Chest CT-based automated vertebral fracture assessment using artificial intelligence and morphologic features.

BACKGROUND: Spinal degeneration and vertebral compression fractures are common among the elderly that adversely affect their mobility, quality of life, lung function, and mortality. Assessment of vertebral fractures in chronic obstructive pulmonary disease (COPD) is important due to the high prevalence of osteoporosis and associated vertebral fractures in COPD. PURPOSE: We present new automated methods for (1) segmentation and labelling of individual vertebrae in chest computed tomography (CT) images using deep learning (DL), multi-parametric freeze-and-grow (FG) algorithm, and separation of apparently fused vertebrae using intensity autocorrelation and (2) vertebral deformity fracture detection using computed vertebral height features and parametric computational modelling of an established protocol outlined for trained human experts. METHODS: A chest CT-based automated method was developed for quantitative deformity fracture assessment following the protocol by Genant et al. The computational method was accomplished in the following steps: (1) computation of a voxel-level vertebral body likelihood map from chest CT using a trained DL network; (2) delineation and labelling of individual vertebrae on the likelihood map using an iterative multi-parametric FG algorithm; (3) separation of apparently fused vertebrae in CT using intensity autocorrelation; (4) computation of vertebral heights using contour analysis on the central anterior-posterior (AP) plane of a vertebral body; (5) assessment of vertebral fracture status using ratio functions of vertebral heights and optimized thresholds. The method was applied to inspiratory or total lung capacity (TLC) chest scans from the multi-site Genetic Epidemiology of COPD (COPDGene) (ClinicalTrials.gov: NCT00608764) study, and the performance was examined (n = 3231). One hundred and twenty scans randomly selected from this dataset were partitioned into training (n = 80) and validation (n = 40) datasets for the DL-based vertebral body classifier. Also, generalizability of the method to low dose CT imaging (n = 236) was evaluated. RESULTS: The vertebral segmentation module achieved a Dice score of .984 as compared to manual outlining results as reference (n = 100); the segmentation performance was consistent across images with the minimum and maximum of Dice scores among images being .980 and .989, respectively. The vertebral labelling module achieved 100% accuracy (n = 100). For low dose CT, the segmentation module produced image-level minimum and maximum Dice scores of .995 and .999, respectively, as compared to standard dose CT as the reference; vertebral labelling at low dose CT was fully consistent with standard dose CT (n = 236). The fracture assessment method achieved overall accuracy, sensitivity, and specificity of 98.3%, 94.8%, and 98.5%, respectively, for 40,050 vertebrae from 3231 COPDGene participants. For generalizability experiments, fracture assessment from low dose CT was consistent with the reference standard dose CT results across all participants. CONCLUSIONS: Our CT-based automated method for vertebral fracture assessment is accurate, and it offers a feasible alternative to manual expert reading, especially for large population-based studies, where automation is important for high efficiency. Generalizability of the method to low dose CT imaging further extends the scope of application of the method, particularly since the usage of low dose CT imaging in large population-based studies has increased to reduce cumulative radiation exposure.

Integrated genomic, proteomic and cognitive assessment in Duchenne Muscular Dystrophy suggest astrocyte centric pathology.

Introduction: Documented Duchenne Muscular Dystrophy (DMD) biomarkers are confined to Caucasians and are poor indicators of cognitive difficulties and neuropsychological alterations. Materials and methods: This study correlates serum protein signatures with cognitive performance in DMD patients of South Asian origin. Study included 25 DMD patients aged 6-16 years. Cognitive profiles were assessed by Wechsler Intelligence Scale for Children. Serum proteome profiling of 1317 proteins was performed in eight DMD patients and eight age-matched healthy volunteers. Results: Among the several novel observations we report, better cognitive performance in DMD was associated with increased serum levels of MMP9 and FN1 but decreased Siglec-3, C4b, and C3b. Worse cognitive performance was associated with increased serum levels of LDH-H1 and PDGF-BB but reduced GDF-11, MMP12, TPSB2, and G1B. Secondly, better cognitive performance in Processing Speed (PSI) and Perceptual Reasoning (PRI) domains was associated with intact Dp116, Dp140, and Dp71 dystrophin isoforms while better performance in Verbal Comprehension (VCI) and Working Memory (WMI) domains was associated with intact Dp116 and Dp140 isoforms. Finally, functional pathways shared with Alzheimer's Disease (AD) point towards an astrocyte-centric model for DMD. Conclusion: Astrocytic dysfunction leading to synaptic dysfunction reported previously in AD may be a common pathogenic mechanism underlying both AD and DMD, linking protein alterations to cognitive impairment. This new insight may pave the path towards novel therapeutic approaches targeting reactive astrocytes.

