Quantitative lung SPECT applied on simulated early COPD and humans with advanced COPD
 Pernilla Norberg^{1}Email author,
 Hans Lennart Persson^{2},
 Gudrun Alm Carlsson^{3},
 Björn Bake^{4},
 Magnus Kentson^{5},
 Michael Sandborg^{1} and
 Agnetha Gustafsson^{6}
DOI: 10.1186/2191219X328
© Norberg et al.; licensee Springer. 2013
Received: 18 December 2012
Accepted: 15 March 2013
Published: 19 April 2013
Abstract
Background
Reduced ventilation in lung regions affected by chronic obstructive pulmonary disease (COPD), reflected as inhomogeneities in the singlephoton emission computed tomography (SPECT) lung image, is correlated to disease advancement. An analysis method for measuring these inhomogeneities is proposed in this work. The first aim was to develop a quantitative analysis method that could discriminate between Monte Carlo simulated normal and COPD lung SPECT images. A second aim was to evaluate the ability of the present method to discriminate between human subjects with advanced COPD and healthy volunteers.
Methods
In the simulated COPD study, different activity distributions in the lungs were created to mimic the healthy lung (normal) and different levels of COPD. Gamma camera projections were Monte Carlo simulated, representing clinically acquired projections of a patient who had inhaled 125 MBq ^{99m}TcTechnegas followed by a 10min SPECT examination. Reconstructions were made with iterative ordered subset expectation maximisation. The coefficient of variance (CV) was calculated for small overlapping volumes covering the 3D reconstructed activity distribution. A CV threshold value (CV_{T}) was calculated as the modal value of the CV distribution of the simulated normal. The area under the distribution curve (AUC), for CV values greater than CV_{T}, AUC(CV_{T}), was then calculated. Moreover, five patients with advanced emphysema and five healthy volunteers inhaled approximately 75 MBq ^{99m}TcTechnegas immediately before the 20min SPECT acquisition. In the human study, CV_{T} was based on the mean CV distribution of the five healthy volunteers.
Results
A significant difference (p < 0.001) was found between the MonteCarlo simulated normal and COPD lung SPECT examinations. The present method identified a total reduction of ventilation of approximately 5%, not visible to the human eye in the reconstructed image. In humans the same method clearly discriminated between the five healthy volunteers and five patients with advanced COPD (p < 0.05).
Conclusions
While our results are promising, the potential of the AUC(CV_{T}) method to detect less advanced COPD in patients needs further clinical studies.
Keywords
Quantitative lung SPECT Ventilation Iterative reconstruction Lung disorder Monte Carlo COPDBackground
Chronic obstructive pulmonary disease (COPD) is characterised by obstructed airways and parenchymal destruction. Characteristically, varying degrees of abnormalities are found in different parts of the COPD lung, and some parts of the lung may even be normal. Consequently, abnormal ventilation distribution is the first abnormality to be detected in the early stages of the disease [1].
COPD starts with inflammation and obstruction in peripheral airways [2, 3] where the resistance is very low [4]. Therefore, conventional lung function tests (spirometry) are insensitive [5]. Imaging techniques primarily aim at localising lesions, but as these techniques become digital, new interpretative possibilities are now arising. By using highresolution computed tomography (HRCT), mild emphysema may be assessed by quantification of the density distribution [6]. Although computed tomography is an excellent method for identifying anatomical changes in the lung tissue, it provides little information about lung function reduction [6]. In contrast, lung function reduction is both imaged and assessed directly by lung singlephoton emission computed tomography (lung SPECT), a method often used in diagnosis of pulmonary embolism [7, 8]. Hence, previous reports have described ventilation and perfusion SPECT as a sensitive method of detecting early changes in COPD [9]. Moreover, SPECT findings correlate significantly with emphysema scored by HRCT and lung function tests [9].
It is common to interpret lung SPECT images qualitatively. However, quantitative information obtained from a SPECT examination has the potential to provide much more information. Different quantitative methods for assessment of lung SPECT images have been reported [10–16]. Xu et al. [11] quantified inhomogeneities in ventilation SPECT images of COPD patients using 50 MBq of Technegas (Vita Medical Limited, Sydney, Australia), measuring the coefficient of variance (CV) in the lung elements. In that study, transaxial SPECT slices (1cm thickness with 3.5cm spacing) were acquired using a lowenergy, generalpurpose collimator, filtered backprojection with nonhomogeneous attenuation correction and a 2D Hann postfilter. These were then divided into 2 × 2 × 1 cm^{3} elements. By that method, it was possible to separate nonsmoking healthy subjects and ‘healthy smokers’ from COPD patients. Importantly, however, that method was not sensitive enough to discriminate between healthy nonsmokers and healthy smokers. Our overall aim is to develop a quantitative method sensitive enough to discriminate between these two groups. Therefore, the present study is currently followed by a larger clinical study evaluating the ability of the method used in this paper to differentiate between healthy nonsmokers and healthy smokers.
The present study had two aims. Firstly, we wanted to develop a quantitative method using Monte Carlo simulated lung SPECT images of phantom lungs that could discriminate between uniform (healthy, simulated normal) and nonuniform (nonhealthy, simulated COPD) activity distributions corresponding to COPD lung changes of varying severity. Secondly, we wished to evaluate the ability of the same method in a clinical environment to differentiate between human subjects with advanced COPD and healthy volunteers.
Methods
Lung phantom and Monte Carlo simulations
Phantom activity distribution notations and their descriptions
Distribution notation  Coronal slice  Number  Distribution description  Total reduction of ventilation (%)  

