An algorithm to estimate the object support in truncated images
An algorithm to estimate the object support in truncated images
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DOI：
10.1118/1.4881521
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发表时间：
2014-07-01
期刊：
影响因子：
3.8
通讯作者：
Pelc, Norbert J.
中科院分区：
文献类型：
--
作者：
Hsieh, Scott S.;Nett, Brian E.;Pelc, Norbert J.
目的：如果要成像的对象超出扫描仪视野（SFOV），则会出现CT截断伪影。这些伪影妨碍诊断，并可能在放射治疗的剂量计划中引入错误。存在用于校正截断伪影的几种方法，但是现有的校正算法不能准确地恢复患者的皮肤线（或支撑），这在一些剂量规划方法中是重要的。本文的目的是开发一种迭代算法，恢复支持的object.Methods：作者假设截断部分的图像是由软组织的均匀CT数，并试图找到一个形状与测量数据一致。正弦图中的每个已知测量值都被解释为对沿着沿着缺失质量的估计。通过对使用先前截断伪影校正算法（例如，例如，在一个实施例中，水筒外推法）。该对象支撑被迭代地变形以减少与测量数据的不一致。使用此对象支持来估计缺失的数据以完成数据集。结果：该算法产生一个更好的定义皮肤线比水筒外推。在实验数据上，皮肤线的RMS误差减小了约60%。对于适度截断的图像，在SFOV附近保留一些软组织对比度。作为截断的程度增加，SFOV外的软组织对比度变得不可用，虽然皮肤线仍然清楚地定义，并在重新格式化的图像中，它从切片到切片的变化顺利，如expected.Conclusions：支持恢复算法提供了一个更准确的估计病人的轮廓比阈值，基本水圆柱外推，并可能是首选在一些放射治疗应用。(C)2014年美国医学物理学家协会。
Purpose: Truncation artifacts in CT occur if the object to be imaged extends past the scanner field of view (SFOV). These artifacts impede diagnosis and could possibly introduce errors in dose plans for radiation therapy. Several approaches exist for correcting truncation artifacts, but existing correction algorithms do not accurately recover the skin line (or support) of the patient, which is important in some dose planning methods. The purpose of this paper was to develop an iterative algorithm that recovers the support of the object.Methods: The authors assume that the truncated portion of the image is made up of soft tissue of uniform CT number and attempt to find a shape consistent with the measured data. Each known measurement in the sinogram is interpreted as an estimate of missing mass along a line. An initial estimate of the object support is generated by thresholding a reconstruction made using a previous truncation artifact correction algorithm (e. g., water cylinder extrapolation). This object support is iteratively deformed to reduce the inconsistency with the measured data. The missing data are estimated using this object support to complete the dataset. The method was tested on simulated and experimentally truncated CT data.Results: The proposed algorithm produces a better defined skin line than water cylinder extrapolation. On the experimental data, the RMS error of the skin line is reduced by about 60%. For moderately truncated images, some soft tissue contrast is retained near the SFOV. As the extent of truncation increases, the soft tissue contrast outside the SFOV becomes unusable although the skin line remains clearly defined, and in reformatted images it varies smoothly from slice to slice as expected.Conclusions: The support recovery algorithm provides a more accurate estimate of the patient outline than thresholded, basic water cylinder extrapolation, and may be preferred in some radiation therapy applications. (C) 2014 American Association of Physicists in Medicine.