Imaging-based assessment of lung function in a population cooking indoors with biomass fuel: a pilot study.

Biomass fuels (wood) are commonly used indoors in underventilated environments for cooking in the developing world, but the impact on lung physiology is poorly understood. Quantitative computed tomography (qCT) can provide sensitive metrics to compare the lungs of women cooking with wood vs. liquified petroleum gas (LPG). We prospectively assessed (qCT and spirometry) 23 primary female cooks (18 biomass, 5 LPG) with no history of cardiopulmonary disease in Thanjavur, India. CT was obtained at coached total lung capacity (TLC) and residual volume (RV). qCT assessment included texture-derived ground glass opacity [GGO: Adaptive Multiple Feature Method (AMFM)], air-trapping (expiratory voxels ≤ -856HU) and image registration-based assessment [Disease Probability Measure (DPM)] of emphysema, functional small airways disease (%AirTrapDPM), and regional lung mechanics. In addition, within-kitchen exposure assessments included particulate matter <2.5 µm(PM2.5), black carbon, ß-(1, 3)-d-glucan (surrogate for fungi), and endotoxin. Air-trapping went undetected at RV via the threshold-based measure (voxels ≤ -856HU), possibly due to density shifts in the presence of inflammation. However, DPM, utilizing image-matching, demonstrated significant air-trapping in biomass vs. LPG cooks (P = 0.049). A subset of biomass cooks (6/18), identified using k-means clustering, had markedly altered DPM-metrics: greater air-trapping (P < 0.001), lower TLC-RV volume change (P < 0.001), a lower mean anisotropic deformation index (ADI; P < 0.001), and elevated % GGO (P < 0.02). Across all subjects, a texture measure of bronchovascular bundles was correlated to the log-transformed ß-(1, 3)-d-glucan concentration (P = 0.026, R = 0.46), and black carbon (P = 0.04, R = 0.44). This pilot study identified environmental links with qCT-based lung pathologies and a cluster of biomass cooks (33%) with significant small airways disease.NEW & NOTEWORTHY Quantitative computed tomography has identified a cluster of women (33%) cooking with biomass fuels (wood) with image-based markers of functional small airways disease and associated alterations in regional lung mechanics. Texture and image registration-based metrics of lung function may allow for early detection of potential inflammatory processes that may arise in response to inhaled biomass smoke, and help identify phenotypes of chronic lung disease prevalent in nonsmoking women in the developing world.

Quantitative Imaging Metrics for the Assessment of Pulmonary Pathophysiology: An Official American Thoracic Society and Fleischner Society Joint Workshop Report.

Multiple thoracic imaging modalities have been developed to link structure to function in the diagnosis and monitoring of lung disease. Volumetric computed tomography (CT) renders three-dimensional maps of lung structures and may be combined with positron emission tomography (PET) to obtain dynamic physiological data. Magnetic resonance imaging (MRI) using ultrashort-echo time (UTE) sequences has improved signal detection from lung parenchyma; contrast agents are used to deduce airway function, ventilation-perfusion-diffusion, and mechanics. Proton MRI can measure regional ventilation-perfusion ratio. Quantitative imaging (QI)-derived endpoints have been developed to identify structure-function phenotypes, including air-blood-tissue volume partition, bronchovascular remodeling, emphysema, fibrosis, and textural patterns indicating architectural alteration. Coregistered landmarks on paired images obtained at different lung volumes are used to infer airway caliber, air trapping, gas and blood transport, compliance, and deformation. This document summarizes fundamental "good practice" stereological principles in QI study design and analysis; evaluates technical capabilities and limitations of common imaging modalities; and assesses major QI endpoints regarding underlying assumptions and limitations, ability to detect and stratify heterogeneous, overlapping pathophysiology, and monitor disease progression and therapeutic response, correlated with and complementary to, functional indices. The goal is to promote unbiased quantification and interpretation of in vivo imaging data, compare metrics obtained using different QI modalities to ensure accurate and reproducible metric derivation, and avoid misrepresentation of inferred physiological processes. The role of imaging-based computational modeling in advancing these goals is emphasized. Fundamental principles outlined herein are critical for all forms of QI irrespective of acquisition modality or disease entity.