Simulated normal  Uniform 
 1  Homogeneous activity distribution  0 
Simulated COPD  1 cm^{50%} 10% evenly 
 2  Lesions with a diameter of 1 cm with 50% activity concentration, evenly distributed over the lung volume, occupying 10% of the total lung volume.  5 
1 cm^{50%} 12% centred 
 3  Lesions with a diameter of 1 cm with 50% activity concentration, centred to the large bronchial tubes, occupying 12% of the total lung volume.  6  
1 cm^{0%} 10% evenly 
 4  Lesions with a diameter of 1 cm with 0% activity concentration, evenly distributed over the lung volume, occupying 10% of the total lung volume.  10  
1 cm^{0%} 12% centred 
 5  Lesions with a diameter of 1 cm with 0% activity concentration, centred to the large bronchial tubes, occupying 12% of the total lung volume.  12  
2 cm^{50%} 10% evenly 
 6  Lesions with a diameter of 2 cm with 50% activity concentration, evenly distributed over the lung volume, occupying 10% of the total lung volume.  5  
2 cm^{0%} 10% evenly 
 7  Lesions with a diameter of 2 cm with 0% activity concentration, evenly distributed over the lung volume, occupying 10% of the total lung volume.  10  
2 cm^{50%} 48% evenly 
 8  Lesions with a diameter of 2 cm with 50% activity concentration, evenly distributed over the lung volume, occupying 48% of the total lung volume.  24  
2 cm^{25%} 48% evenly 
 9  Lesions with a diameter of 2 cm with 25% activity concentration, evenly distributed over the lung volume, occupying 48% of the total lung volume.  36 
SPECT projections from these activity distributions were Monte Carlo simulated using the software SIMIND, version 4.9d [21, 22]. The projection data incorporated the effects of nonuniform attenuation, scatter and motion blurring. The isotope ^{99m}Tc was used and the energy window was set between 130 and 154 keV. The gamma camera rotation orbit was noncircular, corresponding to the autocontouring system used in our clinic. The centre of rotation to collimator distance varied between 17 and 25 cm.
Simulations were made for a GE Infinia gamma camera (9.5mm thick NaI detector) equipped with a lowenergy highresolution collimator (LEHR). Projections were collected at 128 different angles, equally spaced, over 360°, in 128×128 matrices (3.30 × 3.30 mm^{2} per detector element). A total of 1.8 × 10^{10} photons were simulated with the homogeneous activity concentration (the simulated normal) resulting in projections with very low noise levels. The coefficient of variance CV (see Equation 1 in Additional file 1) of one pixel element within the highcount area of the lung in one single projection determined from three consecutive simulations was 0.5%.
Normalisation and statistical noise
The aim of generating lownoise projections was to be able to adjust the mean number of total counts to that representative of a clinical study, and to generate Poisson noise [23, 24] typical of realistic projections. The mean number of counts in the simulated projections was set to 3.635 × 10^{6}. This value was based on a virtual administered activity of 125 MBq for each activity distribution, an acquisition time of 10 s per projection and the average sensitivity (cps MBq^{−1}) of 20 planar lung perfusion studies at Linkoping University Hospital performed in 2009. The acquisition parameters used then were the following: ^{99m}Tc macroaggregated albumin, LEHR collimator and an energy window of 130154 keV. Before reconstruction, the mean number of counts in individual pixel elements in the projections was replaced by random deviates drawn from a Poisson distribution.
For each activity distribution, 20 noise realisations were created, imitating 20 SPECT acquisitions of the same activity distribution. The CV value of one pixel element within the highcount area of the lung in one projection, based on the 20 noise realisations of the simulated normal, was about 15%.
Human subjects and their data acquisition
Subjects’ gender, age, FEV _{ 1 } , FEV _{ 1 } /VC and AUC(CV _{ T } )
Subject  Label  Gender  Age (years)  FEV _{1}(% predicted)  FEV _{1}/VC  AUC(CV _{T}) (%) 

