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Departments of 1Medicine and 2Radiology, and 3School of Medicine University of California, San Diego, La Jolla, California
Submitted 14 November 2006 ; accepted in final form 22 March 2007
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ABSTRACT |
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 TOP ABSTRACT METHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
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functional magnetic resonance imaging; lung perfusion; lung density; gravity
In contrast, although studies in animals using microspheres have demonstrated a gravitational influence on pulmonary perfusion (12, 13, 15), the data from these studies have shown that there is greater perfusion heterogeneity within an isogravitational plane than across gravity. This is a consistent finding in a variety of animal species, not only in quadrupeds such as horses (19) and pigs (14), but also in primates such as baboons who assume an upright posture (12). These studies suggest that gravitational influences account for only 1–25% of the variability in regional perfusion, with the remainder being attributed to the influences of vascular structure (12, 14, 19).
That the lung has the potential to distort under its own weight has been discussed by many authors (3, 23, 24, 36) particularly as it affects the distribution of ventilation and alveolar size. For example, in dog lungs frozen in situ, there are gravitationally dependent gradients in alveolar size (11), with smaller alveoli in dependent portions of the lung. Consistent with this, there are vertical gradients in lung density and, in the supine posture, the density of dependent regions of the lung is 
40% greater than the nondependent portions of the lung (6). Chest radiographs obtained at total lung capacity, functional residual capacity, and residual volume during parabolic flight also show gravitational variation in regional lung density, with an increase in density in the upper lung zones at all lung volumes during microgravity (33) compared with 1 G. However, the effect that nonuniform lung density has on the measurement of the distribution of pulmonary perfusion using different techniques and on pulmonary perfusion itself has been less well described.
A common theme of lung perfusion measurements discussed above relates to the way in which perfusion is measured, either as flow per unit volume or the lung as a whole. When the intact lung is sampled as with inert gas washout studies (CO2 or 133Xenon), the precise volume of lung sampled is unknown, or in the case of positron emission tomography and MRI, perfusion is measured as milliliters of blood per minute per cubic centimeter of lung. Thus for measurements made in intact human lungs, the effect of gravity on lung distortion and regional density is potentially important, as any compression of dependent lung regions will be reflected in measures of regional perfusion. This is because a given volume of lung in the dependent portion of the lung will contain more lung tissue (capillaries) and less air and will thus have a greater density than in nondependent regions. In addition, blood contained within the pulmonary circulation will provide additional deformation under gravity according to its distribution in the lung. This is not the case with perfusion measurements made with microspheres, because after the infusion of microspheres in situ, the lung is washed of blood, air dried, and inflated to total lung capacity. Thus any effects of lung distortion and nonuniform density present in the in situ lung due to gravity are lessened and the resulting measurements of microsphere location have the potential to underestimate any effect that regional variations in lung density, whether gravitationally based or otherwise, will have on the measurement of perfusion (9).
Regional pulmonary perfusion can be quantified using high-resolution MRI techniques (4, 18, 20), as can regional proton (water) density (16). The application of these techniques in the lung has been recently reviewed (21). The importance of these high-resolution (0.14 cm3) techniques are that, combined, they allow the effects of gravity on regional lung perfusion to be considered independently and virtually simultaneously from the effects on regional lung density.
The purpose of this study was to examine the combined vertical gradients in regional blood flow and lung density in the normal human lung. We obtained measures of regional lung perfusion per volume of lung, comparable to the original studies of West and Dollery (45). These measures include the distorting effects of gravity on the lung architecture. We also measured lung proton density to calculate perfusion per gram of lung water, to allow for variation of regional lung density. We hypothesized that the gravitationally based gradients in regional pulmonary perfusion would be reduced when the effects of density are considered, consistent with the discrepant results between microsphere and in situ data. The results of this study are consistent with a simple conceptual model: that the lung behaves like a Slinky, a deformable spring that distorts under its own weight.