Quantitative Chest CT Assessment of Small Airways Disease in Post-Acute SARS-CoV-2 Infection.

Background The long-term effects of SARS-CoV-2 infection on pulmonary structure and function remain incompletely characterized. Purpose To test whether SARS-CoV-2 infection leads to small airways disease in patients with persistent symptoms. Materials and Methods In this single-center study at a university teaching hospital, adults with confirmed COVID-19 who remained symptomatic more than 30 days following diagnosis were prospectively enrolled from June to December 2020 and compared with healthy participants (controls) prospectively enrolled from March to August 2018. Participants with post-acute sequelae of COVID-19 (PASC) were classified as ambulatory, hospitalized, or having required the intensive care unit (ICU) based on the highest level of care received during acute infection. Symptoms, pulmonary function tests, and chest CT images were collected. Quantitative CT analysis was performed using supervised machine learning to measure regional ground-glass opacity (GGO) and using inspiratory and expiratory image-matching to measure regional air trapping. Univariable analyses and multivariable linear regression were used to compare groups. Results Overall, 100 participants with PASC (median age, 48 years; 66 women) were evaluated and compared with 106 matched healthy controls; 67% (67 of 100) of the participants with PASC were classified as ambulatory, 17% (17 of 100) were hospitalized, and 16% (16 of 100) required the ICU. In the hospitalized and ICU groups, the mean percentage of total lung classified as GGO was 13.2% and 28.7%, respectively, and was higher than that in the ambulatory group (3.7%, P < .001 for both comparisons). The mean percentage of total lung affected by air trapping was 25.4%, 34.6%, and 27.3% in the ambulatory, hospitalized, and ICU groups, respectively, and 7.2% in healthy controls (P < .001). Air trapping correlated with the residual volume-to-total lung capacity ratio (ρ = 0.6, P < .001). Conclusion In survivors of COVID-19, small airways disease occurred independently of initial infection severity. The long-term consequences are unknown. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Elicker in this issue.

On Pause: How a Group of Medical Students Engaged with Their Community During the COVID-19 Pandemic.

During academic clinical suspensions related to the COVID-19 pandemic, a group of medical students in Washington, D.C. collaborated with a local federally qualified health center to launch a free COVID-19 testing site to increase access to testing in the community. The patients who accessed the testing site were predominantly Black/African American and Hispanic/Latino, some of whom were uninsured or without access to testing or a timely physician's referral. In this article, medical students reflect on their experiences at this testing site and provide commentary on how existing racial and socioeconomic health disparities have been exacerbated by the COVID-19 pandemic. While under the extremely unusual circumstance of a suspension from their clinical rotations, medical students elaborate on the lessons learned from this experience and the continued work required to engage deeply in the issues of equality and racial justice now and in the future.

Case Studies in Physiology: Temporal variations of the lung parenchyma and vasculature in asymptomatic COVID-19 pneumonia: a multispectral CT assessment.

This study reports systematic longitudinal pathophysiology of lung parenchymal and vascular effects of asymptomatic COVID-19 pneumonia in a young, healthy never-smoking male. Inspiratory and expiratory noncontrast along with contrast dual-energy computed tomography (DECT) scans of the chest were performed at baseline on the day of acute COVID-19 diagnosis (day 0), and across a 90-day period. Despite normal vital signs and pulmonary function tests on the day of diagnosis, the CT scans and corresponding quantification metrics detected abnormalities in parenchymal expansion based on image registration, ground-glass (GGO) texture (inflammation) as well as DECT-derived pulmonary blood volume (PBV). Follow-up scans on day 30 showed improvement in the lung parenchymal mechanics as well as reduced GGO and improved PBV distribution. Improvements in lung PBV continued until day 90. However, the heterogeneity of parenchymal mechanics and texture-derived GGO increased on days 60 and 90. We highlight that even asymptomatic COVID-19 infection with unremarkable vital signs and pulmonary function tests can have measurable effects on lung parenchymal mechanics and vascular pathophysiology, which may follow apparently different clinical courses. For this asymptomatic subject, post COVID-19 regional mechanics demonstrated persistent increased heterogeneity concomitant with return of elevated GGOs, despite early improvements in vascular derangement.NEW & NOTEWORTHY We characterized the temporal changes of lung parenchyma and microvascular pathophysiology from COVID-19 infection in an asymptomatic young, healthy nonsmoking male using dual-energy CT. Lung parenchymal mechanics and microvascular disease followed different clinical courses. Heterogeneous perfused blood volume became more uniform on follow-up visits up to 90 days. However, post COVID-19 mechanical heterogeneity of the lung parenchyma increased after apparent improvements in vascular abnormalities, even with normal spirometric indices.