Healthy volunteers  H1  F  51  132  0.85  52 
H2  F  68  98  0.77  75  
H3  M  75  122  0.73  57  
H4  M  69  89  0.69  72  
H5  M  50  111  0.83  71  
Patients  P1  M  81  43  0.33  99 
P2  M  84  30  0.25  100  
P3  F  71  22  0.21  100  
P4  F  73  51  0.43  100  
P5  F  52  28  0.30  100 
Technetium (^{99m}Tc) was generated (Covidien, Dublin, Ireland) and thereafter Technegas was prepared and delivered from the Technegas generator according to the manufacturer’s instructions. Starting from functional residual capacity, subjects inhaled a deep breath of Technegas and hold their breath for 2 to 5 s and then expired. This manoeuvre was repeated until approximately 75 MBq ^{99m}Tc (according to the gamma camera) had been deposited in the lungs. The subjects inhaled the gas in a supine position immediately before the ventilation SPECT acquisition in the same position. The data was acquired using a doubleheaded gamma camera (GE Infinia, Milwaukee, WI, USA) with a LEHR collimator. There were 120 projections (20 s each) equally spaced over 360°, and each projection was a 128 × 128 element matrix with a pixel size of 3.45 × 3.45 mm^{2}. Autocontouring and a 130 to 154 keV energy window were employed. The CV value of the two regions of interest (3 × 3 pixels) within the highcount area of the lung in two projections, based on the five healthy volunteers, was about 17%, consistent with the result of the simulated normal.
After the SPECT acquisition, without moving the patient, a lowdose CT examination was performed using the Xray equipment mounted on the gamma camera (Hawkeye, GE Infinia, Milwaukee, WI, USA).
The effective dose for this protocol is estimated to be 3.1 mSv (1.1 mSv for SPECT [29] and 2 mSv for CT), which required a total acquisition time of 25 min.
SPECT reconstruction and filtering
Each set of 20 noisy projections in the simulated COPD study and subject projections in the human study was reconstructed using the iterative ordered subset expectation maximisation reconstruction software developed at Johns Hopkins University, Baltimore, MD, USA. The reconstruction included correction for attenuation, scatter and collimator detector response (CDR). In the simulated COPD study, the phantom attenuation was corrected for using an attenuation map of the phantom, and in the human study the CT scans were used for attenuation correction. Scatter correction was performed using the effective source scatter estimation (ESSE) [23, 30]. The ESSE model requires scatter kernel files, which were generated using Monte Carlo simulations with SIMIND. An analytic geometrical model for CDR compensation was used. Reconstructions were performed using ten iterations and 16 subsets. The side length of a voxel in a reconstructed image was for the simulated COPD study and human study with 3.30 mm and 3.45 mm respectively. The reconstructed images were postfiltered with a Butterworth filter [31] with a cutoff frequency of 0.5 cm^{−1} and a power of 6 (i.e. order 3).
Method for analysis of inhomogeneities
Defining the extension of the lung
For the simulated COPD study, the lung voxels in distribution 1 (Table 1), containing values greater than half the maximum lung value, were set to be the lung.
For the human study, semiautomated lung segmentation was used on each subject based on the individual CT acquisition and an empirical linear attenuation coefficient threshold value of 0.12 cm^{−1}. The linear attenuation coefficients for lung tissue and soft tissue are approximately 0.04 and 0.16 cm^{−1}, respectively (ICRU 44). Therefore, the defined lung for each subject in the human study will include the whole lung cavity.
Edge layers
Due to the limited spatial resolution of the SPECT system, with a full width at half maximum (FWHM) of about 1 to 1.5 cm, the reconstructed activity distribution around the edge of the lung will be blurred. Voxels involved in the lung edge will result in high CV values, which are not necessarily correlated to inhomogeneity of the lung activity distribution. Therefore, a onevoxelwide layer of the lung was ‘peeled off’ from the periphery of the segmented lung, thereby creating a reduced lung volume with most of this edge effect excluded.
Threshold values and area under curve
The area under the frequency function (AUC) for CV values greater than a threshold value for CV, called CV_{T}, was defined as AUC(CV_{T}) and expressed as the percentage of the total AUC (Figure 2). Since the future aim is to discriminate between healthy distributions and less severe COPD distributions, the optimal CV_{T} value was evaluated by finding the largest separation between corresponding AUC(CV_{T}) values for the healthy ‘uniform’ and the ‘1cm^{50%} 10% evenly’ distributions (distributions 1 and 2 in Table 1) in the simulated COPD study. CV_{T} values ranging from 1% to 25% were evaluated. The largest separation was found for the CV value corresponding to the peak value of the mean frequency function of the healthy distributions, i.e. the modal value, plus about 1%. The modal value was used for simplicity.
