Computed tomography (CT) has shown that emphysema is more extensive in the inner (core) region than in the outer (rind) region of the lung. It has been suggested that the concentration of emphysematous lesions in the outer rind leads to a better outcome following lung volume reduction surgery (LVRS) because these regions tend to be more surgically accessible. The present study used a recently described, computer-based CT scan analysis to quantify severe emphysema (lung inflation > 10.2 ml gas/g tissue), mild/moderate emphysema (lung inflation = 10.2 to 6.0 ml gas/g tissue), and normal lung tissue (lung inflation < 6.0 ml gas/g tissue) present in the core and rind of the lung in 21 LVRS patients. The results show that the quantification of severe emphysema independently predicts change in maximal exercise response and FEV(1). We conclude that a greater extent of severe emphysema in the rind of the upper lung predicts greater benefit from LVRS because it identifies the lesions most accessible to removal by LVRS.
In order to successfully use a model for parameter identification, it must be carefully analyzed. Current analysis methods, however, are ad hoc and provide only partial information. We extended these methods through the application of stacked dimensions, a scientific visualization method. The end result of our extensions are multi-dimensional parametric model-images. These images depict a model as a function of all its parameters in a single graphic. We applied parametric model-images to model verification (behavioral analysis), sensitivity analysis, and identifiability analysis. We applied our methodology to the evaluation of pulmonary vascular capillary-transport models. Results have shown that the visualization-based method provides a more complete view of a model's behavior and its other characteristics. Furthermore, our method has also proven to be more computationally efficient than the traditional approaches.
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