Chronic Obstructive Pulmonary Disease: CT Quantification of Airways Disease 1

Note: This copy is for your personal non-commercial use only. To order presentation-ready copies for distribution to your colleagues or clients, contact us at www.rsna.org/rsnarights. Reviews and Commentary n State of the Art Maxime Hackx, MD Alexander A. Bankier, MD, PhD Pierre Alain Gevenois, MD, PhD Online CME See www.rsna.org/education/ry_cme.html Learning Objectives: After reading the article and taking the test, the reader will be able to: n Explain the challenges of phenotyping chronic obstructive pulmonary disease through CT quantification of airways disease n Describe the methods to quantify airways disease and their limitations. Chronic Obstructive Pulmonary Disease: CT Quantification of Airways Disease 1 Chronic obstructive pulmonary disease (COPD) is an increasing cause of morbidity and mortality worldwide and results in substantial social and economic burdens. COPD is a heterogeneous disease with both extrapulmonary and pulmonary components. The pulmonary component is characterized by an airflow limitation that is not fully reversible. In the authors opinion, none of the currently available classifications combining airflow limitation measurements with clinical parameters is sufficient to determine the prognosis and treatment of a particular patient with COPD. With regard to the causes of airflow limitation, CT can be used to quantify the two main contributions to COPD: emphysema, and small airways disease (a narrowing of the airways). CT quantification with subsequent COPD phenotyping can contribute to improved patient care, assessment of COPD progression, and identification of severe COPD with increasing risk of mortality. Small airways disease can be quantified through measurements reflecting morphology, quantification of obstruction, and changes in airways walls. This article details these three approaches and concludes with perspectives and directions for further research. RSNA, 2012 Accreditation and Designation Statement The RSNA is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. The RSNA designates this journal-based CME activity for a maximum of 1.0 AMA PRA Category 1 Credit TM. Physicans should claim only the credit commensurate with the extent of their participation in the activity. Disclosure Statement The ACCME requires that the RSNA, as an accredited provider of CME, obtain signed disclosure statements from the authors, editors, and reviewers for this activity. For this journal-based CME activity, author disclosures are listed at the end of this article. 1 From the Department of Radiology, Hôpital Erasme, Université Libre de Bruxelles, 808 Route de Lennik, 1070 Brussels, Belgium (M.H., P.A.G.); and Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (A.A.B.). Received June 24, 2011; revision requested July 23; revision received August 30; accepted October 21; final version accepted December 7. Address correspondence to P.A.G. (e-mail: pierre.alain.gevenois @erasme.ulb.ac.be). q RSNA, 2012 34 radiology.rsna.org n Radiology: Volume 265: Number 1 October 2012

Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity and mortality worldwide (1 5). By 2020, COPD will become the third leading cause of death (3 5). This increasing mortality is attributed to the continued exposure to risk factors such as cigarette smoking, occupational exposure, and air pollution and even more to the aging of the world population (3 5). COPD also results in social and economic burdens with major direct and indirect costs (1,3 5). Because there is a direct relationship between the severity of the disease and patient outcome (6 9), as well as the cost of care (10), it is important to provide appropriate assessment Essentials nn Together with quantification of emphysema, quantification of airways disease at CT can allow clinically meaningful phenotyping of chronic obstructive pulmonary disease (COPD). nn In patients with COPD, CT could contribute to improved patient care, assessment of disease progression, and identification of patients with severe disease and increased risk of mortality. nn CT measurements, based on automated algorithms, of intermediate-sized airways (approximately 2 mm in internal diameter) correlate with the dimensions of small airways as measured at pathologic examination. nn The magnitude of change in attenuation values, selected within a range to limit the influence of emphysema, between paired inspiratory and expiratory CT scans in lung reflects the severity of small airways obstruction. nn The peak attenuation value of an airway wall reflects changes in both size (thickening of each of the wall compartments) and physical density (modified composition of these compartments) of airways walls. tools for COPD to optimize patient care. Considering recent improvements in computed tomography (CT) technology, in this review we will discuss how CT could be used to provide airways disease quantification complementary to that of emphysema. Definition and Classification The Global Initiative for Chronic Obstructive Lung Disease (GOLD) has defined COPD as a preventable and treat able disease with some significant extrapulmonary effects that may contribute to the severity in individual patients. Its pulmonary component is characterized by airflow limitation that is not fully reversible. The airflow limitation is usually progressive and associated with an abnormal inflammatory response of the lung to noxious particles or gases (6). GOLD also proposed a classification of the severity of COPD the current standard of reference based on two spirometric parameters that reflect the degree of airflow limitation: forced expiratory volume in 1 second (FEV 1 ) and the ratio of FEV 1 to forced vital capacity (FEV 1 / FVC) (6). The values obtained in an individual patient are compared with specific spirometric cutoff points arbitrarily chosen for simplicity that define four stages of severity (6). This GOLD classification has several advantages: It is simple to use, repeatable, noninvasive, inexpensive, the cutoff points are easy to memorize, and it is in widespread use in the pulmonary medicine community (6). Nevertheless, this classification provides only a relatively gross assessment of the disease and may be insensitive to the early stages, notably in symptomatic patients who present with cough, sputum production, wheezing, or shortness of breath (6,11,12). In addition, GOLD is designed to assess the respiratory aspects of COPD only and does not take into account the systemic manifestations of the disease (6). Therefore, other proposed classifications combine spirometric measurements with body mass index, degree of dyspnea, exercise capacity index, age, smoking status, frequency of exacerbations, health status, and depressive symptoms (7 9,13). While these classifications are more appropriate than the GOLD classification to assess COPD severity or to predict mortality (7 9), they are more complex and are therefore rarely used in clinical practice. COPD Phenotypes and CT Definition and Evidence for CT-based Phenotyping None of the aforementioned classifications is sufficient on its own to allow determination of the prognosis and appropriate care of an individual patient. This is due to the number of pathologic processes of COPD, the effects of which are modified by varied host susceptibility (6,14) and raises the need for phenotyping. Recently, Han et al (15) proposed an operational definition of COPD phenotypes as a single or combination of disease attributes that describes differences between individuals with COPD as they relate to clinically meaningful outcomes (symptoms, exacerbations, response to therapy, rate of disease progression, or death). The quantification of the findings at chest CT could be used to discriminate between patients with the same spirometric values (15). While the aforementioned classifications (6 9, 13) assess the lung globally, CT provides a regional approach. Among the causes of airflow limitation, CT can be used to quantify the two main contributions to COPD that are directly related to this disease: (a) emphysema, the destruction of the pulmonary parenchyma, and (b) small airways disease, a narrowing of the airway lumina resulting from luminal accumulation of inflammatory mucous exudates Published online 10.1148/radiol.12111270 Radiology 2012; 265:34 48 Content codes: Abbreviations: COPD = chronic obstructive pulmonary disease FWHM = full width at half maximum Potential conflicts of interest are listed at the end of this article. Radiology: Volume 265: Number 1 October 2012 n radiology.rsna.org 35

and airway wall thickening secondary to infiltration by inflammatory immune cells and the repairing processes (16). Small airways, defined as airways with an internal diameter smaller than 2 mm (17 19), reflect the fourth to the 14th generations of branching (20). There is evidence that CT quantification of emphysema can contribute to improved patient care (21 25). There is also evidence that CT can be used to help assess the progression of COPD (22,26) and identify patients with severe COPD who have an increasing risk of mortality (27). Consequently, ongoing research studies such as the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints, or ECLIPSE, study (28) and the COPDGene study (29) (http://www.copdgene.org) have all included CT parameters in their evaluations. CT-based Phenotypes Numerous studies have investigated the quantification of the extent of emphysema by using CT as compared with pathologic evaluation (25,30 43). This method is based on emphysematous lung zones having reduced CT attenuation values. The frequency-attenuation distribution curve and the extent of emphysema are calculated and expressed as relative low-attenuation areas or volumes (ie, the proportion of lung parenchyma with attenuation values lower than a predetermined threshold) or as percentiles of the frequency-attenuation distribution curve (25,30 43). In comparisons with microscopic and macroscopic morphometric findings, thresholds such as 2960 HU and 2950 HU, as well as the first percentile of the frequency-attenuation distribution curve, are reported as appropriate indices to quantify the extent of the emphysema (32 35). In 2005, de Jong et al (44) reported that the quantification of the airway lesions by CT has received less attention, but improvements in CT technology now make it possible to detect and quantify the airway abnormalities. Since the time of this statement, important developments have occurred in objective CT quantification of airways disease in COPD. The following is an update of our knowledge of the currently available methods of quantification. What Is Small Airways Disease? Pathologic Changes Once exposed to tobacco smoke and toxic particles, the small airways develop innate and adaptive immune responses. The innate response includes mucociliary clearance that removes the particles deposited on the airway walls (45). An acute inflammatory response may be triggered by injury to the epithelial barrier by these particles (46 48). This innate response is rapid and responsible for the cough and sputum production on which the definition of chronic bronchitis is based (49,50). This rapid innate response is also associated with a slow adaptive response mediated by both cellular and humoral immunities involving lymphocytes (51). Unlike acute exposure, chronic exposure is associated with abnormal innate and adaptive immune responses that may be expressed by increased production of mucus, defective mucociliary clearance, disruption of the epithelial barrier, and infiltration of the airway walls by inflammatory immune cells forming lymphoid follicles (16,46 48). These responses result in an accumulation of inflammatory mucous exudates within the airway lumen, coupled with remodeling processes that thicken each of the airway wall compartments (epithelium, lamina propria, adventitia) (16). Moreover, Hogg et al (16) showed that progressing occlusion of the airway lumen by mucus, extent of the inflammation within airway walls, and thickening of airway wall compartments are all related to airflow limitation. In addition, it has been suggested that immune responses and changes in small airways may play a role in the onset of emphysema (52). Although some studies with micro-ct (53,54) have revealed a 10-fold reduction in terminal bronchiolar number and a 100-fold reduction in minimal terminal bronchiolar diameter in patients with severe COPD, compared with those in healthy control subjects, a more recent study, also with micro-ct (55), has indeed demonstrated that narrowing and destruction of terminal bronchioles precede the onset of emphysema. Contribution of Small Airways to Airflow Limitation The airflow limitation in COPD results from small airways disease (17 19) and decreased elastic recoil due to emphysematous destruction (56). Of these two processes, small airways disease is the strongest determinant of airflow limitation (17 19). The pathologic changes in the small airways that lead to their obstruction are complex and include luminal occlusion by inflammatory mucous exudates (16), luminal encroaching by epithelial layer thickening (57), airways smooth muscle hypertrophy and contraction (58,59), deposition of connective tissue in the adventitial compartment of the airway wall (16), destruction of the support from the alveolar attachments (60 62), and mucus secretion that alters airway surface tension and predisposes to expiratory collapse (63). CT Quantification Technical CT Parameters The transition from manual tracing of the airway contours to automated algorithms eliminated the need to consider display settings (64). Nevertheless, because several CT acquisition and reconstruction parameters influenced the measurements obtained with early algorithms (65 67), recent algorithms were developed to minimize this influence (68 74). The radiation dose influences the signal-to-noise ratio: The lower the dose, the higher the noise level. An increase in noise would probably reduce the contrast between airway wall and surrounding tissue and, therefore, possibly reduce measurement accuracy. While Tschirren et al (75) and Mair et al (76) reported that algorithms to measure airway dimensions can be applied to low-dose CT images with acceptable measurement errors, as compared with phantom reference measurements; no study, to our knowledge, has inves 36 radiology.rsna.org n Radiology: Volume 265: Number 1 October 2012

tigated the consistency of measurements obtained with commonly available algorithms used with low-dose conditions. This investigation is mandatory for longitudinal studies in patients with COPD who can be young, particularly those with Global Initiative for Chronic Obstructive Lung Disease stages 1 and 2 disease, and in cigarette smokers undergoing low-dose CT screening for lung cancer, in whom repeated CT acquisitions would be performed. As an example, CT parameters recommended in the COPDGene study are shown in the Table (29). Because intravenous iodinated contrast material influences the attenuation values of the organs, its influence on mea surements of airway dimensions should also be investigated. Indeed, chest CT examinations are often performed in such conditions, and it would not be reasonable to perform an additional acquisition without contrast material just to measure the airway dimensions. Finally, because lung volume during CT influences airway caliber (77), as well as the attenuation of tissue surrounding the airway (78), this volume could also influence the measurements. Advances in software now allow three-dimensional segmentation of the lung, so that lung volume can be measured from the voxel count and used to adjust airway dimensions to lung volume (79,80). Subse Recommended CT Parameters from the COPDGene Study Parameter Inspiratory CT Expiratory CT Detector configuration* 16 128 3 0.6 0.75 mm 16 128 3 0.6 0.75 mm Tube current (effective mas) 200 50 Tube potential (kv) 120 120 Rotation time (sec) 0.5 0.5 Automatic exposure control Off Off Pitch 0.923 1.375 0.923 1.375 Reconstruction algorithms High-resolution, standard High-resolution, standard Section thickness (mm) 0.625 0.900 0.625 0.900 Section interval (mm) 0.450 0.625 0.450 0.625 Note. Adapted and reprinted, with permission, from reference 29. * Depends on CT scanner manufacturer. quently, the possible influence of breathing instructions has not been investigated, to our knowledge. Measurements of Airways Morphologic Changes CT measurements are consistently accurate and reproducible in airways down to approximately 2 mm in internal diameter (65,81 87). In addition, dimensions measured at CT in these airways correlated with dimensions of airways measured at pathologic examination (81,85,86). Nakano et al (85) showed that the dimensions of airways with an internal perimeter larger than 7.5 mm (corresponding to airways with an internal diameter larger than 2.4 mm) measured on CT images reflect the dimensions of small airways measured at pathologic examination. In airways, CT can be used to directly measure changes in dimensions: luminal narrowing and wall thickening (65,66,81 92). These direct measurements have several advantages: They offer a noninvasive quantification of changes that are potentially reversible, allow regional assessment throughout the lungs, and require only one inspiratory CT acquisition, and the obtained measurements are strongly correlated with airflow obstruction (65,81,88,89,92). Historical overview of the methods. Early studies of airway dimensions involved manual tracing of the inner and outer wall perimeters on enlarged views of the airways (64,84,93 95), which was extremely time-consuming, prone to large inter- and intraobserver variability (84,93,95), and dependent on the display parameters of the CT image (ie, window settings) (64). In addition, lumen dimensions are systematically underestimated, and wall dimensions are overestimated (83). Measuring airway lumen dimensions. The first algorithm that allowed measurement of the lumen was based on attenuation values (96). From a luminal seed point, the image data are automatically searched until a pixel exceeding a predefined threshold is found. Then, starting from the last pixel below this threshold, pixels are searched in a counterclockwise direction until the starting pixel is reached. This so-called threshold-based algorithm is repeatable and independent of the initial location of the seed point. McNitt-Gray et al showed (96) that the luminal area could be accurately measured by using a threshold of 2500 HU, which King et al (83) later refined to 2577 HU. A second algorithm, developed by Amirav et al (97), estimates lumen margins that are first traced manually and later smoothed by the computer software. On each of the points of this smoothed drawing, a perpendicular ray is automatically generated to determine attenuation values of the air within the lumen and the surrounding soft tissue. The location of the attenuation value halfway between the attenuation values of air and soft tissue is then automatically determined, and the point of the initial smoothed drawing is moved to this new location. This so-called edgedetection algorithm is repeatable and independent of the initial manual tracing (97). A third algorithm, developed by Wood et al (98), is based on a threedimensional approach and considers voxels rather than pixels, making this algorithm independent of the airway orientation (98). From a luminal seed point, all contiguous voxels within an attenuation value range specific for airway lumen are iteratively added. This so-called region-growing algorithm ter Radiology: Volume 265: Number 1 October 2012 n radiology.rsna.org 37

