Rapid Structure-Function MRI of the Lung for Post-COVID-19 Management

用于 COVID-19 后管理的肺部快速结构功能 MRI

• 批准号: 10181576
• 负责人: Li Feng
• 金额: $ 37.04万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-03 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10181576
• 关键词: 3-Dimensional; 4D Imaging; 4D MRI; Adult Respiratory Distress Syndrome; Affect; Air; Anatomy; Breathing; COVID-19; COVID-19 monitoring; COVID-19 pandemic; COVID-19 patient; Caring; Chronic; Clinical; Contrast Media; Development; Diagnostic; Dimensions; Disease; Early identification; Economic Burden; Ensure; Evaluation; Foundations; Four-dimensional; Gold; Healthcare; Heterogeneity; Image; Imaging Techniques; Incidence; Inflammation; Investigation; Ionizing radiation; Lead; Longitudinal Studies; Lung; Lung Capacity; Lung diseases; Magnetic Resonance Imaging; Measures; Methods; Morbidity - disease rate; Motion; Outcome; Patients; Performance; Plethysmography; Prevalence; Protocols documentation; Public Health; Pulmonary Fibrosis; Pulmonary function tests; Radial; Radiation; Radiation exposure; Recording of previous events; Research; Residual state; Resolution; Respiratory Failure; Risk; Scanning; Societies; Spirometry; Structural defect; Structure; Time; United States; Variant; Viral Pneumonia; X-Ray Computed Tomography; base; care burden; chest computed tomography; clinical imaging; coronavirus disease; deep learning; disease heterogeneity; image reconstruction; imaging modality; insight; lung basal segment; lung development; lung imaging; lung injury; lung lobe; lung volume; novel; prevent; pulmonary function; pulmonary function decline; rapid technique; respiratory; soft tissue; ventilation; volunteer

Project Summary The pandemic of coronavirus disease 2019 (COVID-19) has presented an unprecedented crisis and challenge to public health, with tremendous health care and economic burden to our society. Given the high morbidity of this new disease and increasing findings on COVID-associated complications, it has emerged as an important and urgent clinical need to investigate the sequelae in patients recovered from COVID-19 (post-COVID patients), particularly with respect to residual long-term lung damage such as post-COVID decline of pulmonary function and/or development of lung structure abnormality. This will ensure that the long-term outcomes of post-COVID patients can be studied, better understood, and properly managed. At present, Computed Tomography (CT) is the gold standard for imaging lung anatomy, but its radiation burden precludes frequent longitudinal evaluations. Pulmonary function is currently evaluated by spirometry or plethysmography, which provide global measures of function but this is inadequate for assessing the extent of disease heterogeneity. The overarching aim of this application is to develop novel rapid free-breathing four-dimensional (4D=3D+motion) lung MRI techniques that will enable frequent evaluation of lung anatomy and function in the longitudinal follow-ups of post-COVID patients. These new methods will be based on a combination of compressed sensing and golden-angle radial acquisition, with incorporation of the latest advances in deep learning, which are all pioneered by our research team. Specifically, we propose to develop, optimize and evaluate a 10-minute free-breathing lung MRI protocol that will enable one-stop-shop characterization of whole lung anatomy and spatially-resolved pulmonary function without administration of contrast media. The overall hypothesis is that the proposed 4D MRI techniques can serve as a potential radiation-free alternative to CT for longitudinal evaluation of lung structure change and a better alternative to spirometry for deriving regional function parameters that could provide additional novel insights into the heterogeneity of lung ventilation and associated lung diseases. Our proposal includes the following three specific aims: (i) development of motion-resolved 4D UTE-MRI (MRI with ultra-shot echo times) for submillimeter resolution free-breathing whole-lung imaging, (ii) development of deep breathing-based 4D UTE-MRI for deriving global/regional pulmonary function parameters, and (iii) development of deep learning- based lung segmentation for efficient and automated estimation of pulmonary function parameters. Successful completion of this project will deliver non-invasive, non-contrast-enhanced, and free-breathing 4D MRI techniques for rapid assessment of lung anatomy and pulmonary function. Given the overwhelming prevalence of COVID-19 and the overall cumulative incidence in the United States, our novel imaging methods would provide a unique opportunity to augment post-COVID care, particularly for identifying COVID-19-induced lung fibrosis in a timely manner and for studying the longitudinal changes of lung anatomy and global/regional pulmonary function. These methods could also be extended for evaluation of other pulmonary diseases.

项目摘要 2019冠状病毒病（COVID-19）大流行带来了前所未有的危机和挑战 对公共健康的影响，给我们的社会带来巨大的医疗和经济负担。鉴于高发病率， 这种新的疾病和不断增加的新冠病毒相关并发症的发现，它已成为一个重要的 以及迫切的临床需求，以调查从COVID-19中康复的患者（COVID后患者）的后遗症， 特别是对于残留的长期肺损伤，如COVID后肺功能下降 和/或肺结构异常的发展。这将确保COVID后的长期结果 患者可以被研究，更好地理解，并得到适当的管理。目前，计算机断层扫描（CT） 肺解剖成像的黄金标准，但其辐射负担排除了频繁的纵向评价。 肺功能目前通过肺量测定法或体积描记法来评估，其提供了肺功能的总体测量。 但这不足以评估疾病异质性的程度。这项工作的首要目标是， 应用是开发新的快速自由呼吸四维（4D=3D+运动）肺MRI技术， 将能够在COVID后患者的纵向随访中频繁评估肺解剖结构和功能。 这些新方法将基于压缩传感和黄金角径向采集的组合， 结合了深度学习的最新进展，这些都是由我们的研究团队开创的。 具体来说，我们建议开发、优化和评估10分钟自由呼吸肺部MRI方案， 将实现全肺解剖和空间分辨肺功能的一站式表征 而不使用造影剂。总体假设是，所提出的4D MRI技术可以 作为一种潜在的无辐射替代CT纵向评估肺结构的变化， 更好地替代肺量测定法，用于导出区域功能参数， 深入了解肺通气和相关肺部疾病的异质性。我们的建议包括 以下三个具体目标：（i）开发运动分辨4D UTE-MRI（具有超激发回波时间的MRI） 对于亚毫米分辨率的自由呼吸全肺成像，（ii）开发基于深呼吸的4D UTE-MRI，用于导出全局/局部肺功能参数，以及（iii）开发深度学习- 基于肺分割的肺功能参数的有效和自动估计。成功 该项目的完成将提供非侵入性、非对比增强和自由呼吸的4D MRI 快速评估肺解剖结构和肺功能的技术。考虑到这种疾病 的COVID-19和美国的总体累积发病率，我们的新型成像方法将提供 一个独特的机会，以加强后COVID护理，特别是用于确定COVID-19诱导的肺纤维化， 及时地和用于研究肺解剖和全局/区域肺的纵向变化 功能这些方法也可以扩展到其他肺部疾病的评估。