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首页> 美国卫生研究院文献>Journal of Digital Imaging >Quantitative Kinetic Analysis of Lung Nodules Using the Temporal Subtraction Technique in Dynamic Chest Radiographies Performed with a Flat Panel Detector
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Quantitative Kinetic Analysis of Lung Nodules Using the Temporal Subtraction Technique in Dynamic Chest Radiographies Performed with a Flat Panel Detector

机译：使用平板探测器在动态胸部X光片中使用时间减影技术对肺结节进行定量动力学分析

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摘要

Early detection and treatment of lung cancer is one of the most effective means of reducing cancer mortality, and to this end, chest X-ray radiography has been widely used as a screening method. A related technique based on the development of computer analysis and a flat panel detector (FPD) has enabled the functional evaluation of respiratory kinetics in the chest and is expected to be introduced into clinical practice in the near future. In this study, we developed a computer analysis algorithm to detect lung nodules and to evaluate quantitative kinetics. Breathing chest radiographs obtained by modified FPD and breath synchronization utilizing diaphragmatic analysis of vector movement were converted into four static images by sequential temporal subtraction processing, morphological enhancement processing, kinetic visualization processing, and lung region detection processing. An artificial neural network analyzed these density patterns to detect the true nodules and draw their kinetic tracks. Both the algorithm performance and the evaluation of clinical effectiveness of seven normal patients and simulated nodules showed sufficient detecting capability and kinetic imaging function without significant differences. Our technique can quantitatively evaluate the kinetic range of nodules and is effective in detecting a nodule on a breathing chest radiograph. Moreover, the application of this technique is expected to extend computer-aided diagnosis systems and facilitate the development of an automatic planning system for radiation therapy.
机译：肺癌的早期发现和治疗是降低癌症死亡率的最有效方法之一，为此，胸部X射线照相术已被广泛用作筛查方法。基于计算机分析和平板检测器（FPD）的开发的相关技术已使胸部呼吸动力学的功能评估成为可能，并有望在不久的将来引入临床实践。在这项研究中，我们开发了一种计算机分析算法来检测肺结节并评估定量动力学。通过顺序的时间减法处理，形态增强处理，动力学可视化处理和肺区域检测处理，将通过改进的FPD进行的呼吸胸部X线照片和利用矢量运动的隔膜分析获得的呼吸同步转换为四个静态图像。人工神经网络分析了这些密度模式，以检测出真正的结节并绘制其动力学轨迹。 7名正常患者和模拟结节的算法性能和临床疗效评估均显示出足够的检测能力和动力学成像功能，而无显着差异。我们的技术可以定量评估结节的动力学范围，并且可以有效地检测呼吸胸片上的结节。此外，预计该技术的应用将扩展计算机辅助诊断系统并促进放射治疗自动计划系统的开发。

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