In the simulated COPD study, the healthy mean frequency function is based on the 20 noise realisations of the uniform activity distribution; and in the human study, it is based on the activity distributions of the five healthy volunteers. AUC(CV_{T}) was then calculated for all activity distributions in both the simulated COPD study and the human study.
In the simulated COPD study, the group of 20 AUC(CV_{T}) values, based on the 20 noise realisations of the simulated normal distribution, was compared with the corresponding groups of simulated COPD distributions, using the nonparametric Mann–Whitney U test and Statistica (version 9, StatSoft, Tulsa, Oklahoma, USA). This test was used because no assumption of normal distributions was made. The same test was performed comparing the five COPD patients with the five healthy volunteers.
Image and computer processing
The addition of Poisson noise, postfiltration and evaluation were performed using inhouse software developed in Interactive Data Language (IDL; ITT Visual Information Solutions, Boulder, CO, USA).
Results and discussion
Results of the Monte Carlosimulated COPD study
Results of the human study
Discussion
In the present study, we have modified a method by Xu et al. in order to improve the quantification of ventilation inhomogeneities in a phantom model of a COPD lung. In contrast to what has been previously reported, our improved method was able to assess even minor COPD changes by using the AUC(CV_{T}) value, as a global value of ventilation inhomogeneities, and to discriminate these changes from a model of a healthy homogeneous lung. The present pilot study also shows that our way of performing lung SPECT and calculating the AUC(CV_{T}) significantly discriminates nonsmoking healthy volunteers from patients with advanced COPD.
The NCAT software is able to create thorax voxel phantoms of a human, based on a finesegmented male [32]. The NCAT software is flexible since different sizes and shapes of different tissues can be selected and natural movements caused by heartbeat and respiration can be modelled. Because most of the COPD patients are elderly [33], we decided to use a lung volume corresponding to a 65yearold male. Lowventilated regions, associated with anatomical changes of COPD, are distributed in patients in various ways, and these volumes can vary between 0.5 mm (the size of a few alveoli) and several centimetres in diameter. One of the aims of the present study was to mimic mild to moderate changes of COPD, and therefore small lesions were of interest. Since the spatial resolution of the SPECT system is about 1 to 1.5 cm (expressed in FWHM), the lesions modelled had a diameter of 1 and 2 cm. Lacking previous studies on the distribution of ventilation inhomogeneities in mild COPD, we assumed that COPD lesions are either evenly distributed in the whole lung volume or centred in clusters (see Table 1). In this way, our method was evaluated on two groups with completely different lesion distributions. The density of the lesions was approximated to be the same as for the healthy lung tissue since we aimed to model less severe changes of COPD. Indeed, activity distribution 1 cm^{50%} 10% (distribution 2 in Table 1) illustrates activity inhomogeneities that are almost too small for the SPECT system to resolve, and, clearly, not visible to the human eye in the reconstructed image (cf. Figure 5). Thus, we believe this distribution would be a good representative of a case of mild COPD. However, it should be pointed out that the activity distributions selected in the different cases were not primarily chosen because of their consistency with biology, but more because of our ability to unambiguously describe them.
How does the magnitude of the present volume of ventilation defects compare to reductions of spirometric variables? Fifty percent reduction of the ventilation in 10% of the lung volume (distributions 2 and 6 in Table 1) corresponds to the 5% total reduction of the functioning lung tissue. Although not exactly comparable, reductions of spirometric variables of similar magnitudes are likely to remain undetected, as the normal range of spirometric variables is roughly ±15% to 20%. Thus, in our anthropomorphic phantom, we consider most of the present volume of ventilation defects as comparatively small.
