Expanding Objective CT-based Phenotyping to Lungs with Enhanced Radiodensities

描述（由申请人提供）：自动生成的、客观的、可重复的基于CT成像的生物标志物对于肺部疾病亚类的区分至关重要，可以更好地将表型与基因型联系起来，从而开发新的肺部疾病治疗方法，如慢性阻塞性肺疾病（COPD）、间质性肺纤维化、结节病或哮喘。虽然先前开发COPD和哮喘的基于图像的生物标志物的研究已经显示出有希望的结果，但由于缺乏自动化的强大的肺图像分析技术，迄今为止，已经开发的用于由明显炎症、实变、肺泡充血或纤维化引起的高放射密度病理疾病实体的CT生物标志物的翻译尚未成功。这一缺陷不仅阻碍了现有CT生物标志物的利用，也给开发新的基于图像的肺部疾病生物标志物带来了问题。本提案的目的是通过开发针对单次CT扫描和高/低体积CT扫描对的鲁棒和快速图像分析技术来解决定量肺部图像分析的瓶颈。具体来说，将开发和验证肺、肺叶和气道分割的新方法，这些方法可以耐受高密度肺病理，并且是计算肺路径解剖CT生物标志物所需的关键组成部分。利用基于模型的图像分析方法实现肺和肺叶分割方法的鲁棒性。到目前为止，由于肺的器官尺寸较大，这种方法被认为对计算量要求太高。我们将通过在图形处理硬件技术上使用通用计算来解决这个问题，这使我们能够显着减少计算时间，正如初步结果所证明的那样。通过提供一种客观识别肺结构的有效方法，这些结构可以被量化并用于亚表型患者群体，以促进有20,000或更多受试者的大型多中心研究。

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.