基于卷积神经网络的CT伪影降阶方法

IF 2 Q2 ENGINEERING, MULTIDISCIPLINARY
Mahdi Ghafarzadeh, Mohammad Tavakoli Kejani, Mehdi Karimi, Amirreza Asadi
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引用次数: 0

摘要

摘要随着工业的发展,利用精确的无损检测方法来提高质量控制和减少制造误差已成为各个领域的突出问题。工业计算机断层扫描(CT)由于其独特的特点,如提供三维标本模型,在所有传统方法中占有相当大的份量。由于具有高线性衰减系数的金属在工业应用中的普遍存在,光束硬化和散射伪影是任何重建体积中最常见的两种伪影。其他值得注意的人工制品包括那些具有非理想焦斑和锥形光束辐射。这些伪影可能表现为灰度值峰值失真、系统差异、模糊样拔罐和重建图像中的条纹,降低体积重建质量。在本文中,通过采用我们提出的方法来说明和减轻这些伪影的影响,该方法结合了传统和现代技术,包括使用预训练的卷积神经网络(CNN)。在不同的几何参数下重复进行了五次测试,以进行几何构型分析,表明所提出的方法在遇到不同几何情况时的有效性。结果表明,所提出的方法基本上达到了提高我们的模型尺寸测量精度的目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CT Artifact Reduction Employing A Convolutional Neural Network Within the Context of Dimensional Metrology
Abstract Utilizing accurate, nondestructive testing methods to improve quality control and reduce manufacturing errors has gained prominence in light of industry development in various fields. Industrial computed tomography (CT) scanning carries considerable weight among all conventional methods because of their unique features, such as providing a three-dimensional specimen model. Due to the prevalence of metals with high linear attenuation coefficients in industrial applications, beam hardening and scatter artifacts are two of the most prevalent artifacts in any reconstructed volume. Other notable artifacts include those with a nonideal focal spot and conical beam radiation. These artifacts may manifest as a distortion of gray value peaks, systematic discrepancies, blurring-like cupping, and streaking in reconstructed images, degrading volume reconstruction quality. In this paper, the effect of these artifacts is illustrated and mitigated by adopting our proposed method, a combination of conventional and contemporary techniques, including the use of a pretrained convolutional neural network (CNN). Five tests are replicated in different geometric parameters to perform a geometric configuration analysis, indicating how effective the proposed approach is at encountering different geometric situations. The results demonstrate that the proposed method has substantially achieved its goal of improving the accuracy of dimensional metrology performed on our phantom.
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来源期刊
CiteScore
3.80
自引率
9.10%
发文量
25
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