Partial liquid ventilation (PLV) with high-specific-weight perfluorocarbon liquids has been shown to improve oxygenation in acute lung injury, possibly by redistributing perfusion from dependent, injured regions to nondependent, less injured regions of the lung. Our hypothesis was that during PLV in normal lungs, a shift in perfusion away from dependent lung zones might, in part, be due to vasoconstriction that could be reversed by infusing sodium nitroprusside (NTP). In addition, delivering inhaled NO during PLV should improve gas exchange by further redistributing blood flow to well-ventilated lung regions. To examine this, we used a single transverse-slice positron emission tomography camera to image regional ventilation and perfusion at the level of the heart apex in six supine mechanically ventilated sheep during five conditions: control, PLV, PLV + NTP, and PLV + NO at 10 and 80 ppm. We found that PLV shifted perfusion from dependent to middle regions, and the dependent region demonstrated marked hypoventilation. The vertical distribution of perfusion changed little when high-dose intravenous NTP was added during PLV, and inhaled NO tended to shift perfusion toward better ventilated middle regions. We conclude that PLV shifts perfusion to the middle regions of the lung because of the high specific weight of perflubron rather than vasoconstriction.

To determine the spatial distributions of pulmonary perfusion, shunt, and ventilation, we developed a compartmental model of regional (13)N-labeled molecular nitrogen ((13)NN) kinetics measured from positron emission tomography (PET) images. The model features a compartment for right heart and pulmonary vasculature and two compartments for each region of interest: 1) aerated alveolar units and 2) alveolar units with no gas content (shunting). The model was tested on PET data from normal animals (dogs and sheep) and from animals with experimentally injured lungs simulating acute respiratory distress syndrome. The analysis yielded estimates of regional perfusion, shunt fraction, and specific ventilation with excellent goodness-of-fit to the data (R(2) > 0.99). Model parameters were estimated to within 10% accuracy in the presence of exaggerated levels of experimental noise by using a Monte Carlo sensitivity analysis. Main advantages of the present model are that 1) it separates intraregional blood flow to aerated alveolar units from that shunting across nonaerated units and 2) it accounts and corrects for intraregional tracer removal by shunting blood when estimating ventilation from subsequent washout of tracer. The model was thus found to provide estimates of regional parameters of pulmonary function in sizes of lung regions that could potentially approach the intrinsic resolution for PET images of (13)NN in lung (approximately 7.0 mm for a multiring PET camera).

The pattern of a spatial structure that repeats itself independently of the scale of magnification or resolution is often characterized by a fractal dimension (D). Two-dimensional low-pass filtering, which may serve as a method to assess D, was applied to functional images of pulmonary perfusion measured by positron emission tomography. The corner frequency of a low-pass filter is inversely proportional to the resolution scale. The method was applied to three types of images: random noise images, synthetic fractal images, and positron emission tomographic images of pulmonary perfusion. Images were processed with two-dimensional low-pass filters of decreasing corner frequencies, and a spatial heterogeneity index, the coefficient of variation, was calculated for each low-pass-filtered image. The natural logarithm of the coefficient of variation scaled linearly with the natural logarithm of the resolution scale for the PET images studied (average R(2) = 0.99). D ranged from 1.25 to 1.36 for the residual distribution of pulmonary perfusion after vertical gradients were removed by linear regression. D of the same data without removal of vertical gradients ranged from 1.11 to 1.14, but the fractal plots had systematic deviations from linearity and a lower linear correlation coefficient (R(2) = 0. 96). The method includes all data in the lung field and is insensitive to the effects of misregistration. We conclude that low-pass filtering offers new insights into the interpretation of D of two-dimensional functional images as a measure of the frequency content of spatial heterogeneity.