Comparison between magnetic resonance imaging and computed tomography of the lung in patients with cystic fibrosis with regard to clinical, laboratory, and pulmonary functional parameters

OBJECTIVE To evaluate whether magnetic resonance imaging (MRI) is effective as computed tomography (CT) in determining morphologic and functional pulmonary changes in patients with cystic fibrosis (CF) in association with multiple clinical parameters. MATERIALS AND METHODS Institutional review board approval and patient written informed consent were obtained. In this prospective study, 30 patients with CF (17 men and 13 women; mean (SD) age, 30.2 (9.2) years; range, 19-52 years) were included. Chest CT was acquired by unenhanced low-dose technique for clinical purposes. Lung MRI (1.5 T) comprised T2- and T1-weighted sequences before and after the application of 0.1-mmol·kg gadobutrol, also considering lung perfusion imaging. All CT and MR images were visually evaluated by using 2 different scoring systems: the modified Helbich and the Eichinger scores. Signal intensity of the peribronchial walls and detected mucus on T2-weighted images as well as signal enhancement of the peribronchial walls on contrast-enhanced T1-weighted sequences were additionally assessed on MRI. For the clinical evaluation, the pulmonary exacerbation rate, laboratory, and pulmonary functional parameters were determined. RESULTS The overall modified Helbich CT score had a mean (SD) of 15.3 (4.8) (range, 3-21) and median of 16.0 (interquartile range [IQR], 6.3). The overall modified Helbich MR score showed slightly, not significantly, lower values (Wilcoxon rank sum test and Student t test; P > 0.05): mean (SD) of 14.3 (4.7) (range, 3-20) and median of 15.0 (IQR, 7.3). Without assessment of perfusion, the overall Eichinger score resulted in the following values for CT vs MR examinations: mean (SD), 20.3 (7.2) (range, 4-31); and median, 21.0 (IQR, 9.5) vs mean (SD), 19.5 (7.1) (range, 4-33); and median, 20.0 (IQR, 9.0). All differences between CT and MR examinations were not significant (Wilcoxon rank sum tests and Student t tests; P > 0.05). In general, the correlations of the CT scores (overall and different imaging parameters) to the clinical parameters were slightly higher compared to the MRI scores. However, if all additional MRI parameters were integrated into the scoring systems, the correlations reached the values of the CT scores. The overall image quality was significantly higher for the CT examinations compared to the MRI sequences. CONCLUSIONS One major diagnostic benefit of lung MRI in CF is the possible acquisition of several different morphologic and functional imaging features without the use of any radiation exposure. Lung MRI shows reliable associations with CT and clinical parameters, which suggests its implementation in CF for routine diagnosis, which would be particularly important in follow-up imaging over the long term.

Modeling postglacial vegetation dynamics of temperate forests on the Olympic Peninsula (WA, USA) with special regard to snowpack

Past and future forest composition and distribution in temperate mountain ranges is strongly influenced by temperature and snowpack. We used LANDCLIM, a spatially explicit, dynamic vegetation model, to simulate forest dynamics for the last 16,000 years and compared the simulation results to pollen and macrofossil records at five sites on the Olympic Peninsula (Washington, USA). To address the hydrological effects of climate-driven variations in snowpack on simulated forest dynamics, we added a simple snow accumulation-and-melt module to the vegetation model and compared simulations with and without the module. LANDCLIM produced realistic present-day species composition with respect to elevation and precipitation gradients. Over the last 16,000 years, simulations driven by transient climate data from an atmosphere-ocean general circulation model (AOGCM) and by a chironomid-based temperature reconstruction captured Late-glacial to Late Holocene transitions in forest communities. Overall, the reconstruction-driven vegetation simulations matched observed vegetation changes better than the AOGCM-driven simulations. This study also indicates that forest composition is very sensitive to snowpack-mediated changes in soil moisture. Simulations without the snow module showed a strong effect of snowpack on key bioclimatic variables and species composition at higher elevations. A projected upward shift of the snow line and a decrease in snowpack might lead to drastic changes in mountain forests composition and even a shift to dry meadows due to insufficient moisture availability in shallow alpine soils.