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: 10831646
• 负责人: Li Feng
• 金额: $ 108.34万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 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 2025-09-02
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10831646
• 关键词: Rapid; Structure; Function; MRI; Lung

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.

项目总结

项目成果

Golden-Angle Radial MRI: Basics, Advances, and Applications.

• DOI: 10.1002/jmri.28187
• 发表时间: 2022-07
• 期刊: JOURNAL OF MAGNETIC RESONANCE IMAGING
• 影响因子: 4.4
• 作者: Feng, Li

Performance of spiral UTE-MRI of the lung in post-COVID patients.

• DOI: 10.1016/j.mri.2022.12.002
• 发表时间: 2023-02
• 期刊: MAGNETIC RESONANCE IMAGING
• 影响因子: 2.5
• 作者: Fauveau, Valentin;Jacobi, Adam;Bernheim, Adam;Chung, Michael;Benkert, Thomas;Fayad, Zahi A.;Feng, Li
• 通讯作者: Feng, Li