When considering the result of the presented quantitative method, a number of important parameters have to be accounted for, e.g. count density (statistical noise), collimator, number of iterations and subsets, reconstruction compensations and postfiltering. In this work we used clinically relevant values for these parameters. Other important factors are the methodspecific ones, i.e. the lung edge effects and kernel size that we evaluated. The resolution of the SPECT system is limited, and therefore, high CV values will always be found in the periphery of the healthy lung. Lung edges with low activity lesions will instead give lower CV values. High CV values due to healthy edges reduce the differences between frequency functions from healthy and unhealthy activity distributions; i.e. they also reduce the separation between corresponding AUC(CV_{T}) values. Therefore, the CV analysis was performed in a volume that had part of the edge effect excluded. Exclusion of a onevoxel layer, however, removes 21% of the phantom lung and 20% to 29% of the human lung parenchyma from the analysis, which is why small lesions in the periphery might not be detected. The kernel approach used does not exclude any additional volume of the lung in the analysis. Kernels with five different side lengths were evaluated in the simulated COPD study, i.e. 1.0, 1.7, 2.3, 3.0, 3.6 and 4.3 cm. Increasing the side length resulted in an increasing differentiation between the AUC(CV_{T}) values of the simulated normal and 1 cm^{50%} 10% evenly distribution (distributions 1 and 2 in Table 1). However, for the two largest cube sizes tested, in combination with some values of the abovementioned parameters (e.g. LEHR collimator, a Butterworth postfilter with a cutoff frequency of 0.3 cm^{−1} and a power of 6, 125 MBq, ten iterations and 16 subsets and no exclusion of edge voxels), the CV frequency functions of the simulated normal distribution were double peaked. Therefore, to minimise the risk of double peaks in a clinical setting, we chose a kernel with the side length of 3 cm, including 729 voxels.
The present method of quantitative analysis has two major advantages. Firstly, it discriminates cases with the same loss of ventilation, but with inhomogeneities differently distributed and with different lesion sizes, from the simulated normal lung, even when COPD changes are minor. For example, distributions 2 (1 cm^{50%} 10% evenly), 3 (1 cm^{50%} 12% centred) and 6 (2 cm^{50%} 10% evenly) in Figure 3 all represent only a 5% to 6% total reduction of ventilation, but their resulting AUC(CV_{T}) values are well above the value of the normal lung. The same holds true for distributions 4 (1 cm^{0%} 10% evenly), 5 (1 cm^{0%} 12% centred) and 7 (2 cm^{0%} 10% evenly), which all correspond to a 10% to 12% total reduction of ventilation. Secondly, for the same lesion size, increasing AUC(CV_{T}) values tend to correlate with decreasing total ventilation. For example, distribution 6 (2 cm^{50%} 10% evenly) in Figure 3, corresponding to a 5% total reduction of ventilation, gives a lower AUC(CV_{T}) value than distribution 7 (2 cm^{0%} 10% evenly) corresponding to a 10% total reduction of ventilation. But clearly, even with the same loss of ventilation, activity distributions with a few large lesions (≥1.5 cm) with low activity will give higher AUC(CV_{T}) values than activity distributions with many small lesions (≤1.5 cm) with relatively high activity in each lesion (e.g. see Figure 3 and Figure 5, distributions 4 and 7 at rows 3 and 5, column C).
It is desirable that distributions with the same total reduction of ventilation result in the same level of AUC(CV_{T}) values independent of lesion shape and distribution. This is the case of the evenly distribution 4 and the clustered distribution 5 (with a total reduction of ventilation of 10% to 12%). However, the difference in distribution can be seen in the frequency functions (Figure 4) and in corresponding CV matrices (Figure 5). Clusters positioned in other parts of the lung have not been investigated. Factors influencing the result are the volume and activity concentration of the spheres in the cluster and the size of the cluster’s surface area towards the uniform part of the lung, and not where in the lung the cluster is positioned.
One limitation of the present method is its loss of sensitivity when COPD changes become more advanced. An activity distribution with an AUC(CV_{T}) value close to 100% can easily be separated from the simulated normal distribution; however, further reduction of ventilation will only result in an almost unchanged AUC(CV_{T}) value (because 100% is the highest possible value). On the other hand, the present method is customised to detect early and minor COPD changes and not to be a diagnostic for advanced emphysema, for which methods such as HRCT are more useful. Furthermore, in cases of advanced COPD, the appearance of the frequency function of CV values can be used directly, without calculating the CV_{T} and the AUC(CV_{T}), to estimate the COPD severity.
