PDF(Pubmed)
Abstract：
Computed tomography (CT), through the use of ionizing radiation, allows us to assess the different parts of the body. It is made up of an X-ray tube that rotates rapidly around the patient generating the radiation beam. This is attenuated with the patient producing information, which is collected by the detectors that are opposite to the tube located in the gantry (part of the tomography equipment); finally, these collected data are sent to the computer that will reconstruct the information obtained and will represent it as an image on the monitor. In the practice of a study, artifices or artifacts may appear regardless of their origin, which limits the scan examination; this leads to stopping the examination and starting again, and added to this with the contrast media, they have to apply these drugs again. State-of-the-art scanners allow complete reconstructions to be performed with few projections, limiting radiation doses, by means of statistical algebraic reconstruction methods. The present work shows the simulation of artifacts in sinusitis diagnosis computed tomography images, the extraction of features from each image, and an automatic classification algorithm for the differentiation of artifacts. The results show that the algorithm is able to classify the simulated artifacts with a percentage of 90%.
摘要：
计算机断层扫描(CT)通过使用电离辐射，允许我们评估身体的不同部位。它由X射线管组成，该X射线管绕患者快速旋转，生成辐射束。这随着患者产生信息而减弱，这是由与位于机架（层析成像设备的一部分）中的管相对的探测器收集的;最后，这些收集的数据被发送到计算机，该计算机将重建所获得的信息，并将其表示为显示器上的图像。在一项研究的实践中，无论其来源如何，都可能出现器官或文物，这限制了扫描检查；这导致停止检查并重新开始，并添加了造影剂，他们必须再次使用这些药物。最先进的扫描仪可以用很少的投影进行完整的重建，限制辐射剂量，通过统计代数重构方法。本工作显示了模拟鼻窦炎诊断计算机断层扫描图像中的伪影，从每个图像中提取特征，和用于区分伪影的自动分类算法。结果表明,该算法能够以90%的百分比对仿真伪影进行分类。