minates in distal airways, when the attenuation values of the lumen and of the surrounding soft tissue overlap, resulting in an attenuation value outside of the considered range (98). Thereafter, central voxels are automatically determined and connected in a line corresponding to the central axis of the airway. The cross-sectional area of a particular airway lumen is then measured by creating a new section perpendicular to this axis (98) (Figs 1, 2). However, all three algorithms (96 98) have the major limitation that they cannot be used to measure the dimensions of the airway wall. Measuring airway lumen and wall dimensions. Full width at half maximum (FWHM) is the earliest and simplest algorithm (65,66). From a luminal seed point, attenuation value profiles are measured along rays in all directions. As a ray enters the wall, the attenuation will increase and thereafter decrease as it passes into the lung parenchyma. The distance between the points where attenuation is halfway between the maximum and minimum values of the wall and lumen and the value of the lung parenchyma is considered as the airway wall thickness (65,66) (Fig 3). To avoid Figure 1 Figure 1: Display of airway tree after segmentation. Connected lines correspond to airway axis. vessels adjacent to airways, rays that spread into these vessels are manually removed. The FWHM algorithm has a major limitation. As compared with measurements from phantoms and anatomic specimens, FWHM consistently underestimates luminal dimensions and overestimates wall dimensions; these errors increase as the airway generation increases (66), owing to the limited spatial resolution of CT, the greater potential error with increasing angle of obliquity relative to the imaging plane, the pointspread function, the reconstruction kernel, and the inability to visualize the folding of the epithelium on CT images (66,99). For all these reasons, other algorithms to measure both luminal and wall dimensions have been developed (68,81,83,86,87,92,100). One of these corrects for the angle of airway orientation and the point-spread function obtained with the FWHM algorithm (100). The results from FWHM are used as estimates of the inner and outer airway wall locations, and the two ellipses that best fit these two locations are determined. Thereafter, the angle of orientation of the airway can be calculated from the characteristics of both ellipses. By combining this information with an airway model that takes the point-spread function into account, this so-called best-fitting ellipse algorithm ultimately pro vides more accurate measurements than the FWHM algorithm alone (100). A second algorithm, which is based on a three-dimensional approach that does not need correction for the angle of orientation, also represents an attempt to correct the results obtained with the FWHM algorithm for the point-spread function (86,92). The airways tree is segmented with a region-growing algorithm. A section orthogonal to the airway axis is chosen, and measurements are performed with the FWHM algorithm (66). The results are then corrected by using a formula that reverses the effect of the point-spread function. The attenuation profiles obtained with the FWHM algorithm along each ray are narrowed and elevated, with the integrals under these profiles kept constant (86,92). This algorithm showed increased accuracy in a study (101) in which the FWHM algorithm was compared with this socalled integral-based algorithm in different conditions of pixel sizes. A third algorithm, which is based on a phase congruency approach, limits the influence of the reconstruction kernel (68). Phase congruency is an intrinsic property of a signal that can be used to localize edges. In practice, several reconstruction kernels are applied to the raw data to modulate the signal and measure phase congruency. Attenuation profiles of the selected airway, similar to those obtained with the FWHM algorithm (66), are obtained for each reconstruction kernel and are compared. The common crossing points between the attenuation profiles are determined. These points are the locations of maximal phase congruency and correspond to the locations of the edges, allowing measurements of airway dimensions (68) (Fig 4). In a fourth algorithm (83), a seed point is manually placed in the approximate center of the lumen, and surrounding pixels with attenuation values below a specific threshold are iteratively added. Thereafter, the luminal area is cal 38 radiology.rsna.org n Radiology: Volume 265: Number 1 October 2012

Figure 2 Figure 2: Dimensions of a particular airway (red box on Fig 1) from a CT section obtained orthogonal to the airway axis. Avg 5 average, Diam 5 diameter. Figure 3 Figure 3: Graph shows attenuation profile along a ray obtained with FWHM algorithm. As the ray enters the wall, attenuation will increase and then decrease as it passes into lung parenchyma. The distance between points where attenuation is halfway between the maximum value of the wall and the value of the lumen on one hand, and the maximum value of the wall and the value of lung parenchyma on the other, is considered the airway wall thickness. (Reprinted, with permission, from reference 44.) culated by multiplying the number of pixels by the pixel size. For wall measurement, a circle is manually drawn to approximate the outer perimeter. This so-called erosion algorithm then iteratively erodes that circle until the outer border of the airway is clearly identified and the total airway area is calculated. The wall area ultimately results from the difference between the total area and the luminal area (83) (Fig 5). A fifth algorithm, which is based on a Laplacian of Gaussian algorithm, was developed by Berger et al (81,87) using second-derivative filters to find abrupt attenuation changes. In a square region of interest containing the selected airways, this algorithm applies