Context sensitive cardiac x-ray imaging: a machine vision approach to x-ray dose control
Context sensitive cardiac x-ray imaging: a machine vision approach to x-ray dose control
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DOI：
10.1117/1.jei.24.5.051002
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发表时间：
2015-09-01
影响因子：
1.1
通讯作者：
Davies, Andrew G.
中科院分区：
文献类型：
--
作者：
Kengyelics, Stephen M.;Gislason-Lee, Amber J.;Davies, Andrew G.
现代心脏X射线成像系统基于患者的厚度来调节其辐射输出，以在X射线检测器的输入处维持可接受的信号。这种方法不考虑检查的背景或显示的图像的内容。我们已经开发了一种机器视觉算法，该算法可以检测充满碘的血管，并使用对比度、直径和线性衰减系数等关键参数拟合理想化的血管模型。线性衰减系数样本的时空分布，当适当地安排时，可以通过简单的线性关系来描述，而不管场景信息的复杂性。该算法进行了测试静态拟人胸部模型图像在不同的放射学因素和60个动态临床图像序列。它被认为是强大的和敏感的血管对比度的变化，导致系统参数的变化。机器视觉算法具有提取可用于增强现有剂量控制策略的实时上下文敏感信息的潜力。(C)2015年SPIE和IS&T
Modern cardiac x-ray imaging systems regulate their radiation output based on the thickness of the patient to maintain an acceptable signal at the input of the x-ray detector. This approach does not account for the context of the examination or the content of the image displayed. We have developed a machine vision algorithm that detects iodine-filled blood vessels and fits an idealized vessel model with the key parameters of contrast, diameter, and linear attenuation coefficient. The spatio-temporal distribution of the linear attenuation coefficient samples, when appropriately arranged, can be described by a simple linear relationship, despite the complexity of scene information. The algorithm was tested on static anthropomorphic chest phantom images under different radiographic factors and 60 dynamic clinical image sequences. It was found to be robust and sensitive to changes in vessel contrast resulting from variations in system parameters. The machine vision algorithm has the potential of extracting real-time context sensitive information that may be used for augmenting existing dose control strategies. (C) 2015 SPIE and IS&T