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METHODS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
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This study was approved by the Human Subjects Research Protection Program of the University of California, San Diego. Six healthy subjects (2 female, 4 male, age = 25 ± 1 yr, weight = 73.6 ± 8.3 kg) participated after giving informed consent. Subjects underwent screening using pulmonary and MRI safety questionnaires, followed by a medical history and physical exam.
Data Collection
Each subject underwent MRI scanning using a Vision 1.5 T whole body MR Scanner (Siemens Medical Systems, Erlangen, Germany). All sequence parameters were kept within US Food and Drug Administration guidelines for clinical magnetic resonance examinations. Subjects were positioned supine in the scanner, with a custom-designed rigid PVC tubing cage positioned over the torso. The phased-array torso coil was positioned on this frame, ensuring a constant distance between the anterior and posterior elements of torso coil. A water phantom doped with gadolinium (Berlex Imaging, Magnevist, 469 mg/ml gadopentetate dimeglumine, 1:5,500 dilution) to T1 and T2 values, approximating that of blood, was placed next to the subject within the field of view for absolute quantification of pulmonary perfusion and proton density (see below). Perfusion, proton density, and coil correction data (all described below) were acquired by imaging the right lung in the sagittal plane to eliminate artifact from the aorta and heart present within the left hemithorax. Sequential 15-mm slices were obtained in triplicate and a constant level of breath holding was ensured by overlaying sequential images from the same slice and visually inspecting for discrepant lung volumes, which were discarded. Data were acquired during 8–10 s of breath holding at functional residual capacity starting in the medial lung adjacent to the heart and progressing laterally until signal intensity was reduced to <50% of baseline or until the imaging plane included the lateral chest wall. This was accomplished in three to five slices for each subject, depending on lung size. Of these, the middle three slices were selected for each subject or, in the case where four slices were obtained, data from the most lateral slice was not used.
Correction for coil inhomogeneity. To maximize the signal-to-noise ratio in the pulmonary perfusion and proton density data (described below), a torso coil was used, which has substantially higher gain than the body coil built into the scanner. However, unlike the body coil, which is quite homogeneous, the torso coil exhibits a degree of inhomogeneity in signal strength that varies in all three directions. To correct for this inhomogeneity, the image signal obtained from the torso coil was corrected to the homogeneous (but noisy) body coil signal for each subject individually as follows: 15-mm-thick 2D standard proton density images were acquired in the same slice location using a FLASH (Fast Low-Angle SHot) sequence (see below), one with the torso coil and one with body coil. Each of two images was smoothed by taking the 2D Fourier Transform, applying a Gaussian smoothing function, and transforming back into the image domain. The resultant images were heavily smoothed with a maximum spatial frequency across the field of view of approximately two cycles: approximately twice the spatial frequency of the torso coil elements (approximately a 5-cm resolution). The two smoothed images were divided to define the low spatial frequency coil sensitivity function, which was then multiplied by all images obtained using the torso coil on a voxel-by-voxel basis.
Quantification of regional pulmonary perfusion with arterial spin labeling.
Regional pulmonary blood flow was assessed using a 2D arterial spin labeling (ASL)-flow-sensitive alternating inversion recovery with an extra radiofrequency pulse (FAIRER) sequence with a half-Fourier acquisition single-shot turbo spin-echo (HASTE) imaging scheme. The 15-mm-thick sagittal slices have a field of view of 40 x 40 cm and a resolution of 256 x 128 pixels, therefore voxels of 1.5 x 3 x 15 mm (0.07 cm3) were obtained. These ASL image files were later resized during postprocessing to match the voxel size of the FLASH proton density images (3 x 3 x 15 mm, giving an effective resolution of 0.14 cm3) using bilinear interpolation in MATLAB (The MathWorks, Natick, MA). Once the subtracted ASL image was corrected for coil inhomogeneity, as described previously, pulmonary blood flow was quantified in milliliters per minute per cubic centimeter. Assuming that the water phantom and blood have matched T1 and T2 (the phantom is doped to achieve this) and the T1 of lung at 1.5 T is 1,350 ms (28), then for a given R-R interval and inversion time (TI), pulmonary blood flow can be quantified from the subtracted ASL signal provided it is corrected to the signal resulting from the water phantom in the raw images. The quantified flow for each voxel was expressed in units of milliliters of blood per minute per cubic centimeters of lung (averaged over a complete cardiac cycle). The technique for quantifying regional pulmonary perfusion was modified from one previously reported (30, 32) to allow for acquisition of data within a single breath hold (4). It has been recently described in detail (4, 21) and is therefore only briefly described here.