filters to provide a binary image: Black pixels correspond to the lumen, and white pixels correspond to the wall. Ultimately, the number of pixels is converted to area to obtain measurements of the luminal and wall dimensions (81) (Fig 6). Recently, three new algorithms were proposed. From volumetric acquisitions of contiguous thin sections, these algorithms segment the airways tree, determine the airways axis, and reformat a section orthogonal to the axis of the selected airway (69 74). Each pixel on this section can be characterized by several parameters such as its attenuation value, its distance from other pixels, or the parameters of the neighboring pixels. Depending on the algorithm, various mathematic functions are then applied to reach equilibrium between the parameters of all pixels that lead to the ideal paths among pixels corresponding to the airway inner and outer contours. These algorithms have the advantage of yielding reliable measurements of a particular airway, regardless of the presence of an adjacent vessel. To date, to our knowledge there has been no study comparing these algorithms. Neither is there consensus on the most appropriate, whether in daily practice or in clinical trials. Validation of the measurements methods. Because the methods discussed directly assess the morphologic changes in airways through the measurement of airways dimensions, these methods should be validated by comparing their results to a pathologic reference. However, this has several technical limitations (85): Lung fixation induces smooth muscle relaxation, shrinkage, and elimination of the liquid filling the mucosal folds in scanned air-inflated lung specimens and, therefore, causes differences between the airway dimensions measured at CT and those measured at pathologic examination. A validation of CT measurements based on micro-ct has been proposed by Dame Carroll et al (102), but even in Radiology: Volume 265: Number 1 October 2012 n radiology.rsna.org 39

Figure 4 Figure 4: Attenuation profiles (right) obtained with nine reconstruction kernels by using the phase-congruency approach. The common crossing points between the attenuation profiles are the locations of maximum phase congruency and correspond to locations of edges, allowing measurements of airway dimensions. (Reprinted, with permission, from reference 132.) that study, micro-ct measurements were validated against a pathologic reference. FWHM, integral-based, erosion, and Laplacian of Gaussian algorithms were compared with pathologic references and have been shown to be accurate for airways as small as approximately 2 mm in internal diameter (81,83,85 87). Almost all methods were validated with phantoms (65,70,71,73,81,83,87 89,100) (Fig 7). This validation is not limited by lung fixation induced problems, but phantoms do not exactly reflect in vivo airways within the lung parenchyma. These phantoms are made with tubes of various materials embedded either in foam blocks (65,73,81, 83,87 89) or in potato flakes (71,100), with a possible extra cylinder representing the adjacent vessel (70). FWHM, best-fitting ellipse, erosion, and Laplacian of Gaussian algorithms, as well as the more recent algorithms, were compared with such phantoms and have been shown to be accurate for airways as small as approximately 2 mm in internal diameter (65,70,71,73,81,83, 87 89,100). An alternative method for validating CT measurements may be in vivo optical coherence tomography (103 107). What dimensions to use? Airways measurements characterize the lumen, the wall, or the entire airway without dis tinction between lumen and wall, either through a distance or a surface area. An airway can be characterized by its luminal diameter, wall thickness, total diameter, inner perimeter, outer perimeter, luminal area, wall area, and total area. Because the airway contours are irregular on CT scans, distance measurements can be less accurate than area measurements. Therefore areas, such as luminal area and wall area, are preferred and are now the most commonly used measures (26,65,66,81,83, 87 89). However, these areas vary widely throughout the bronchial tree and even along the same branch (108), suggesting that several measurements should be considered in an individual patient. To our knowledge, there have been no studies to date to evaluate how many measurements would be representative of the entire airways tree. Two approaches have been proposed to obtain representative measurements. The first is to calculate the percentage of the total airway area occupied by its wall, an index less dependent of the airway size than area(s). The mean percentage of airway area occupied by the wall of the airways in an individual patient can be calculated and used to characterize this patient (26,65,85,86,88,89,92). The second approach is to plot each airway on a graph, with the inner perimeter along the x-axis, and the square root of the wall area along the y-axis. The slope of the straight-line relationship between these two indexes is then calculated, as well as the value of the square root of the wall area corresponding to a defined inner perimeter (eg, 10 mm), to obtain a value characterizing an individual patient (85,90,91) (Fig 8). A major limitation of these two approaches is that changes may be due to modifications either in airway lumen, airway wall, or both. A further limitation is the relatively low range of values, and overlap with normal findings. To our knowledge, there is currently no consensus on the most appropriate dimension to use. Further studies are thus needed to compare these various dimensions in several conditions and to determine the most robust one. Labeling and categorizing bronchi by generation and location are important because they are possible sources of variation. The number of considered airways has indeed markedly increased from one to presently all accessible large and intermediate airways (65,87). Automatic labeling and categorizing by software should be checked and corrected appropriately (87). Characterizing an individual patient as a whole, the square root of the wall area corresponding to a defined inner perimeter has the advantage of preventing us from labeling and categorizing. 40 radiology.rsna.org n Radiology: Volume 265: Number 1 October 2012

