abstract Two adaptive discretization frameworks are tested for computerized tomography (CT) data reconstruction. Removal of inactive pixels is primary motivation. Efficient and user independent entropy optimized masking is employed for spatial filtering purposes. Density of nodes at high gradient of reconstructed physical property is used as adaptation criterion. An alternative option, independent from noisy projection data and nature of the physical properties, is also discussed. Sensitivity analysis between the uniform and nonuniform (evolved via adaptive route) reconstruction grid reveals the utility of nonuniform grids. Iterative and transform based reconstruction techniques are used. Outcomes are tested successfully on three real world projection data from two different compact CT setups and one commercial high-resolution micro-CT scanner.

中文翻译：

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计算机断层扫描的自适应离散化

摘要 测试了两种自适应离散化框架用于计算机断层扫描 (CT) 数据重建。去除非活动像素是主要动机。高效且独立于用户的熵优化掩蔽用于空间过滤目的。重构物理属性高梯度下的节点密度被用作适应标准。还讨论了与噪声投影数据和物理特性的性质无关的替代选项。均匀和非均匀（通过自适应路径演化）重建网格之间的敏感性分析揭示了非均匀网格的效用。使用迭代和基于变换的重建技术。结果在来自两种不同紧凑型 CT 设置和一台商用高分辨率微型 CT 扫描仪的三个真实世界投影数据上成功测试。