Sensitivity analysis of models of gas exchange for lung hyperpolarised 129Xe MR.

Sensitivity analysis enables the identification of influential parameters and the optimisation of model composition. Such methods have not previously been applied systematically to models describing hyperpolarised 129Xe gas exchange in the lung. Here, we evaluate the current 129Xe gas exchange models to assess their precision for identifying alterations in pulmonary vascular function and lung microstructure. We assess sensitivity using established univariate methods and scatter plots for parameter interactions. We apply them to the model described by Patz et al and the Model of Xenon Exchange (MOXE), examining their ability to measure: i) importance (rank), ii) temporal dependence and iii) interaction effects of each parameter across healthy and diseased ranges. The univariate methods and scatter plot analyses demonstrate consistently similar results for the importance of parameters common to both models evaluated. Alveolar surface area to volume ratio is identified as the parameter to which model signals are most sensitive. The alveolar-capillary barrier thickness is identified as a low-sensitivity parameter for the MOXE model. An acquisition window of at least 200 ms effectively demonstrates model sensitivity to most parameters. Scatter plots reveal interaction effects in both models, impacting output variability and sensitivity. Our sensitivity analysis ranks the parameters within the model described by Patz et al and within the MOXE model. The MOXE model shows low sensitivity to alveolar-capillary barrier thickness, highlighting the need for designing acquisition protocols optimised for the measurement of this parameter. The presence of parameter interaction effects highlights the requirement for care in interpreting model outputs.

Feasibility of dynamic T2 *-based oxygen-enhanced lung MRI at 3T.

PURPOSE: To demonstrate proof-of-concept of a T2 *-sensitized oxygen-enhanced MRI (OE-MRI) method at 3T by assessing signal characteristics, repeatability, and reproducibility of dynamic lung OE-MRI metrics in healthy volunteers. METHODS: We performed sequence-specific simulations for protocol optimisation and acquired free-breathing OE-MRI data from 16 healthy subjects using a dual-echo RF-spoiled gradient echo approach at 3T across two institutions. Non-linear registration and tissue density correction were applied. Derived metrics included percent signal enhancement (PSE), ∆R2 * and wash-in time normalized for breathing rate (τ-nBR). Inter-scanner reproducibility and intra-scanner repeatability were evaluated using intra-class correlation coefficient (ICC), repeatability coefficient, reproducibility coefficient, and Bland-Altman analysis. RESULTS: Simulations and experimental data show negative contrast upon oxygen inhalation, due to substantial dominance of ∆R2 * at TE > 0.2 ms. Density correction improved signal fluctuations. Density-corrected mean PSE values, aligned with simulations, display TE-dependence, and an anterior-to-posterior PSE reduction trend at TE1 . ∆R2 * maps exhibit spatial heterogeneity in oxygen delivery, featuring anterior-to-posterior R2 * increase. Mean T2 * values across 32 scans were 0.68 and 0.62 ms for pre- and post-O2 inhalation, respectively. Excellent or good agreement emerged from all intra-, inter-scanner and inter-rater variability tests for PSE and ∆R2 *. However, ICC values for τ-nBR demonstrated limited agreement between repeated measures. CONCLUSION: Our results demonstrate the feasibility of a T2 *-weighted method utilizing a dual-echo RF-spoiled gradient echo approach, simultaneously capturing PSE, ∆R2 * changes, and oxygen wash-in during free-breathing. The excellent or good repeatability and reproducibility on intra- and inter-scanner PSE and ∆R2 * suggest potential utility in multi-center clinical applications.