Figure 5 Figure 5: Airway dimensions measurements obtained with the erosion algorithm. A, From a seed point placed manually in the approximate center of the lumen, B, the algorithm adds the surrounding pixels with attenuation values below a specific threshold. Thereafter, luminal area is calculated by multiplying the number of pixels by pixel size. C, For measurement of wall dimensions, a circle is manually drawn that approximately fits the outer perimeter. D, E, The algorithm then iteratively erodes that circle until the outer border of the airway is clearly identified, and airway total area is then calculated. F, Wall area ultimately results from the difference between total area and luminal area. (Reprinted, with permission, from reference 83.) Normal values. To our knowledge, there is no study that provides normal values of airways dimensions at CT. Nevertheless, Montaudon et al (109), who used a Laplacian of Gaussian algorithm, provided such values for airways generation orders from zero to 10 in a group of 12 healthy subjects who were compared with subjects with asthma. Relationships between airways dimensions and clinical parameters. Airway wall thickness is related to symptoms of chronic bronchitis such as chronic cough and wheezing, symptoms not related to emphysema extent (91). This supports the fact that chronic bronchitis is related to the changes in the airways rather than in the parenchyma (16,46 48). Thus CT could be clinically useful in distinguishing patients, because airways changes are potentially reversible, whereas emphysema is not. In smokers or ex-smokers, airway wall thickness is also related to sex (airway walls are thicker in men than in women), age (airway walls thicken with increasing age), and smoking habits (airway walls thicken with daily consumption of cigarettes and cumulative pack-years) (90). In addition, airway walls are thicker in smokers than in neversmokers (92) and even thicker in patients with COPD (81). These relationships are important to consider for two reasons. First, they could help provide important information to convince a current smoker with normal pulmonary function test results but with changes demonstrated at CT to follow a smoking cessation program. Second, they could provide information for the standardization of measurements and for longitudinal follow-up. For example, when considering an individual patient in longitudinal follow-up, at least a part of the observed wall changes should be attributed to age and/or to possibly modified smoking habits. Other challenges of longitudinal evaluation could be the patient s intrinsic variability, the effect of treatment, the algorithm used for measurement, and the technical parameters of the CT examination. Quantification of Small Airways Obstruction Beside direct measurements of airways dimensions, CT can also help assess pathologic changes in the small airways by quantifying air trapping. Air trapping is defined as retention of air in the lung distal to an obstruction that can be seen on expiratory CT scans as lung areas with a less-than-normal increase in attenuation (110). Consequently, the magnitude of change in attenuation values between paired inspiratory and expiratory scans reflects the extent of air trapping. This magnitude, measured by using dedicated software similar to that used for quantifying emphysema (111), reflects the pathologic changes in small airways. Because pulmonary emphysema also corresponds to areas of reduced lung attenuation and is by itself a cause of air trapping, emphysema acts as a confounding factor in the quantification of air trapping. Therefore, Matsuoka et al (79) proposed a range of low attenuation values to minimize this confounding factor. Such indirect quantification of airways changes has three advantages: One does not need to consider airways by generation, the correlations between airflow limitation and this quantification are stronger than Radiology: Volume 265: Number 1 October 2012 n radiology.rsna.org 41

those obtained by mea suring bronchial dimensions, and it can provide regional assessment (79,88). As a spirometric Figure 6 Figure 6: (a) Square regions of interest delimit two airways on CT image, to which the Laplacian of Gaussian algorithm will be applied. (b) Binary images obtained by using the Laplacian of Gaussian algorithm in the two airways in a. The number of black and white pixels is converted to area to obtain measurements of the lumen and wall dimensions, respectively. (Reprinted, with permission, from reference 81.) Figure 7 measurement, however, indirect quantification provides only an assessment of the consequences of the pathologic changes of the airways and thus cannot be compared with pathologic measurements. Moreover, two CT acquisitions are needed, one at inspiration and one at expiration, with the latter not always easy to obtain in patients with COPD. Finally, there is still some overlap between air trapping due to small airways disease and to that due to emphysema. Historical overview of the methods. Studies showing that indexes derived from the attenuation values on expiratory or paired expiratory and inspiratory CT scans could reflect airway obstruction have been reported for emphysema (37,111,112), COPD (40,79,113 116), asthma (117,118), bronchiectasis (119), obliterative bronchiolitis (120,121), hypersensitivity pneumonitis (122,123), sarcoidosis (124), and pulmonary Langerhans cell histiocytosis (125). Using dedicated software, Matsuoka et al (113) investigated the relationships between relative area (RA) of lung with attenuation values within a particular ranges (eg, 2500 to 21024 HU) and pulmonary function test results in patients with COPD. These authors first calculated RA of lung with attenuation values lower than 2950 HU (RA 950 ) corresponding to emphysematous lung (32,33) and RA with attenuation values lower than 2900 HU (RA 900 ) as a reflection of air Figure 7: Axial CT image of sample phantom that was created for the COPDGene study. Four tubes (labeled 1 through 4) represent simulated airways of different sizes. (Reprinted, with permission, from reference 129.) trapping (117). They then identified the lung portions with little emphysema through a range of attenuation values from 2500 to 2950 HU. In these portions, RA ranging from 2900 to 2950 HU (RA 900 950 ) was considered to reflect air trapping in poorly emphysematous lung (113). These authors showed that although both changes in RA 900 and RA 900 950 between inspiratory and expiratory scans were correlated with the