Expanding Objective CT-based Phenotyping to Lungs with Enhanced Radiodensities

将基于 CT 的客观表型扩展到具有增强放射密度的肺部

• 批准号: 8222609
• 负责人: Reinhard R. Beichel
• 金额: $ 35.56万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8222609
• 关键词: Address; Adopted; Adult Respiratory Distress Syndrome; Air; Alveolar; Anatomy; Applications Grants; Asthma; Austria; Biological Markers; Biomedical Engineering; Blood Vessels; Canada; Chest wall structure; Chronic Obstructive Airway Disease; Collaborations; Comb animal structure; Computer software; Data; Data Set; Databases; Development; Diagnostic; Disease; Environment; Fibrosis; Floods; Funding; Genotype; Glass; Goals; Honey; Iceland; Image; Image Analysis; Inflammation; Inflammatory; Interstitial Pneumonia; Intervention; Investigation; Iowa; Japan; Korea; Link; Lobar; Lobe; Lung; Lung diseases; Mediastinal; Medical; Methodology; Methods; Michigan; Modeling; Organ Size; Pathology; Patients; Phenotype; Process; Pulmonary Emphysema; Pulmonary Fibrosis; Radio; Research; Research Personnel; Research Project Grants; Sarcoidosis; Scanning; Scotland; Structure; Structure of parenchyma of lung; Sweden; Techniques; Technology; Testing; Texture; Time; Translations; Trees; United States National Institutes of Health; Universities; Work; X-Ray Computed Tomography; base; cone-beam computed tomography; density; detector; experience; improved; interstitial; lung basal segment; lung imaging; lung lobe; lung volume; morphometry; novel; patient population; success; tool; uptake

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.

描述（由申请人提供）：自动生成的、客观的和可重现的基于CT成像的生物标志物对于区分肺部疾病的亚类至关重要，可以更好地将表型与基因型联系起来，从而能够开发慢性阻塞性肺疾病（COPD）、间质性肺纤维化、结节病或哮喘等肺部疾病的新治疗方法。虽然先前关于开发用于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，但是由于缺乏自动化的稳健的肺图像分析技术，迄今为止，已经开发的用于具有由显著炎症、实变、肺泡泛液或肺栓塞引起的高放射密度病理的疾病实体的CT生物标志物的翻译还没有成功。这一缺点不仅阻碍了现有CT生物标志物的利用，而且对于开发用于这些肺部疾病的新的基于图像的生物标志物也是有问题的。本提案的目的是通过为单次CT扫描和高/低体积CT扫描对开发稳健且快速的图像分析技术来解决定量肺部图像分析中的这一瓶颈。具体而言，将开发和验证用于肺、肺叶和气道分割的新方法，其可以耐受高密度肺病理，并且是计算肺路径解剖的CT生物标志物所需的关键组成部分。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，这种方法被认为是太计算要求肺图像分析，因为大的器官尺寸。我们将通过利用图形处理硬件技术的通用计算来解决这个问题，使我们能够显着减少计算时间，如初步结果所示。通过提供客观地鉴定肺结构的有效手段，这些结构可以被量化并用于亚表型患者群体，如促进具有20，000或更多受试者的大型多中心研究所需。 公共卫生关系：在该项目中开发的自动肺部CT图像分析技术将能够在大型多中心研究中利用基于客观图像的肺部疾病生物标志物，如间质性肺纤维化、结节病、重度哮喘或COPD。