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(Chest. 2003;123:1655-1663.)
© 2003 American College of Chest Physicians

An Automated Approach to Quantitative Air Trapping Measurements in Mild Cystic Fibrosis*

Michael L. Goris, MD, PhD; Hongyun J. Zhu, MD; Francis Blankenberg, MD; Frandics Chan, MD, PhD and Terry E. Robinson, MD

* From the Divisions of Nuclear Medicine/Radiology (Drs. Goris and Zhu), Pediatric Radiology/Radiology (Dr. Blankenberg), Chest Imaging/Radiology (Dr. Chan), and Pediatric Pulmonary Medicine/Pediatrics (Dr. Robinson), Stanford University, Stanford, CA.

Correspondence to: Michael L. Goris, MD, PhD, MC: 5281, Division of Nuclear Medicine H0101, Stanford University School of Medicine, Stanford, CA 94305-5281; e-mail: mlgoris{at}stanford.edu


   Abstract
TOP
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
 References
 
Purpose: To automatically derive the degree of air trapping in mild cystic fibrosis (CF) disease from high-resolution CT (HRCT) data, and to evaluate the discriminating power of the measurement.

Materials and methods: The data consist of six pairs of anatomically matched tomographic slices, obtained during breath-holding in triggered HRCT acquisitions. The pairs consist of an inspiratory slice, at >= 95% of slow vital capacity, and an expiratory slice at near residual volume (nRV). The subjects are 25 patients with mild CF and 10 age-matched, normal control subjects.

Subjects: Lung segmentation is automatic. The limits defining air trapping in the expiratory slices are determined by the distribution of densities in the expanded lung. They are modulated by density changes between expiration and inspiration. Air trapping defects consist of contiguous low-density voxels. The difference between patients and control subjects was evaluated in comparison to pulmonary function test (PFT) results and lung density distribution descriptors (global density descriptors).

Results: In mild CF, air trapping does not correlate with global PFT results, except for the ratio of residual volume (RV) to total lung capacity (TLC); however, the size of air trapping defects was the best discriminator between patients and control subjects (p < 0.005). Of PFT results, only RV/TLC reached significance at p < 0.05. The global density descriptors reached near significance in the nRV images only.

Conclusion: Air trapping defined as defect size and measured in an objective automated manner is a powerful discriminator for mild CF.

Key Words: air trapping • cystic fibrosis • high-resolution CT


   Introduction
TOP
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 Conclusion
 References
 
Air trapping is part of the pathophysiology of a number of pulmonary disorders, including bronchiolitis obliterans,1 2 reactive airway disease,3 chronic bronchitis,4 atypical pneumonia,5 bronchiectasis,6 emphysema,7 sarcoidosis,8 eosinophilic granuloma,9 and cystic fibrosis (CF).10 11 12 13 14 15 16 17 18 Inspiratory high-resolution CT (HRCT) has been established as the imaging technique of choice for the assessment of pulmonary parenchymal abnormalities in these disorders19 20 21 22 23 ; however, while healthy lungs demonstrate homogenous attenuation that uniformly increases on expiration, in the case of small airway obstruction the increase in attenuation is heterogeneous and focal areas of low attenuation are present.24 25 The conspicuity of the focal abnormalities is greatly accentuated on expiratory HRCT images in which the increase in attenuation of normal lung can be > 100 Hounsfield units (HU) greater than that found within regions of "air trapping."26 27

While there is little doubt that expiratory HRCT can depict regions of air trapping, a indirect reflection of small-airways obstruction, there appears to be no agreement in the literature on how best to quantify these abnormalities. Efforts to do so borrow from the extensive HRCT work done in patients with asthma and emphysema and correlations with standard pulmonary function test (PFT) results. Subjective visual grading and objective CT quantification with a number of lung density masks have been applied to two-dimensional and three-dimensional images with varying degrees of success. Other studies have suggested a role for respiratory HRCT triggering employing modified spirometry devices as a means to generate more reproducible measurements of air trapping in both individuals and large groups of patients at predetermined lung volumes.28

Variations in methodology (imaging protocols and technical parameters) may obscure the universality of the results. Important factors affecting the results are the sampling density (number of slices), interpatient variation in lung density even in the inspiratory images, the degree of expiration at the time of imaging, and the selection of the density threshold defining air trapping. While increasing the sampling (the number of slice images) likely increases precision, this does not come without cost in time for the interpreting radiologist and an increased radiation burden to the patient. A general computerized normalization algorithm combining the data sets from anatomically matched expiratory and inspiratory HRCT axial slices may be a partial solution to the problems of consistency, comparability, time, and cost seen with other analytic methodologies designed to quantify the degree of air trapping.