Racial Segregation and Respiratory Outcomes among Urban Black Residents with and at Risk of Chronic Obstructive Pulmonary Disease.

Rationale: Racial residential segregation has been associated with worse health outcomes, but the link with chronic obstructive pulmonary disease (COPD) morbidity has not been established.Objectives: To investigate whether racial residential segregation is associated with COPD morbidity among urban Black adults with or at risk of COPD.Methods: Racial residential segregation was assessed using isolation index, based on 2010 decennial census and baseline address, for Black former and current smokers in the multicenter SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study), a study of adults with or at risk for COPD. We tested the association between isolation index and respiratory symptoms, physiologic outcomes, imaging parameters, and exacerbation risk among urban Black residents, adjusting for established COPD risk factors, including smoking. Additional mediation analyses were conducted for factors that could lie on the pathway between segregation and COPD outcomes, including individual and neighborhood socioeconomic status, comorbidity burden, depression/anxiety, and ambient pollution.Measurements and Main Results: Among 515 Black participants, those residing in segregated neighborhoods (i.e., isolation index ⩾0.6) had worse COPD Assessment Test score (ß = 2.4; 95% confidence interval [CI], 0.7 to 4.0), dyspnea (modified Medical Research Council scale; ß = 0.29; 95% CI, 0.10 to 0.47), quality of life (St. George's Respiratory Questionnaire; ß = 6.1; 95% CI, 2.3 to 9.9), and cough and sputum (ß = 0.8; 95% CI, 0.1 to 1.5); lower FEV1% predicted (ß = -7.3; 95% CI, -10.9 to -3.6); higher rate of any and severe exacerbations; and higher percentage emphysema (ß = 2.3; 95% CI, 0.7 to 3.9) and air trapping (ß = 3.8; 95% CI, 0.6 to 7.1). Adverse associations attenuated with adjustment for potential mediators but remained robust for several outcomes, including dyspnea, FEV1% predicted, percentage emphysema, and air trapping.Conclusions: Racial residential segregation was adversely associated with COPD morbidity among urban Black participants and supports the hypothesis that racial segregation plays a role in explaining health inequities affecting Black communities.

Contribution of Individual and Neighborhood Factors to Racial Disparities in Respiratory Outcomes.

Rationale: Black adults have worse health outcomes compared with white adults in certain chronic diseases, including chronic obstructive pulmonary disease (COPD).Objectives: To determine to what degree disadvantage by individual and neighborhood socioeconomic status (SES) may contribute to racial disparities in COPD outcomes.Methods: Individual and neighborhood-scale sociodemographic characteristics were determined in 2,649 current or former adult smokers with and without COPD at recruitment into SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study). We assessed whether racial differences in symptom, functional, and imaging outcomes (St. George's Respiratory Questionnaire, COPD Assessment Test score, modified Medical Research Council dyspnea scale, 6-minute-walk test distance, and computed tomography [CT] scan metrics) and severe exacerbation risk were explained by individual or neighborhood SES. Using generalized linear mixed model regression, we compared respiratory outcomes by race, adjusting for confounders and individual-level and neighborhood-level descriptors of SES both separately and sequentially.Measurements and Main Results: After adjusting for COPD risk factors, Black participants had significantly worse respiratory symptoms and quality of life (modified Medical Research Council scale, COPD Assessment Test, and St. George's Respiratory Questionnaire), higher risk of severe exacerbations and higher percentage of emphysema, thicker airways (internal perimeter of 10 mm), and more air trapping on CT metrics compared with white participants. In addition, the association between Black race and respiratory outcomes was attenuated but remained statistically significant after adjusting for individual-level SES, which explained up to 12-35% of racial disparities. Further adjustment showed that neighborhood-level SES explained another 26-54% of the racial disparities in respiratory outcomes. Even after accounting for both individual and neighborhood SES factors, Black individuals continued to have increased severe exacerbation risk and persistently worse CT outcomes (emphysema, air trapping, and airway wall thickness).Conclusions: Disadvantages by individual- and neighborhood-level SES each partly explain disparities in respiratory outcomes between Black individuals and white individuals. Strategies to narrow the gap in SES disadvantages may help to reduce race-related health disparities in COPD; however, further work is needed to identify additional risk factors contributing to persistent disparities.

