x射线计算机断层扫描系统的最大似然校准。

Jared W Moore, Roel Van Holen, Harrison H Barrett, Lars R Furenlid
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引用次数: 0

摘要

我们提出了一种最大似然(ML)方法来校准x射线计算机断层扫描(CT)系统的几何参数。这种方法利用了完整的图像数据,而不是简化的数据集。该算法对于在CT采集过程中改变其几何形状的CT系统特别有用,例如自适应CT扫描。我们的机器学习搜索方法使用一种不需要初始起始值来执行估计的收缩网格算法,从而避免了与选择初始值相关的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Maximum-Likelihood Calibration of an X-ray Computed Tomography System.

We present a maximum-likelihood (ML) method for calibrating the geometrical parameters of an x-ray computed tomography (CT) system. This method makes use of the full image data and not a reduced set of data. This algorithm is particularly useful for CT systems that change their geometry during the CT acquisition, such as an adaptive CT scan. Our ML search method uses a contracting-grid algorithm that does not require initial starting values to perform its estimate, thus avoiding problems associated with choosing initialization values.

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