obstructive deficit at pulmonary function testing, the correlation was stronger with RA 900 950 than with RA 900, which suggests that paired inspiratory and expiratory scans in poorly emphysematous lung are suitable for the quantification of small airways obstruction (113). The same group of investigators (79) considered three-dimensional data rather than six two-dimensional sections per examination and tested several thresholds for air trapping. In addition, to test whether the relationship between air trapping and pulmonary function test results were independent of the degree of emphysema, they divided their patients into a minimal-tomild emphysematous group and a moderate-to-severe emphysematous group, according to the subjects inspiratory relative lung volume, with attenuation values lower than 2950 HU (32,33,79). They showed that the strongest correlations with the obstructive deficit were found with changes in attenuation values between 2860 and 2950 HU in portions of lung with little emphysema (79). These correlations were reported in both groups and suggest a limited influence of emphysema on the indirect assessment of airways changes. In clinical practice however, the calculation of such relative areas or volumes has two main limitations. First, it requires paired inspiratory and expiratory CT scans. Because reliable and reproducible CT scans may be difficult to obtain in patients with severe COPD and lung volume at acquisition influences its attenuation values (78), comparisons between consecutive examinations could be limited. Second, calculation of relative area or volume requires a combination of several estimates of lung area or volume, which is 42 radiology.rsna.org n Radiology: Volume 265: Number 1 October 2012

Figure 8 Figure 8: Graph shows relationship between inner perimeter (Pi) and square root of wall area (WA). The slope of the straight relationship between these two indexes is calculated, as well as the value of the square root of the wall area corresponding to a defined inner perimeter (eg, inner perimeter of 10 mm 5 AWT- Pi10). (Reprinted, with permission, from reference 90.) time consuming because of postprocessing and possible cumulative errors in calculation. For quantification of small airways obstruction with paired inspiratory and expiratory CT scans, volumetric acquisition should be preferred to help overcome possible misregistration if sequential sections are used and to take into account the heterogeneity of air trapping (126). Measurements of Airways Wall Changes Normally, the walls of small airways are very thin, usually less than 1 mm thick. In COPD, infiltration of the walls by inflammatory cells associated with the remodeling process results in changes in both size (thickening of each of the wall compartments) and physical density (modified composition of these compartments) (16). When such thin objects are scanned, the mean attenuation value underestimates their density (127,128), but the peak attenuation value is a function of size, density, and reconstruction kernel (99). Subsequently, Washko et al postulated that at a fixed reconstruction kernel the peak wall attenuation value reflects wall changes in both thickness and composition observed in COPD (129,130). This indirect quantification of wall changes has several advantages: it allows a regional assessment; it correlates with airflow limitation with the sixth-order bronchi more strongly than with third-order bronchi (130) and provides information regarding both thickness and composition of the airway wall. However, the peak wall attenuation has limitations as it indirectly reflects only changes within the wall and has not been validated against independent references. Historical overview of the methods. The CT ability to reflect the density of a tiny structure through its peak attenuation value was first reported in an analysis of cortical shell of vertebral bone (99). Washko et al (129) performed a computer-based simulation study, and the results suggested that measurements of airway wall attenuation could enable detection of changes in both wall thickness and composition; they recently proposed to apply this technique to the assessment of wall changes in patients with COPD. Those authors manually placed a seed point in the airway lumen, and the centroid point was automatically determined by the algorithm. From this centroid point, rays were spread in all directions. By using the FWHM algorithm (66), attenuation profiles along each ray were obtained. From these profiles, the mean lumen area, the mean wall area, the percentage of total airway area occupied by airway wall (WA%), and the mean peak wall attenuation value were then calculated and correlated with pulmonary function test results. On the basis of these correlations, these authors reported that the peak wall attenuation was comparable to WA% for predicting obstructive deficit at pulmonary function testing (129). While correlations between WA% and pulmonary function test results are stronger in distal than in proximal airways (88), measurements of WA% are less accurate in distal than in proximal airways (85). Yamashiro et al (130) compared WA% and the mean peak wall attenuation value of airways from the third to the sixth generation against pulmonary function test results. Both WA% and mean peak wall attenuation were calculated with the FWHM algorithm (66). These authors observed stronger correlations between mean peak attenuation values and PFT in distal than in proximal airways. Furthermore, when combined with CT quantification of emphysema extent, the mean peak wall attenuation was as predictive of forced expiratory volume in 1 second as WA% (130). Because this indirect approach to mea sure airway wall changes was very recently developed, its precise role in the quantification of airways disease at CT has not yet been established. However, since the mean peak attenuation value is obtained by using the FWHM algorithm, it is likely that this index would be liable to the same limitations as FWHM. How Can I Measure Airways at My Hospital? One who wishes to measure airways at CT could use noncommercial or commercial software. Airway Inspector (http:// www.airwayinspector.org; Brigham and Women s Hospital, Boston, Mass) and BronCare (Artemis Department, Institut National des Télécommunications, Evry, France) are two examples of noncommercial software. Airway Inspector uses the aforementioned FWHM algorithm and a phase congruency approach to mea sure airway dimensions and also allows calculation of the mean peak attenuation value of airways walls Radiology: Volume 265: Number 1 October 2012 n radiology.rsna.org 43