Heterogeneous burden of lung disease in smokers with borderline airflow obstruction.

BACKGROUND: The identification of smoking-related lung disease in current and former smokers with normal FEV1 is complex, leading to debate regarding using a ratio of forced expiratory volume in 1 s to forced vital capacity (FEV1/FVC) of less than 0.70 versus the predicted lower limit of normal (LLN) for diagnosis of airflow obstruction. We hypothesized that the discordant group of ever-smokers with FEV1/FVC between the LLN and 0.70 is heterogeneous, and aimed to characterize the burden of smoking-related lung disease in this group. METHODS: We compared spirometry, chest CT characteristics, and symptoms between 161 ever-smokers in the discordant group and 940 ever-smokers and 190 never-smokers with normal FEV1 and FEV1/FVC > 0.70 in the SPIROMICS cohort. We also estimated sensitivity and specificity for diagnosing objective radiographic evidence of chronic obstructive pulmonary disease (COPD) using different FEV1/FVC criteria thresholds. RESULTS: The discordant group had more CT defined emphysema and non-emphysematous gas trapping, lower post-bronchodilator FEV1 and FEF25-75, and higher respiratory medication use compared with the other two groups. Within the discordant group, 44% had radiographic CT evidence of either emphysema or non-emphysematous gas trapping; an FEV1/FVC threshold of 0.70 has greater sensitivity but lower specificity compared with LLN for identifying individuals with CT abnormality. CONCLUSIONS: Ever-smokers with normal FEV1 and FEV1/FVC <  0.70 but > LLN are a heterogeneous group that includes significant numbers of individuals with and without radiographic evidence of smoking-related lung disease. These findings emphasize the limitations of diagnosing COPD based on spirometric criteria alone.

PCB exposure and potential future cancer incidence in Slovak children: an assessment from molecular finger printing by Ingenuity Pathway Analysis (IPA®) derived from experimental and epidemiological investigations.

The risk of cancer due to PCB exposure in humans is highly debated. In eastern Slovakia, high exposure of the population to organochlorines (especially PCBs) was associated with various disease and disorder pathways, viz., endocrine disruption, metabolic disorder & diabetes, and cancer, thereby disturbing several cellular processes, including protein synthesis, stress response, and apoptosis. We have evaluated a Slovak cohort (45-month children, at lower and higher levels of PCB exposure from the environment) for disease and disorder development to develop early disease cancer biomarkers that could shed new light on possible mechanisms for the genesis of cancers under such chemical exposures, and identify potential avenues for prevention.Microarray studies of global gene expression were conducted from the 45-month-old children on the Affymetrix platform followed by Ingenuity Pathway Analysis (IPA®) to associate the affected genes with their mechanistic pathways. High-throughput qRT-PCR TaqMan low-density array (TLDA) was performed to further validate the selected genes on the whole blood cells of the most highly exposed children from the study cohort (n = 71). TP53, MYC, BCL2, and LRP12 differential gene expressions suggested strong relationships between potential future tumor promotion and PCB exposure in Slovak children. The IPA analysis further detected the most important signaling pathways, including molecular mechanism of cancers, prostate cancer signaling, ovarian cancer signaling, P53 signaling, oncostatin M signaling, and their respective functions (viz., prostate cancer, breast cancer, progression of tumor, growth of tumor, and non-Hodgkin's disease). The results suggest that PCB exposures, even at the early age of these children, may have lifelong consequences for the future development of chronic diseases.

Comparison of low- and ultralow-dose computed tomography protocols for quantitative lung and airway assessment.

