基于小气道病变及肺呼吸运动的多层次智能慢性阻塞性肺病的评估与预测方法研究

慢性阻塞性肺病(COPD)已经成为人类健康的主要威胁，小气道病变作为COPD形成的一种重要前期影响因素，对小气道病变与COPD的深度关联分析，特别对COPD等级发展趋势预测是当前亟待解决的问题。本项目拟以小气道病变及肺呼吸运动作为切入点，通过自动提取肺部CT影像数据的形态特征，建立肺组织形态学模型，探究COPD肺组织的形态学参数与影像学的对应关系；在小气道病变的空气潴留区域构建肺小叶计算模型，研究在呼吸运动过程中肺小叶在影像学上的灰度变化规律，进而实现基于CT影像的COPD的多层次智能评估与分级预测。.本研究可改变目前仅能对COPD做定性诊断的现状，使医生可以考虑COPD诱因及关注肺呼吸运动形态的异常，在不改变医院现有流程的前提下，给出一套从肺部整体到局部、从宏观肺呼吸运动到微观小气道变化的智能定量评估和病变分级的方法，实现肺组织、小气道继发症的定量评估及趋势预测，提升COPD的诊疗水平。

Chronic obstructive pulmonary disease (COPD) has become a major threat to human health. As an important early influencing factor of COPD formation, the deep correlation analysis between COPD and small airway disease, especially the prediction of COPD development trend, is an urgent problem to be solved. This project is based on small airway lesions and respiratory motion as a starting point. By automatically extracting the morphological characteristics of lung CT image data, we establish a lung tissue morphology model, explore the corresponding relationship between COPD lung morphology parameters and imaging. In the air retention area of small airway lesions, we construct a * model of pulmonary lobule, and study the imaging of pulmonary lobules during respiratory movement. According to the change rule of gray level, the multi-level intelligent evaluation and hierarchical prediction of COPD based on CT image can be realized. .This study can change the current situation that only qualitative diagnosis can be made for COPD, so that doctors can consider the causes of COPD and pay much attention to the abnormalities of pulmonary respiratory movement. Without changing the existing process of the hospital, a set of intelligent quantitative evaluation and pathological grading methods from the whole to the part of the lung, from the macro pulmonary respiratory movement to the micro small airway change are provided, so as to realize the succession of lung tissue and small airway The quantitative evaluation and trend prediction of COPD can improve the diagnosis and treatment level of COPD.