The activity distribution in healthy humans is not as homogeneous as the simulated normal distribution used in the simulated COPD study, which might be due to the shape of the bronchial tree and the gravity influencing the lung. This difference is seen in the defined CV_{T} values, 20.5% for the simulated COPD study and 22.0% for the human study. Furthermore, the human thorax exhibits a large variety of sizes and shapes. In order to decrease the influence of the lung size, AUC(CV_{T}) values are presented in a percentage of total lung volume instead of absolute values. This prevents large healthy lungs giving high AUC(CV_{T}) values and small inhomogeneous lungs giving low AUC(CV_{T}) values. However, a larger 95% CI of the AUC(CV_{T}) value of the healthy volunteers of ±13% around the mean compared to the simulated COPD study of ±1% was found. This large variation of the healthy volunteers might be due to genetic variations, age effects, different histories of occupational and environmental exposures of noxious particles and gases e.g. passive exposure to tobacco smoke and varying techniques of inhaling the Technegas. The total amount of inhaled Technegas, expressed in megabecquerel, also affects the resulting CV values. A reconstructed ventilation distribution based on a low activity level will contain higher statistical noise compared to a distribution based on a higher activity level. Higher noise levels will result in higher CV values. A variation in the amount of inhaled Technegas was observed between the human subjects. Therefore, due to different activity levels, small shifts along the CV axis of the frequency functions in Figure 7 are present. Procedures resulting in more reproducible amounts of inhaled Technegas and normalisation methods that after image acquisition can compensate for such shifts will be further investigated. Reproducibility may be improved also by control of the depth and inhalation flow rate of the Technegas administration. In particular, the inhalation flow rate influences the ventilation distribution in normal subjects [34] and almost certainly also the deposition in patients with COPD.
The activity distribution in the COPD patients was characterised by areas of low activity and areas of high activity i.e. hot spots. Low activity areas are caused by reduced regional ventilation resulting in low Technegas particle deposition. Reduced regional ventilation may be caused by local obstruction of peripheral airways and/or by emphysematous areas with low elasticity. Hot spots on the other hand, as pointed out by Pellegrino et al. [35], may be caused by obstruction of central airways probably resulting in some Technegas impaction during inspiration but in particular by facilitating regional airflow limitation during expiration. Airflow limitation implies pronounced and oscillating narrowing of airway walls causing strong turbulence resulting in high impaction of Technegas particles during expiration [35]. These hot spots were included in the assessment of the CV values. This might be considered as a weakness of the present method, since the intention behind the use of CV values was to identify low activity volumes as an indication of disease and not to generate high CV values due to abnormal uptake. On the other hand, such hot spots are typical of the COPD lung and might therefore be accepted as contributing to the analysis. How these hot spots influence the analysis will be evaluated in our future work. Presently, we investigate if the present method is reliable enough for detection of COPD changes in smokers without manifested COPD.
Conclusion
The proposed method generates as a global measure of ventilation inhomogeneities, an AUC (CV_{T}) value that can differentiate with statistical significance between the activity distributions corresponding to varying severity of COPD changes and the healthy normal activity distribution in a breathing lung phantom. The method can also clearly differentiate between five patients with COPD of grades 3 and 4, and five healthy nonsmokers. Therefore, the AUC (CV_{T}) method is promising for assessment of COPD inhomogeneities in the lung SPECT ventilation image. The potential of the AUC(CVT) method to detect less advanced COPD in patients, e.g. smokers without manifested COPD, needs further investigations.
Abbreviations
 AUC:

