锥束层析数据的快速收敛多重网格重建
Rapidly-converging multigrid reconstruction of cone-beam tomographic data
摘要：
在大角度锥束层析成像（CBCT）的背景下，我们提出了一个实用的迭代重建（IR）计划，旨在快速收敛所需的大型数据集。重建的鲁棒性由“空间填充”源轨迹提供，沿着该轨迹收集实验数据。收敛速度是通过利用该轨迹的高度各向同性性质来设计近似反卷积滤波器来实现的，该近似反卷积滤波器用作多重网格方案中的预处理器。我们证明了CBCT的IR计划，并比较收敛到更传统的技术。
英文摘要：
In the context of large-angle cone-beam tomography (CBCT), we present a practical iterative reconstruction (IR) scheme designed for rapid convergence as required for large datasets. The robustness of the reconstruction is provided by the "space-filling" source trajectory along which the experimental data is collected. The speed of convergence is achieved by leveraging the highly isotropic nature of this trajectory to design an approximate deconvolution filter that serves as a pre-conditioner in a multi-grid scheme. We demonstrate this IR scheme for CBCT and compare convergence to that of more traditional techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