In this study, we describe a method in which air trapping is defined as coherent regions of low density in expiratory HRCT image slices. The lower density limits defining air trapping are set on the basis of the density distribution in the corresponding expanded or inspiratory lung slices, but are modulated by the degree of expiration as estimated by the change in density of the densest 90th percentile of the lung at inspiration and expiration. We have applied this analytical method to a group of 25 patients with mild CF lung disease and 10 age-matched control subjects using six anatomically matched, HRCT spirometry-triggered slice pairs. We also correlated air-trapping analyses with standard PFT results obtained within 4 h of HRCT.


   Materials and Methods
TOP
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 Conclusion
 References
 
Subjects
The tested population consisted of 25 children and adolescents with mild CF lung disease and 10 unaffected siblings or normal subjects matched over a similar age range and serially entered from March 2000 to March 2001. The CF group consisted of 10 female and 15 male subjects (mean ± SD age, 10 ± 3.62 years; range, 6.4 to 17.9 years). The control group consisted of six female and four male subjects (mean age, 11.7 ± 3.20 years; range, 7.1 to 17.0 years). Mild CF was defined by a positive pilocarpine iontophoresis sweat chloride test result and/or CF gene mutation analysis and PFT results demonstrating a percentage of predicted FVC > 85% and percentage of predicted FEV1 >= 69%. The control siblings were asymptomatic and had percentage of predicted FVC values >= 93% and percentage of predicted FEV1 >= 84%. Each participant gave informed consent for the procedure after risks and benefits had been explained in strict accordance with the internal review board for human research subjects.

PFTs
Lung function was assessed by standard spirometry utilizing a portable pulmonary function system (Vmax model 229; SensorMedics Corporation; Yorba Linda, CA), and by body plethysmography (model 1085; Medical Graphics Corporation; St. Paul, MN). Spirometry and lung volumes were obtained in the sitting position. Supine pulmonary function measurements were also obtained for the purposes of spirometer triggering of the CT scanner.28 Pulmonary function measurements (FVC, FEV1, and forced midexpiratory flow [FEF25–75%]) were expressed as percentages of predicted (except for FEV1/FVC and residual volume [RV]/total lung capacity [TLC] ratios) based on normal prediction equations derived from the data of the Harvard Six Cities Study.29

HRCT
In each patient, supine-position, spirometer-triggered inspiratory and expiratory HRCT images were obtained using an electron-beam CT scanner (C-150XP; Imatron Corporation; South San Francisco, CA) triggered by a portable pulmonary function unit (Vmax model 229). The HRCT studies were performed using the following parameters: 130 kilovolt peak, 650 mA, 1.5-mm collimation, and a scan time of 100 ms. Images were reconstructed using a high-resolution reconstruction algorithm (VSHARP kernel; Imatron Corporation). Contiguous inspiratory HRCT images (1.5-mm axial slices) were obtained from 1.5 cm above the aortic arch to 1 cm above the right hemidiaphragm at a lung volume >= 95% of slow vital capacity. The six breath-holding, expiratory, 1.5-mm axial slices were equally spaced from the level of 1 cm above the aortic arch to 1 cm above the right hemidiaphragm at a lung volume at near residual volume (nRV).28 After completion of the examination, anatomic pairing of expiratory and inspiratory slices was performed by three of the authors of this article, who by consensus reading generated six paired sets of HRCT images for analyses. In a subset of 10 patients with CF, the data also consists of matched slices at two anatomic levels, acquired at >= 95% of full capacity, at nRV, and at near functional residual capacity (nFRC).

Image Processing
The lungs were segmented automatically using the observation that lung is surrounded by higher density tissues (> 0 HU), which separates it from the surrounding air. The pixels with air density were eliminated around the high-density tissues first. Subsequently, the images were filtered with 11 x 11 median filter; only those remaining pixels located within the high-density tissue boundaries between - 985 HU and - 250 HU were considered as belonging to lung tissue. The lower threshold combined with the median filter eliminated large bronchial air spaces, and the upper threshold combined with the median filter eliminates most large bronchial densities and vascular structures.