the area under the curve
 AUC(CVT):

the area under the curve for CV values greater than CV_{T}
 CDR:

collimator detector response
 COPD:

chronic obstructive pulmonary disease
 CV:

the coefficient of variance
 CVT:

CV threshold value
 DLCO:

diffusion capacity of the lung for carbon monoxide
 ESSE:

effective source scatter estimation
 FEV1:

forced expiratory volume in one second
 FWHM:

full width at half maximum
 HRCT:

highresolution computed tomography
 LEHR:

lowenergy highresolution
 RV:

residual volume
 SPECT:

singlephoton emission computed tomography
 TLC:

total lung capacity
 VC:

vital capacity
Declarations
Acknowledgements
This work has been conducted within the Center for Medical Image Science and Visualization (CMIV) at Linköping University, Sweden. CMIV is acknowledged for provision of financial support and access to a leading edge research infrastructure. Reconstruction software developed at John Hopkins University, Baltimore, USA, has been used in this work. Financial support was provided by the County Council of Östergötland, Sweden (ALF; grants to AG, MS and HLP) and the Medical Research Council of Southeast Sweden (FORSS; grant to AG). The required scatter kernel files for the ESSE model were generated by Michael Ljungberg from Lund University, Sweden. Furthermore, the authors thank the staff of the Department of Clinical Physiology, County Council of Östergötland, Linköping, Sweden for the acquisition of spirometry and SPECT data.
Authors’ Affiliations
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