ASL exploits the capability of MRI to invert the magnetization of protons (primarily in water molecules) in a spatially selective way using a combination of radiofrequency pulses and spatial magnetic field gradient pulses. By inverting the magnetization of arterial blood, these "tagged" protons in blood act as an endogenous tracer. During each measurement, two images are acquired during a single breath hold of each lung slice with the signal of blood prepared in a different way. Then the two images are subtracted, canceling the stationary signal, to give a quantitative map of pulmonary perfusion (4). In the first image, termed the "tag" image, the magnetization of the arterial blood both inside and outside the imaged section is inverted at the beginning of the experiment with a preparatory inversion (180°) pulse applied to the whole lung (a spatially nonselective inversion). In the second "control" image, a preparatory inversion (180°) pulse is applied only to the section being imaged (a spatially selective inversion), leaving the arterial blood outside the imaged section undisturbed. In both images, a spatially selective 90° pulse is also applied to the imaged section immediately after the preparatory inversion pulse. The effect of these radio frequency pulses during the preparation phase is that the static magnetization within each voxel of the image plane is reduced to near zero in both experiments, but the blood magnetization outside the imaging plane is fully inverted prior to the tag image but fully relaxed prior to the control image. In both cases, after a delay encompassing 80% of one R-R interval, the images are acquired. During this delay, blood flows into each voxel of the imaged section and there is also relaxation of the magnetization. The static magnetization relaxes identically in both cases, so when the two images are subtracted, this signal is cancelled. In the tag image, the inverted magnetization of blood has relaxed part way to equilibrium, while for the control image the magnetization of blood outside the imaging plane remains fully relaxed (giving an strong magnetic resonance signal). The difference signal (control – tag) measured for each voxel then reflects the amount of blood delivered during the interval TI, weighted with a decay factor due to the relaxation of the blood magnetization during that interval. In the ASL difference image (Fig. 1A), the signal intensity for each voxel is proportional to the amount of blood delivered during one heart cycle (TI) and so is proportional to the local pulmonary blood flow expressed as milliliters per minute per cubic centimeter.
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Density-corrected perfusion. Perfusion expressed in units of milliliter per minute per gram lung can be approximated by dividing the image acquired by ASL, which has the units of milliliters per minute per cubic centimeter lung, by the FLASH image of proton density (in g H2O/cm3 lung) to give perfusion in milliliters per minute per gram lung (tissue + blood). A mutual information-based technique that included translation and rotation was used to register the two images (39), and the ASL image was divided by the FLASH image to give perfusion in milliliters per minute per gram using a custom-designed program in MATLAB. To the extent that regional lung density is reflected by the water content, this then reflects perfusion in milliliters per minute per gram lung.