Image Analysis
A combined frequency distribution of densities was computed separately for all inspiratory and expiratory slices as shown in Figure 1 . These distributions were characterized by their respective average, median, mode, and 90th percentile. In addition, the change in 90th percentile values in inspiration and expiration ({Delta}90) was computed.



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  		Figure 1.. Combined frequency distribution of densities in six inspiratory and expiratory (nRV) slices, with 3° of air trapping. The density distributions within the defects are indicated under the histogram of the densities in the expiratory slices, in shades of gray. Note that defect 1 includes 2 and 3, and defect 2 includes 3. Not all pixels with densities within that range are included.

 
Three thresholds were set for the definition of air trapping on the basis of the expanded lung characteristics as shown in Figure 1 . If T(i) is the ith threshold, X is the 90th percentile, and Y the median of density distribution in the inspiratory image, and D is the {Delta} value, then:

The first term sets the limit at the 90th percentile density of the expanded lung. The second term divides the distance between median and 90th percentile in three intervals and sets a threshold at the 90th percentile minus a third of the interval times 0, 1, or 2 for the three thresholds, respectively.

The third term compensates for different degrees of expiration by normalizing for {Delta}90. In the normal population, the average {Delta} value was 343 HU. If the value is less than that, the thresholds are moved toward lower densities.

For each threshold, a defect area (number of voxels) is defined in all expiratory slices and summated over all slices. Voxels are included in the air-trapping defect according to their HU value and propinquity to other voxels with low densities. Propinquity is defined by a 15 x 15 median filter applied to the binary image in which the voxels below threshold value are set at 1 and the others at 0. The size of the defect areas is expressed as a fraction of the number of voxels in the individual or summated expiratory slices (A1 to A3) or inspiratory lung slices (D1 to D3). A1 and D1 represent defects on the basis of liberal criteria; A3 and D3 represent defects on the basis of stringent criteria. For A and D, a summed weighted defect was defined: A1 and D1 are weighted by a factor of 1, A2 and D2 by a factor of 2, and A3 and D3 by a factor of 3. The analysis of a patient data set (six inspiratory/expiratory slice pairs) takes less than a minute with a 2.4-gigahertz Pentium 4 processor (Intel Corporation; Santa Clara, CA) with 500 megabyte random access memory and requires no special skills.

Statistics
The linear correlation between the degree of air trapping and global PFT results was defined; however, to define how well the proposed measure discriminates between age-matched unaffected and minimally affected subjects, the statistical analysis consisted of an unpaired t test, two-tailed with unequal variances. For significance, the value of p was set at 0.05.


   Results
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 Conclusion
 References
 
Each patient data set, consisting of six anatomically matched axial slices, underwent automated lung segmentation of both inspiratory and expiratory HRCT images (Fig 2 ). On morphologic analyses, there was a heterogeneous distribution of air trapping defects for each CF subject across expiratory scans as shown from a representative individual detailed in Figure 3 . The defect sizes did vary from slice to slice as expected if the defects follow anatomic lung segments. Indeed, anatomic segments are expected to markedly differ in cross-sectional areas from pair slice to pair slice (Table 1 ). In contrast, the frequency distribution of lung densities on the inspiratory HRCT images tended to be homogeneous between all levels in this individual and all other patients with CF and normal subjects (Table 2 ).



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  		Figure 2.. Example of the processing of slice No. 4 in a typical CF case. In the first column of the first row (top), the inspiratory HRCT slice is shown next to the segmented lung at the same level. The negative values are the median and 90th in HU. In row 2 (center), the slice images at nRV in the first column, next to the segmented expiratory lung. In row 3 (bottom), the segmented expiratory lung is seen with the three defects in overlay in white. The positive numbers are the percentage of pixels included in the defects. The negative values are the HU of the threshold. The third image in the first row (top right) is the combined histogram at that level. In row 2 (center, right), a composite of the expiratory slice with the three defects in shades of gray.

 


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  		Figure 3.. Illustration of one CF case, with the results of the defect definition on six expiratory levels.