PURPOSE: Quantitative computed tomography (CT) measures are increasingly being developed and used to characterize lung disease. With recent advances in CT technologies, we sought to evaluate the quantitative accuracy of lung imaging at low- and ultralow-radiation doses with the use of iterative reconstruction (IR), tube current modulation (TCM), and spectral shaping. METHODS: We investigated the effect of five independent CT protocols reconstructed with IR on quantitative airway measures and global lung measures using an in vivo large animal model as a human subject surrogate. A control protocol was chosen (NIH-SPIROMICS + TCM) and five independent protocols investigating TCM, low- and ultralow-radiation dose, and spectral shaping. For all scans, quantitative global parenchymal measurements (mean, median and standard deviation of the parenchymal HU, along with measures of emphysema) and global airway measurements (number of segmented airways and pi10) were generated. In addition, selected individual airway measurements (minor and major inner diameter, wall thickness, inner and outer area, inner and outer perimeter, wall area fraction, and inner equivalent circle diameter) were evaluated. Comparisons were made between control and target protocols using difference and repeatability measures. RESULTS: Estimated CT volume dose index (CTDIvol) across all protocols ranged from 7.32 mGy to 0.32 mGy. Low- and ultralow-dose protocols required more manual editing and resolved fewer airway branches; yet, comparable pi10 whole lung measures were observed across all protocols. Similar trends in acquired parenchymal and airway measurements were observed across all protocols, with increased measurement differences using the ultralow-dose protocols. However, for small airways (1.9 ± 0.2 mm) and medium airways (5.7 ± 0.4 mm), the measurement differences across all protocols were comparable to the control protocol repeatability across breath holds. Diameters, wall thickness, wall area fraction, and equivalent diameter had smaller measurement differences than area and perimeter measurements. CONCLUSIONS: In conclusion, the use of IR with low- and ultralow-dose CT protocols with CT volume dose indices down to 0.32 mGy maintains selected quantitative parenchymal and airway measurements relevant to pulmonary disease characterization.

Respiratory Symptoms Items from the COPD Assessment Test Identify Ever-Smokers with Preserved Lung Function at Higher Risk for Poor Respiratory Outcomes. An Analysis of the Subpopulations and Intermediate Outcome Measures in COPD Study Cohort.

RATIONALE: Ever-smokers without airflow obstruction scores greater than or equal to 10 on the COPD Assessment Test (CAT) still have frequent acute respiratory disease events (exacerbation-like), impaired exercise capacity, and imaging abnormalities. Identification of these subjects could provide new opportunities for targeted interventions. OBJECTIVES: We hypothesized that the four respiratory-related items of the CAT might be useful for identifying such individuals, with discriminative ability similar to CAT, which is an eight-item questionnaire used to assess chronic obstructive pulmonary disease impact, including nonrespiratory questions, with scores ranging from 0 to 40. METHODS: We evaluated ever-smoker participants in the Subpopulations and Intermediate Outcomes in COPD Study without airflow obstruction (FEV1/FVC ≥0.70; FVC above the lower limit of normal). Using the area under the receiver operating characteristic curve, we compared responses to both CAT and the respiratory symptom-related CAT items (cough, phlegm, chest tightness, and breathlessness) and their associations with longitudinal exacerbations. We tested agreement between the two strategies (κ statistic), and we compared demographics, lung function, and symptoms among subjects identified as having high symptoms by each strategy. RESULTS: Among 880 ever-smokers with normal lung function (mean age, 61 yr; 52% women) and using a CAT cutpoint greater than or equal to 10, we classified 51.8% of individuals as having high symptoms, 15.3% of whom experienced at least one exacerbation during 1-year follow-up. After testing sensitivity and specificity of different scores for the first four questions to predict any 1-year follow-up exacerbation, we selected cutpoints of 0-6 as representing a low burden of symptoms versus scores of 7 or higher as representing a high burden of symptoms for all subsequent comparisons. The four respiratory-related items with cutpoint greater than or equal to 7 selected 45.8% participants, 15.6% of whom experienced at least one exacerbation during follow-up. The two strategies largely identified the same individuals (agreement, 88.5%; κ = 0.77; P < 0.001), and the proportions of high-symptoms subjects who had severe dyspnea were similar between CAT and the first four CAT questions (25.9% and 26.8%, respectively), as were the proportions reporting impaired quality of life (66.9% and 70.5%, respectively) and short walking distance (22.4% and 23.1%, respectively). There was no difference in area under the receiver operating characteristic curve to predict 1-year follow-up exacerbations (CAT score ≥10, 0.66; vs. four respiratory items from CAT ≥7 score, 0.65; P = 0.69). Subjects identified by either method also had more depression/anxiety symptoms, poor sleep quality, and greater fatigue. CONCLUSIONS: Four CAT items on respiratory symptoms identified high-risk symptomatic ever-smokers with preserved spirometry as well as the CAT did. These data suggest that simpler strategies can be developed to identify these high-risk individuals in primary care.

