Development of Image Analysis Tools for Pulmonary Spatial and Temporal Mapping

肺部时空测绘图像分析工具的开发

• 批准号: RGPIN-2019-06238
• 负责人: Kirby, Miranda
• 金额: $ 2.4万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=738791
• 关键词: Development; Image; Analysis; Tools; Pulmonary

Motivation: Medical imaging has enabled both the internal structure and function of the lung to be studied in vivo and in three-dimensions. Numerous advances in image acquisition technology have resulted in computed tomography (CT) emerging as the most effective imaging modality for assessing lung structure, and magnetic resonance imaging (MRI) with inhaled hyperpolarized noble gases emerging as a novel method for visualizing lung function. Although CT and MRI quantitative measurements have been developed, approaches for integrating and quantifying the inter-relationships between these imaging measurements are required. Research Problem: Methods have been developed to classify the lung on a voxel-by-voxel basis according to structural disease state for CT (normal, small airway disease, emphysema) and according to functional disease state for MRI (normal or abnormal gas exchange). However, multiparametric approaches are required that can investigate the spatial heterogeneity of these features throughout the lung on a regional basis, the temporal heterogeneity of how these features change or transition over time, and combine the measurements from both CT and MRI to investigate the interaction between structure and function. Research Objectives: The objective of my research program is to develop novel multiparametric imaging measurements that interrogate and quantify pulmonary spatial and temporal relationships. Specifically, over 5 years, my objectives are: 1) develop spatial heterogeneity measurements and spatial inter-relationship measurements using CT images; 2) develop voxelwise temporal trajectories using longitudinal CT images; and, 3) develop multiparametric measurements using structural and functional information from CT and hyperpolarized 129Xe MRI. Anticipated Outcomes: By the end of our proposed 5-year research plan, we will have developed image analysis tools that generate novel measurements that characterize spatial and temporal heterogeneity. Our long-term goals are to incorporate these imaging measurements into machine learning algorithms for predicting adverse health outcomes in patients with lung disease. This work will generate highly qualified personnel for the private and academic sectors in medical imaging, imaging physics/engineering and software development. Impact: This research program will provide imaging scientists within the natural science and engineering fields of academia and industry with multiparametric image analysis tools that can incorporate information from multiple imaging time points and from different imaging modalities to yield novel spatial and temporal heterogeneity measurements. Importantly, these image analysis tools will be develop to extend to other imaging modalities or organ systems, allowing for use in a broad range of applications.

动机：医学成像使人们能够在体内和三维上研究肺的内部结构和功能。随着图像采集技术的不断进步，计算机断层扫描(CT)成为评价肺结构的最有效的成像手段，而吸入超极化惰性气体的磁共振成像(MRI)则成为一种新的肺功能可视化方法。虽然已经开发了CT和MRI定量测量方法，但仍需要整合和量化这些成像测量之间的相互关系的方法。研究问题：已经开发出一种方法，根据CT的结构疾病状态(正常、小气道疾病、肺气肿)和MRI的功能疾病状态(气体交换正常或异常)逐个体素地对肺进行分类。然而，需要多参数方法来研究这些特征在整个肺内的区域性空间异质性，这些特征如何随时间变化或过渡的时间异质性，并结合来自CT和MRI的测量来研究结构和功能之间的相互作用。研究目标：我的研究计划的目标是开发新的多参数成像测量方法，以询问和量化肺的空间和时间关系。具体地说，在5年的时间里，我的目标是：1)使用CT图像开发空间异质性测量和空间相互关系测量；2)使用纵向CT图像开发体素时间轨迹；以及3)使用CT和超极化129Xe MRI的结构和功能信息开发多参数测量。预期结果：到我们提议的五年研究计划结束时，我们将开发出图像分析工具，生成表征空间和时间异质性的新测量。我们的长期目标是将这些成像测量纳入机器学习算法，以预测肺部疾病患者的不良健康结果。这项工作将为私营部门和学术部门培养医学成像、成像物理/工程和软件开发方面的高素质人才。影响：该研究计划将为学术界和工业界自然科学和工程领域的成像科学家提供多参数图像分析工具，这些工具可以整合来自多个成像时间点和不同成像模式的信息，以产生新颖的空间和时间异质性测量。重要是，这些图像分析工具将被开发以扩展到其他成像方式或器官系统，从而允许在广泛的应用中使用。