 

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  		Table 1.. Variability of Defect Sizes According to Slice Location*

 

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  		Table 2.. Variability of Global Descriptors of Densities Across Slices in Inspiration and Expiration*

 
The descriptors of the inspiratory distribution of densities were not significantly different between patients with CF and control subjects. In contrast, the differences between CF and control descriptors of the expiratory histogram of densities were closer to significance as shown in Table 3 . This was true especially for the mean, the parameter most likely to be affected by even a small number of pixels with very low densities. Surprisingly, the {Delta}90 did differ significantly between patients with CF and control subjects (335 HU vs 397 HU, p = 0.015), even though it was expected that the pixels contributing to the highest attenuation values in the lung parenchyma (at the 90th percentile) in patients with CF would not represent regions with some degree of air trapping.


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  		Table 3.. Descriptors of the Global Density Distribution at Inspiration and nRV in Patients With Mild CF and Control Subjects*

 
The global PFT results did not discriminate satisfactorily between patients with CF and control subjects, except for the static measure RV/TLC and nearly for percentage of predicted FEF25–75% as shown in Table 4 . There was no significant correlation between the dynamic PFT results (percentage of predicted FEF25–75 and percentage of predicted FEV1) and the degree of air trapping (r < 0.33, p > 0.05), but there was a weak but significant correlation (r > 0.37, p < 0.05) between all measures of defect size and RV/TLC. The fraction of the pulmonary volume with air trapping was the most sensitive discriminator between CF and control, regardless of the definition, as shown in Table 5 .


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  		Table 4.. Comparison of PFT Results Between Subjects With Mild CF and Control Subjects*

 

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  		Table 5.. Cumulative Defect Size Over Six Slices, Expressed as Percentage of Voxels in Inspiratory or nRV Lung Slices, in Patients With Mild CF and Control Subjects*

 
The correction for the degree of expiration based on {Delta}90 did work partially as shown by the improved correlation coefficients in the corrected as compared with uncorrected defect values as shown in Table 6 . The results obtained at nRV and nFRC are more similar when the correction (the third term in the definition of the defect threshold) is included (corrected) than when it is deleted (not corrected).


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  		Table 6.. Effect of Correction for the Degree of Expiration by Modulating the Air Trapping Limits by {Delta}90*

 

   Discussion
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
Conclusion
 References
 
Pulmonary pathology in CF affects both small and large airways. Because of reduced mucociliary clearance, recurrent endobronchial infections, and chronic inflammation, there is progressive obstructive lung disease and subsequent destruction of the airways. Early pulmonary manifestations include bronchiolar and bronchial obstruction.

In CT, small-airway obstruction with air trapping results in lower densities in the affected regions. The lower densities can be detected on images obtained during full inspiration,30 nRV, nFRC,31 32 33 or at unspecified points of the respiratory cycle.34 The analysis generally starts with a segmentation of the lungs that can be manual or semiautomatic.31 34 Further analysis defines air trapping as regions with a density lower than a given threshold or defines a global parameter based on the frequency distribution of densities in the lung fields. The parameters are either the mean or full width at half maximum, or the median and tenth-lowest percentile.31 32 33 Regional assessment is based either on subdividing the lung field in core and rind, in superior, middle, and lower fields or posterior, middle, and anterior segments.33 The density of lung sampling varies from volumetric to as few as two slices.32

The method described here defines air trapping as coherent regions of low density in expiratory images. Coherence is based on contiguity of the affected voxels or pixels. Low density is defined as a function of inspiratory densities, with a correction for the degree of expiration. The method avoids the problems related to manual lung segmentation, essentially based on contouring, in which large bronchi and vessels are included as lung parenchyma. Basing the density threshold defining air trapping on the densities in the inspiratory lung is prompted by the consideration that the inspiratory images are less likely to be abnormal early in the disease. Adapting the threshold to individual patients reflects the interpatient variability of lung densities.

Others35 have successfully used a fixed threshold to define air trapping, rather than refer to the densities in the inspiratory lung. However, it seemed reasonable to take the variations in initial conditions (of the expanded lung) into account, but more detailed studies are needed to demonstrate a definite clinical advantage.

The main thrust of the method is first to base the reference threshold on the inspiratory lung densities of the patient, and second to characterize air trapping as a fraction of the lung involved, rather than on parameters derived from global or regional density distributions in the expiratory lung. In the population of patients and control, defect size was the best discriminator between the groups. Global measures of density distribution in the expiratory lungs did not differ significantly between both groups.

