Expanding Objective CT-based Phenotyping to Lungs with Enhanced Radiodensities

项目摘要 自动生成的、客观的和可重复的基于CT成像的生物标记物对于不同的 对肺部疾病的亚类进行分类，以更好地将表型与基因型联系起来，使发展成为可能 治疗慢性阻塞性肺疾病(COPD)、间质性肺疾病等肺部疾病的新疗法 纤维化、结节病或哮喘。虽然之前关于开发基于图像的COPD生物标志物和 哮喘已经显示出有希望的结果，已经开发的用于疾病实体的CT生物标志物的翻译 由于明显的炎症、实变、肺泡积水或纤维组织而引起的高放射性密度病理。 到目前为止，由于缺乏自动化的稳健的肺图像分析技术，SIS尚未成功。这 这些缺点不仅阻碍了现有CT生物标志物的利用，也给开发新的CT生物标志物带来了困难 这些肺部疾病的基于图像的生物标志物。这项提议的目标就是解决这一瓶颈 开发稳健快速的单次CT扫描图像分析技术在定量肺图像分析中的应用 和高/低容量CT扫描对。具体来说，肺、肺叶和呼吸道分割的新方法将 要开发和验证，它可以耐受高密度肺病理，是所需的关键组件 用于计算肺部病理解剖的CT生物标记物。肺和肺叶分割方法的稳健性将 通过利用基于模型的图像分析方法来实现。到目前为止，人们一直在考虑这样的方法 由于器官尺寸较大，对肺图像分析的计算量要求太高。我们将解决 本课题通过利用通用计算上的图形处理硬件技术，使我们能够 如初步结果所示，显著减少计算时间。通过提供有效的手段 对于客观地识别肺结构，这些结构可以被量化并用于亚表型 患者群体，根据需要促进具有20,000或更多受试者的大型多中心研究。

Project Summary Automatically generated, objective, and reproducible CT imaging-based biomarkers are critical for the differen- tiation of sub-classes of pulmonary disease to better link phenotypes to genotypes, enabling the development of new treatments for lung diseases like chronic obstructive pulmonary disease (COPD), interstitial pulmonary fibrosis, sarcoidosis, or asthma. While previous research on developing image-based biomarkers for COPD and asthma has shown promising results, the translation of already developed CT biomarkers for disease entities with high radiodensity pathology caused by significant inflammation, consolidation, alveolar flooding or fibro- sis was not successful up to now due to the lack of automated robust lung image analysis techniques. This shortcoming not only hinders the utilization of existing CT biomarkers, it is also problematic for developing new image-based biomarkers for these lung diseases. The objective of this proposal is to address this bottleneck in quantitative lung image analysis by developing robust and fast image analysis techniques for single CT scan and high/low volume CT scan pairs. Specifically, novel methods for lung, lung lobe, and airway segmentation will be developed and validated, which can tolerate high density lung pathology and are a key component required for calculating CT biomarkers for lung pathoanatomy. Robustness of lung and lobe segmentation methods will be achieved by utilizing model-based image analysis methods. Up to now, such approaches were considered as too computationally demanding for lung image analysis because of the large organ size. We will address this issue by utilizing general-purpose computation on graphics processing hardware techniques, allowing us to reduce computation times significantly, as demonstrated by preliminary results. By providing an efficient means of objectively identifying lung structures, these structures can be quantified and utilized for sub-phenotyping patient populations, as required to facilitate large multi-center studies with 20,000 or more subjects.