Expanding Objective CT-based Phenotyping to Lungs with Enhanced Radiodensities

描述(申请人提供)：自动生成的、客观的和可重现的基于CT成像的生物标记物对于区分肺部疾病的亚类至关重要，以便更好地将表型与基因型联系起来，从而能够开发治疗慢性阻塞性肺疾病(COPD)、间质性肺纤维化、结节病或哮喘等肺部疾病的新疗法。虽然先前关于开发基于图像的COPD和哮喘生物标记物的研究取得了令人振奋的结果，但由于缺乏自动化的稳健的肺图像分析技术，已经开发的针对由严重炎症、实变、肺泡积水或纤维化引起的高放射性密度病理的疾病的CT生物标记物的翻译到目前为止还不成功。这一缺陷不仅阻碍了现有CT生物标记物的应用，也给开发新的基于图像的肺部疾病生物标记物带来了困难。该建议的目的是通过开发用于单次CT扫描和高/低容量CT扫描对的稳健和快速的图像分析技术来解决定量肺图像分析中的这一瓶颈。具体地说，将开发和验证肺、肺叶和呼吸道分割的新方法，这些方法可以耐受高密度的肺病理，是计算肺路解剖CT生物标记物所需的关键组件。利用基于模型的图像分析方法，实现肺叶分割方法的稳健性。到目前为止，由于器官尺寸较大，这种方法被认为对肺图像分析的计算要求太高。我们将通过利用图形处理硬件技术上的通用计算来解决这个问题，使我们能够显著减少计算时间，如初步结果所示。通过提供一种客观识别肺结构的有效手段，这些结构可以被量化并用于患者群体的亚表型，以促进有20,000名或更多受试者的大型多中心研究。 公共卫生相关性：在该项目中开发的自动化肺部CT图像分析技术将能够在大型多中心研究中利用基于图像的客观生物标记物来诊断肺部疾病，如间质性肺纤维化、结节病、严重哮喘或COPD。

DESCRIPTION (provided by applicant): Automatically generated, objective, and reproducible CT imaging-based biomarkers are critical for the differentiation 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 radio density 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 path anatomy. 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. PUBLIC HEALTH RELEVANCE: Automated lung CT image analysis technology developed within this project will enable utilizing objective image-based biomarkers for lung diseases like interstitial pulmonary fibrosis, sarcoidosis, severe asthma, or COPD in large multi-center studies.