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Mitigating artifacts by data driven identification & correction of rotational misalignment in gamma ray computed tomography
Applied Radiation and Isotopes ( IF 1.6 ) Pub Date : 2020-09-08 , DOI: 10.1016/j.apradiso.2020.109408
Rajesh Acharya , Umesh Kumar , V.H. Patankar , Soumitra Kar , Ashutosh Dash

Industrial Computed Tomography (ICT) is a radiation based cross sectional imaging technique that requires a sample to be manipulated precisely in a specific geometry to acquire analytically useful data. Unlike medical CT, industrial CT may require use of gamma radiation from radio-isotopes like Co60, Cs137 etc. having higher energy radiations for penetrating through higher density and thickness of material under inspection. Data acquisition in ICT involves use of a mechanical manipulator to rotate either the specimen or the source and detectors assembly in circular and linear geometry. Misalignment in mechanical set-up leads to significant artifacts in CT image. The effects may be even more pronounced in data acquired with discrete detector as against Linear Detector Array (LDA) because of certain built-in mechanical integrity associated with LDA. This paper discusses cross correlation based software correction method for combination of gamma ray source and NaI (Tl) scintillation detector based transmission ICT system in parallel beam CT geometry. The proposed correction does not require calibration of the set-up and any prior knowledge of the sample geometry or composition. This data driven correction yields improved CT reconstruction with limited data. The method is demonstrated with a mathematical simulation and applied to experimental data for validation.



中文翻译:

通过数据驱动的识别和伽马射线计算机断层扫描中旋转不对准的校正来减轻伪影

工业计算机断层扫描(ICT)是一种基于辐射的截面成像技术,该技术要求在特定的几何结构中精确地处理样品,以获取分析有用的数据。与医学CT不同,工业CT可能需要使用来自放射性同位素(如Co 60,Cs 137)的伽马射线等具有较高能量辐射的材料可以穿透被检查材料的更高密度和厚度。ICT中的数据采集涉及使用机械操纵器以圆形和线性几何形状旋转样本或源和检测器组件。机械设置中的未对准会导致CT图像中出现明显的伪影。与线性检测器阵列(LDA)相比,使用离散检测器采集的数据中的影响甚至更为明显,因为与LDA相关的某些内置机械完整性。本文讨论了基于平行相关CT几何中基于γ射线源和NaI(Tl)闪烁探测器的透射ICT系统相结合的基于互相关的软件校正方法。建议的校正不需要校准设置,也不需要任何样品几何形状或成分的先验知识。这种数据驱动的校正可以改善有限数据的CT重建。该方法通过数学模拟进行了演示,并应用于实验数据进行验证。

更新日期:2020-09-21
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