。