Data Analysis
Data representation. For each image acquired as described above (ASL perfusion, FLASH density, ASL/FLASH), the data were analyzed in the following manner. For each image, mean, SD, and relative dispersion (also know as the coefficient of variation, a global index of heterogeneity, defined as the standard deviation/mean, where the larger the relative dispersion, the more heterogeneous the distribution) was calculated. The vertical distributions (distance above the most dependent portion of the lung for each subject) were plotted for each slice for perfusion (in ml·min–1·cm–3), density (g/cm3), and perfusion normalized for density (ml·min–1·g–1). The relationship between vertical height and perfusion, density, and density-normalized perfusion was characterized using least squares linear regression and the slope and strength of the association (R2) obtained. Since distributions of perfusion and proton density across vertical distances may not necessarily be best expressed as a linear relationship, each sagittal slice was divided into three gravitationally based regions of interest: dependent, middle, and nondependent regions to allow for comparison between regions. The image with the greatest anterior to posterior width was selected from the three contiguous sagittal images and divided horizontally into three regions of interest with equal vertical thickness based on the maximum anterior-posterior dimension of the lung. The remaining two contiguous lung slices were divided into three regions of interest using the same horizontal coordinates. Mean perfusion, density, and density-normalized perfusion were obtained for each slice and region.
Statistical analysis. Linear regression (Statview, 5.0 SAS Institute, Cary, NC) was used to evaluate the linear relationships between the vertical height and ASL perfusion, FLASH lung density, and density-normalized perfusion (ASL/FLASH). These relationships were evaluated individually for each subject, and the slopes of the relationships between vertical height and the variable of interest were evaluated using a one group t-test comparing the means to zero. ANOVA for repeated measures was used to statistically evaluate changes in the major dependent variables over the three gravitational regions (3 levels: nondependent, intermediate, dependent region). Dependent variables for this analysis were ASL measurement of perfusion in units of milliliters per minute per cubic centimeter of lung, lung density as measured by FLASH in units of grams per cubic centimeter of lung, and ASL perfusion normalized for lung density (ASL/FLASH, ml·min–1·g–1). Where overall significance occurred, post hoc testing was conducted using Student's t-testing. All data are presented as means ± SD, the null-hypothesis (no effect) was rejected for P < 0.05, two tailed.
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RESULTS |
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All subjects tolerated the study well. Heart rate averaged 66 ± 12 beats/min over the course of the study, and mean arterial oxygen saturation measured by pulse oximetry was 97.4 ± 1.0%. The total duration of the study was 1 h.
Lung Perfusion
Figure 1A shows the distribution of pulmonary perfusion (ASL, ml·min–1·cm–3 lung) from a middle lung slice in a representative subject lying supine in the magnetic resonance scanner, after correction for coil heterogeneity. Lighter shades of gray denote greater flow. Figure 2A shows flow per voxel as a function of distance from the most dependent portion of the lung for all three slices in all six subjects, and Fig. 2B shows the same data averaged for voxels lying within the same gravitational plane. Over all measurements, perfusion in the right lung averaged 1.7 ± 0.6 ml·min–1·cm–3. Perfusion heterogeneity, as measured by the relative dispersion (SD of signal intensity/mean signal intensity) averaged 0.78 ± 0.23. This value is similar to that previously reported for healthy normal subjects of a similar age (18, 20, 27). There was a significant negative relationship between vertical height and perfusion for each subject, and, on average, perfusion decreased by 3%/cm of height above the most posterior portion (P < 0.0001; Table 1). This slope was significantly different from zero (P < 0.005); however, the strength of the linear relationship, while statistically significant, was relatively weak (mean R = 0.12).
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Lung Density
Figure 1B shows lung density data measured with FLASH (g/cm3) from the same middle lung slice in the same subject as in Fig. 1A, after absolute quantification and correction for coil heterogeneity. Figure 2D shows density per voxel relative to mean density as a function of distance from the most dependent portion of the lung for all three slices in all six subjects, and Fig. 2E shows the same data averaged for voxels lying with the same isogravitational plane. The density of right lung averaged 0.34 ± 0.08 g/cm3. There was a highly significant negative relationship between height from the dependent portion of the lung and lung density (P < 0.005), with the density decreasing, on average, by 4.9%/cm of height (Table 1). The strength of the linear association was much greater than for perfusion (average R = 0.44).