The fact that the dynamic, global PFT results did not discriminate well is partially due to selection bias. Since PFT results are used in the definition of mild disease (in this case FVC >= 85% and FEV1 >= 69%) patients with more severe PFT results were excluded. In addition, the relative contribution of the affected lung regions to the dynamic global function is probably decreased. It is not surprising, however, that the defect size correlates best with the more static measure, eg, RV/TLC a measure of global air trapping.

The correction for the degree of expiration is not perfect for differences of the magnitude of the difference between nRV and nFRC, but sufficient for small variations around a set end point. In this group of subjects, the change in correlation did reach significance for the defects at the highest threshold (A3 and D3) and near significance for the others. The systematic difference between the degree of density change from inspiration to expiration (expressed by {Delta}90) suggest that in this patient group the least affected part of the lung is not exactly normal.

The restriction of the number of expiratory slices is obviously a compromise. Measures within the inspiratory lung are not affected, but precision would be improved with more slices, but at a cost.

The setting of the limits, based on densities in the expanded lung, is not very sensitive to the exact matching of image pairs, since the inspiratory density distribution is nearly identical across all levels. This relative constancy overcomes the difficulty in the exact matching of inspiratory and expiratory slices. This difficulty, however, is probably reflected in the larger coefficient of variation in the D parameters, where the defect size is normalized to the matched inspiratory slice.

The results presented here are exclusive to early disease. In the cases included in the study, mucoid impaction and bronchiectasis were present but did not negatively affect the results.

The technique is adaptable to other types of CT systems, including multidetector CT systems, but would be sensitive to variations in reconstruction kernels. The median filter, however, can be adapted to make the system resolution more comparable between devices. Most CT systems do have an external trigger mechanism, but the delay between trigger and imaging start does vary.

In the absence of respiratory triggering individual variations (between patients or repeated studies on one patient) are expected to be larger. In a set of cases not included in this study, we did find that the result of the absence of triggering was a higher correlation with global PFT results (FEV1). We speculate that the increase in correlation results from the effect of global function on the degree of expiration.

The radiation dose of chest CT imaging for evaluation of air trapping is only a drawback when full chest CTs are utilized (the radiation dose for a full-chest CT using the Philips Mx8000 series scanners [Philips Medical Systems; Bothell, WA], with 120 kilovolt peak, 4 x 2.5-mm collimation mode, 3.2-mm helical slice width, 1.25 pitch, 0.75-s rotation time [30 to 100 mA per slice] ranges from 3.2 to 8.2 mGy for children from 2 to 90 kg36 ). But with faster scanners with 0.5-s to 0.3-s rotation times with 1.0-mm collimation at only six levels (ie, limited chest CT/HRCT imaging), the doses are substantially lower. In addition, new therapies and the recognition that early intervention is useful combine to increase the utility of methods for the evaluation of early disease in potentially affected children.


   Conclusion
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
References
 
Since the method was tested on patients with mild CF only, no conclusion should as yet be made regarding other disease entities (eg, emphysema) or physiologic mechanisms of air trapping (eg, bronchiectasis, asthma), although one expects that air trapping, defined as low density regions, would be quantifiable regardless of the underlying pathology. Our results, however, strongly indicate that regional air trapping can be present in the absence of significant global PFT abnormalities and is a good measure of mild CF lung disease.


   Footnotes
 
Abbreviations: CF = cystic fibrosis; {Delta}90 = change in 90th percentile values in inspiration and expiration; FEF25–75% = forced midexpiratory flow; HRCT = high-resolution CT; HU = Hounsfield unit; nFRC = near functional residual capacity; nRV = near residual volume; PFT = pulmonary function test; RV = residual volume; TLC = total lung capacity

Received for publication July 18, 2002. Accepted for publication October 17, 2002.


   References
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
 References
 

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 Am. J. Respir. Crit. Care Med.Home page
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T. E. Robinson, M. L. Goris, H. J. Zhu, X. Chen, P. Bhise, F. Sheikh, and R. B. Moss
Dornase Alfa Reduces Air Trapping in Children With Mild Cystic Fibrosis Lung Disease: A Quantitative Analysis
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P. A. de Jong, N. L. Muller, P. D. Pare, and H. O. Coxson
Computed tomographic imaging of the airways: relationship to structure and function
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