用于肺通气功能障碍定量的超极化气体4D-MRI研究

肺通气功能障碍是新冠肺炎、COPD等肺部重大疾病的显著特征，对其严重程度的评估在临床诊断及预后中具有重要价值。临床常用肺功能检查评估肺通气功能障碍，然而该方法波动性较大，且难以发现局部通气障碍。超极化气体MRI可非侵入性、无电离辐射的探测肺部，在肺通气功能障碍评估中有较大潜力。然而，静态超极化气体MRI无法观测肺部动态通气过程，易忽视轻微通气功能障碍区域，如新冠肺炎轻微磨玻璃影、COPD小气道狭窄等。因此，需要发展超极化气体4D-MRI，获取动态通气过程的时间空间信息。本项目拟研究用于肺通气功能障碍定量的超极化气体4D-MRI：1）设计自适应数据快速采集策略，解决特征数据难提取、信噪比低等难题；2）构建基于时空特性的4D-MRI重建策略，跳出加速倍数低、伪影明显的困境；3）建立肺通气功能障碍量化模型，全面评估肺通气功能障碍。本项目有望对新冠肺炎、COPD等肺部疾病的诊断及预后提供数据支撑。

Lung ventilation obstruction is a significant feature of many lung major diseases (such as COVID-19 and COPD), and the evaluation of the lung ventilation obstruction severity is of great significance to the diagnosis and prognosis of these lung diseases. Pulmonary function tests (PFTs) are usually used to evaluate pulmonary ventilation obstruction in clinic. However, this method has great fluctuation, and it is difficult to detect local ventilation obstruction. Hyperpolarized gas MRI can detect the pulmonary ventilation process non-invasively and without ionizing radiation, which has great potential in the evaluation of pulmonary ventilation obstruction. However, static hyperpolarized gas MRI cannot observe the dynamic ventilation process of the lung, and it is easy to neglect the mild ventilation obstruction area, such as the ground glass shadow in COVID-19 and the small airway stenosis in COPD. Therefore, it is necessary to develop hyperpolarized gas 4D-MRI to obtain the temporal and spatial information of dynamic ventilation process. This project aims to propose a hyperpolarized gas 4D-MRI for the quantification of pulmonary ventilation obstruction. Three steps are included in the proposed algorithm: 1) Design the fast and adaptive gas MRI data acquisition strategy to solve the problems of feature data extraction and low SNR; 2) Build the reconstruction strategy based on the spatial-temporal characteristics of 4D-MRI data, and escape from the dilemma of low acceleration factor and obvious artifact; 3) Establish a quantitative model of pulmonary ventilation obstruction and evaluate it comprehensively. This is expected to provide the basis for the diagnosis and prognosis of lung diseases such as COVID-19 and COPD.