There was a highly significant difference (P < 0.0001) in density between lung gravitational regions such that density was significantly less in the nondependent region and greater in the gravitationally dependent region (Fig. 2F). In the nondependent region, lung density averaged 0.28 ± 0.09 g/cm3 and this progressively increased (P < 0.05) in the intermediate (0.33 ± 0.1 g/cm3) and dependent (0.39 ± 0.09 g/cm3) regions.
Density-Normalized Perfusion
Figure 1C shows data from a middle lung slice in the same subject as in Fig. 1, A and B, after absolute quantification of perfusion and density, image registration, and division of the ASL measure of perfusion by the FLASH density (ASL/FLASH, ml·min–1·g–1) to give a measure of perfusion per gram of lung (tissue + blood). Figure 2G shows density-normalized perfusion graphed as a function of distance from the most dependent portion of the lung for all three slices in all six subjects. Figure 2H shows the same data averaged for voxels lying with the same isogravitational plane. Averaged over all measurements, the density-normalized perfusion of the right lung averaged 5.12 ± 1.8 ml·min–1·g–1. There was no significant negative relationship between height from the most dependent lung region and perfusion when normalized for lung density, and, averaged over all the six subjects, the slope of the linear relationship was not significantly different from zero (P = 0.2). However, there was a significant difference in density-normalized perfusion between lung gravitational regions (P < 0.05, Fig. 2I), with density-normalized perfusion in the midzone of the lung being significantly greater than either the dependent or nondependent regions. However, in contrast to the ASL perfusion data, which was not normalized by density, the nondependent lung regions did not differ significantly from the dependent lung regions.
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DISCUSSION |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
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Distribution of Perfusion and Density in the Lung
The lung presents unique challenges for the measurement of perfusion. In a tissue such as the brain, where the density is close to one, the quantification of perfusion measured as milliliters per minute per gram tissue is not substantially different than perfusion measured by volume (ml·min–1·cm–3). Furthermore, the contribution of blood within the tissue to the overall density of the brain is relatively small [4% (8)]. However, in the lung the measurement of perfusion in milliliters per minute per cubic centimeter gives very different values than the measurement of perfusion in milliliters per minute per gram tissue. This is because the lung density at functional residual capacity is approximately one-third of that of the brain, and the blood volume itself contributes to more than one-half of the weight of the lung (6). Furthermore, lung density changes depending on the lung volume at which measurements are made (e.g., total lung capacity vs. residual volume). In addition, any gravitationally based deformation of elastic structures with accompanying changes in regional lung density will influence the distribution of regional perfusion in a gravitationally dependent fashion. That the lung would deform under it's own weight was appreciated by West and Matthews (47) more than 30 years ago, as well as by others (3, 10, 11). West and Matthews also pointed out that the regional differences in density would be accentuated by increased blood volume in the dependent portions of the lung. This concept was extended by Brudin et al. (6), who showed that the vascular component was the major contributor to vertical density gradients in the lung. Millar and Denison (36) expanded these observations by a theoretical analysis of computed tomography measures of lung density at different levels of lung inflation. They suggested that the lung behaved as a compressible foam and that the vessels imposed an additional contribution to this density gradient. Recently, the effect that changes in posture have on the distribution of lung tissue were documented, showing that the dependent lung tissue is compressed irrespective of prone or supine posture (38).