(66,68,131). BronCare relies on more recently developed algorithms for measurement of airways dimensions (69,70). Virtual Bronchoscopy (Siemens, Forchheim, Germany) and Pulmonary Workstation (VIDA Diagnostics, Coralville, Iowa) are two examples of commercial software. Both Virtual Bronchoscopy and Pulmonary Workstation also rely on recently developed algorithms for measurement of airways dimensions (71 74). Pulmonary Workstation has been used in large cohort studies such as the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints and COPDGene studies (28,29). Recently, Thoracic VCAR (GE Healthcare, Waukesha, Wis), which also provides measurements of the airways, became commercially available. Brillet et al (69) proposed five reasonable but arbitrary selection criteria that can possibly ensure accurate measurements of a particular airway: (a) segmental or subsegmental bronchus to measure a large- or intermediate-sized airway; (b) lumen area greater than 4 mm 2 to take into account the limited spatial resolution of CT responsible for low signal-to-noise ratio in airways with a lumen area smaller than 4 mm 2 ; (c) minimum bronchus length of 7 mm to ensure distance from bronchial bifurcation, where the outer contour of the bronchus could be difficult to differentiate from normal lymph nodes; (d) percentage of bronchus not abutted by a vessel by more than 55%; and (e) a minimum of 10 successive images that fulfill the selection criteria, so that averaged value can be calculated. Perspectives and Conclusion Three main directions for future research are indicated. First, methods should be simplified and standardized to be upgradeable in clinical practice. More precisely, postprocessing should be much less time consuming and not require additional manipulations such as transferring CT data to a personal computer or manual selection of all the airways up to the sixth generation on the CT images. In this perspective, further studies on morphologic measurements should be conducted to determine the minimum number of airways to consider, possibly by generation, to obtain an appropriate compromise between time spent on postprocessing and precise assessment of airways disease. It is also likely that progress in development of more automated software would help radiologists depict and label each airway. Second, consensus should be reached among researchers on the most appropriate method for quantifying airways disease in clinical practice, as well as in clinical trials. In this perspective, considerations for the patient s comfort during examinations and the radiation dose delivered would probably put at a relative disadvantage the methods of quantification of small airways obstruction requiring paired inspiratory and expiratory CT acquisitions. Consensus should also include definition of the method that provides the most appropri ate assessment of airways disease with regard to advantages and drawbacks. Third, longitudinal studies should be undertaken to determine the actual contributions of CT quantification of airways disease for predicting outcomes in patients with COPD and evaluating possible implementation in clinical management. In summary, currently available evidence and recent technical advances allow quantification of airways disease and suggest that quantification of emphysema extent and airways disease, both at CT, could match the definition of COPD phenotypes. Further advances in methods, practical aspects, and longitudinal follow-up studies of airways measurements are now needed to determine their respective contributions in care for the individual patient with COPD. Acknowledgment: We thank Donna Wolfe for her support in editing our manuscript. Disclosures of Potential Conflicts of Interest: M.H. No potential conflicts of interest to disclose. A.A.B. Financial activities related to the present article: none to disclose. Financial activities not related to the present article: served as consultant to Olympus; received payment for lectures from the American Thoracic Society; received royalties from Elsevier and Amirsys. Other relationships: none to disclose. P.A.G. No potential conflicts of interest to disclose. References 1. European Respiratory Society. European Lung White Book. Huddersfield, England: European Respiratory Society Journals, 2003. 2. Centers for Disease Control and Prevention. Chronic obstructive pulmonary disease surveillance: United States, 1971-2000. MMWR Surveill Summ 2002;51(SS-6):1 16. 3. Lopez AD, Shibuya K, Rao C, et al. Chronic obstructive pulmonary disease: current burden and future projections. Eur Respir J 2006;27(2):397 412. 4. Lopez AD, Murray CC. The global burden of disease, 1990-2020. Nat Med 1998;4(11): 1241 1243. 5. Murray CJ, Lopez AD. Alternative projections of mortality and disability by cause 1990-2020: Global Burden of Disease Study. Lancet 1997;349(9064):1498 1504. 6. Global Initiative for Chronic Obstructive Lung Disease. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. Bethesda, Md: National Heart, Lung, and Blood Institute/ World Health Organization, 2009. 7. Celli BR, Cote CG, Marin JM, et al. The body-mass index, airflow obstruction, dyspnea, and exercise capacity index in chronic obstructive pulmonary disease. N Engl J Med 2004;350(10):1005 1012. 8. Puhan MA, Garcia-Aymerich J, Frey M, et al. Expansion of the prognostic assessment of patients with chronic obstructive pulmonary disease: the updated BODE index and the ADO index. Lancet 2009;374(9691): 704 711. 9. Jones RC, Donaldson GC, Chavannes NH, et al. Derivation and validation of a composite index of severity in chronic obstructive pulmonary disease: the DOSE Index. Am J Respir Crit Care Med 2009;180(12): 1189 1195. 10. Jansson SA, Andersson F, Borg S, Ericsson A, Jönsson E, Lundbäck B. Costs of COPD in Sweden according to disease severity. Chest 2002;122(6):1994 2002. 11. Löfdahl CG, Postma DS, Laitinen LA, Ohlsson SV, Pauwels RA, Pride NB. The European Respiratory Society study on chronic obstructive pulmonary disease (EUROS COP): recruitment methods and strategies. Respir Med 1998;92(3):467 472. 12. Peto R, Speizer FE, Cochrane AL, et al. 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