The data from the present study demonstrate the effect that these density gradients have on the interpretation of pulmonary perfusion measurements. Although in the 1960s, studies showed gravitational gradients in pulmonary ventilation and alveolar size (7, 35) and numerous authors have referred to this mechanism and its effect on lung ventilation, what has been less-well appreciated is that this gravitational compression has direct and important effects on pulmonary perfusion. In essence, if one considers a lung that has perfectly uniform perfusion per alveolus, any effect that results in a gradient in alveolar size must necessarily result in a similar although reversed gradient in perfusion of the lung when viewed in situ, because the perfusion occurs within the walls of the alveoli. Conceptually, a simple analogy can be used: the lung might be thought of as a deformable structure like a Slinky (Fig. 3), where the greater density of lung tissues in the dependent regions of the lung is analogous to a greater number of coils in the dependent portion of the spring when it is held in a vertical orientation. In this analogy, the pulmonary vessels form part of the coils and so a gradient in density also implies a gradient in overall perfusion. Using this analogy, it can be appreciated that measurements of perfusion in the intact lung will be influenced by this density distribution, because measures made in the nondependent portions will contain fewer blood vessels than in the dependent portions of the lung. In addition, the contribution of the weight of the blood contained with the pulmonary vasculature is important because this has the potential to provide additional deformation of the elastic lung structures under the influence of gravity. These findings have important implications for how data are interpreted and compared between different techniques; measurements made in situ will be subject to the effects that lung density gradients have on perfusion, whereas measurements made in excised lung will minimize these effects.
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8% (3)], but which is comparable to that found in humans in the supine posture [4–5% (1, 37)]. We also observed a vertical gradient in density of 5%/cm in height (Fig. 2, D–F), which is similar to that reported in the supine lung measured with single photon emission-computed tomography (1) during quiet breathing and with computed tomography at residual volume (36); however, the magnitude of this lung density gradient is less than reported by others (37) measured at different lung volumes. The effects of this vertical gradient in regional lung density can be seen in Fig. 2H; the vertical gradient in perfusion is largely eliminated when normalized for regional density.
The implication of this observation is that a substantial part of the vertical gradient observed in perfusion in Fig. 2A in the present study is a reflection of gravitationally induced lung deformation as opposed to hydrostatic gradients in pulmonary vascular pressures. The extent of this can be expressed by the regression of perfusion and density, which yields a correlation of R = 0.40, thus 20% of the observed regional variation in perfusion in our supine subjects at functional residual capacity can be explained by regional variation in density. This also suggests that when lungs are excised, washed of blood, air dried, and inflated to total lung capacity that vertical density gradients may be reduced. Thus we predict that if measured in supine humans at similar lung volumes and at a similar resolution, vertical gradients in blood flow measured by microspheres may more closely resemble the pattern in 2H.
Effects of Lung Volume and Supine Posture on the Gravitational Influence on the Distribution of Pulmonary Blood Flow and Density
A necessary limitation of MRI is that most scanners can only accommodate recumbent postures for data collection. In upright postures, the gravitational influences are expected to be greater because the vertical height of the lung is greater, and thus both gravitational distortion and hydrostatic effects are greater. In addition, we imaged the lung at functional residual capacity as this improves the signal-to-noise characteristics of the ASL images obtained (31). However, the vertical gradients in regional perfusion are less at functional residual capacity than at total lung capacity (22). Thus our data likely underestimate vertical influences in perfusion per cubic centimeter that might be found in the upright lung at total lung capacity. Nonetheless, the overall similarity between our data obtained at functional residual capacity and that using both radioisotopes (22) and microspheres (12) is striking. It should be noted that the heterogeneity within plane and the lower R2 for the relationship between height and perfusion in the present data is an expected consequence of the approximately one order of magnitude higher resolution of our MRI technique (0.14 cm3) compared with microspheres (1.2 cm3), because greater variation in perfusion will be appreciated and, therefore, the SD will be larger as the resolution of measurement increases. Vertical gradients in lung density have been shown to vary with lung volume, and, in the supine posture, the density gradients at total lung capacity are less than those at residual volume (36). Thus, depending on the conditions of measurement, including posture or lung volume, the net effect on lung density gradients and density normalized perfusion may be greater or less than identified by the current data.
It should be pointed out that our results do not exclude the presence of hydrostatically induced gradients in blood flow [the zone model (45, 46)] and the results in Fig. 2, G–I, are consistent with what might be expected in a normal supine human. Given the relatively anterior position of the heart in the chest and typical pulmonary vascular pressures, one would expect most of the lung to be in zone 3, only a small zone 2 region, and in all likelihood, no zone 1. That is what is seen in Fig. 2H, where over much of the vertical extent of the lung, density normalized perfusion is quite uniform (in keeping with zone 3 conditions in which the arterial to venous pressure difference is constant). In the uppermost part of the lung, density-normalized perfusion falls with increasing height (consistent with zone 2 conditions). In the lowermost regions, density-normalized perfusion again decreases [consistent with zone 4 conditions (22)]. In keeping with this idea, electron-beam computed tomography data obtained in the supine posture and under positive pressure ventilation at a higher lung volume than the present study, where more zone 2 is likely, showed a persistent gradient in density-normalized perfusion (23). However, it is important to recognize that our measurements of lung density encompasses both the water-containing portion of lung alveolar tissue and the blood in the pulmonary vasculature. Thus a region may have high density not only because of compression of the lung, but also because of a larger intravascular volume, and the relative contributions of these cannot be distinguished from our data. In addition, it is worth noting the high degree of perfusion heterogeneity within an isogravitational plane, and the low overall correlation between perfusion and vertical height, which is less than previously described for the mammalian lung (12, 13) when evaluated at this high level of resolution. This suggests a substantial contribution from factors other than gravitational forces on the distribution of pulmonary blood flow.
Technical Limitations
MRI using ASL techniques has been widely used to determine regional blood flow in other organ systems, such as brain (8). The technique has been validated in tube-flow models (2), heart (40), brain (43) and skeletal muscle (42). However, there are some limitations to the technique that should be considered when evaluating our data. ASL-FAIRER provides an image map of all tagged protons that move into the imaging slice during the delay between tagging and image acquisition. Thus components of both pulmonary arterial and venous blood flow are likely present, the significance of which is presently unclear (4). In addition, because the technique relies on a delay between tagging of protons and imaging, some slow-moving tagged protons may not have entered the imaging plane. These theoretical considerations and a detailed description of the methods used in the present study to measure pulmonary perfusion and lung density, including the advantages and disadvantages, have been described in detail (4, 16, 21, 44). Our measurement of perfusion for the right lung averaged 1.7 ml·min–1·cm–3, which is similar to values expected on theoretical grounds (4) and those measured using positron emission tomography (5) and MRI (4, 44). Similarly, our density measurements are limited both by the need to correct for rapid signal loss in the lung [a short T2* (17)] and by the extent that a small portion of the lung tissue may not generate MRI signal. Nevertheless, our density measurements are close to what might be expected. Since the weight of the human lung is about 1 kg, including the blood in the pulmonary vasculature (6), the average density of the lung at functional residual capacity is expected to approximate 0.3 g/cm3, and perfusion expressed per gram tissue is expected to be about 5 ml·min–1·g–1. These values have also been confirmed by other studies (1, 5, 44) using a variety of techniques and are also remarkably close to the values for lung density of 0.34 g/cm3 and perfusion per gram of 5.15 ml·min–1·g–1 measured in the present study.
In conclusion, the data in the present study suggest that in situ variations in regional lung density affect the interpretation of perfusion data in the lung. The vertical gradients in regional pulmonary perfusion are greatly reduced when normalized for regional variation in lung density, providing a means to reconcile the apparently discrepant results between microsphere and in situ data. A simple model, that the lung behaves like a Slinky, a spring that deforms under its own weight, provides a simple conceptual model. Because it is already well established that lung deformation due to gravity is present in the normal lung and because the blood within the pulmonary vasculature forms a large proportion of the total lung weight with the potential to provide additional deformational forces under gravity, such effects need to be considered in the interpretation of regional measures of pulmonary perfusion.
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ACKNOWLEDGMENTS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
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FOOTNOTES |
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The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
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