The role of dual-energy computed tomography in the assessment of pulmonary function.

The assessment of pulmonary function, including ventilation and perfusion status, is important in addition to the evaluation of structural changes of the lung parenchyma in various pulmonary diseases. The dual-energy computed tomography (DECT) technique can provide the pulmonary functional information and high resolution anatomic information simultaneously. The application of DECT for the evaluation of pulmonary function has been investigated in various pulmonary diseases, such as pulmonary embolism, asthma and chronic obstructive lung disease and so on. In this review article, we will present principles and technical aspects of DECT, along with clinical applications for the assessment pulmonary function in various lung diseases.

A Robust and Efficient Curve Skeletonization Algorithm for Tree-Like Objects Using Minimum Cost Paths.

Conventional curve skeletonization algorithms using the principle of Blum's transform, often, produce unwanted spurious branches due to boundary irregularities, digital effects, and other artifacts. This paper presents a new robust and efficient curve skeletonization algorithm for three-dimensional (3-D) elongated fuzzy objects using a minimum cost path approach, which avoids spurious branches without requiring post-pruning. Starting from a root voxel, the method iteratively expands the skeleton by adding new branches in each iteration that connects the farthest quench voxel to the current skeleton using a minimum cost path. The path-cost function is formulated using a novel measure of local significance factor defined by the fuzzy distance transform field, which forces the path to stick to the centerline of an object. The algorithm terminates when dilated skeletal branches fill the entire object volume or the current farthest quench voxel fails to generate a meaningful skeletal branch. Accuracy of the algorithm has been evaluated using computer-generated phantoms with known skeletons. Performance of the method in terms of false and missing skeletal branches, as defined by human experts, has been examined using in vivo CT imaging of human intrathoracic airways. Results from both experiments have established the superiority of the new method as compared to the existing methods in terms of accuracy as well as robustness in detecting true and false skeletal branches. The new algorithm makes a significant reduction in computation complexity by enabling detection of multiple new skeletal branches in one iteration. Specifically, this algorithm reduces the number of iterations from the number of terminal tree branches to the worst case performance of tree depth. In fact, experimental results suggest that, on an average, the order of computation complexity is reduced to the logarithm of the number of terminal branches of a tree-like object.

Emphysema Quantification on Cardiac CT Scans Using Hidden Markov Measure Field Model: The MESA Lung Study.

Cardiac computed tomography (CT) scans include approximately 2/3 of the lung and can be obtained with low radiation exposure. Large cohorts of population-based research studies reported high correlations of emphysema quantification between full-lung (FL) and cardiac CT scans, using thresholding-based measurements. This work extends a hidden Markov measure field (HMMF) model-based segmentation method for automated emphysema quantification on cardiac CT scans. We show that the HMMF-based method, when compared with several types of thresholding, provides more reproducible emphysema segmentation on repeated cardiac scans, and more consistent measurements between longitudinal cardiac and FL scans from a diverse pool of scanner types and thousands of subjects with ten thousands of scans.

Assessment of regional ventilation and deformation using 4D-CT imaging for healthy human lungs during tidal breathing.

This study aims to assess regional ventilation, nonlinearity, and hysteresis of human lungs during dynamic breathing via image registration of four-dimensional computed tomography (4D-CT) scans. Six healthy adult humans were studied by spiral multidetector-row CT during controlled tidal breathing as well as during total lung capacity and functional residual capacity breath holds. Static images were utilized to contrast static vs. dynamic (deep vs. tidal) breathing. A rolling-seal piston system was employed to maintain consistent tidal breathing during 4D-CT spiral image acquisition, providing required between-breath consistency for physiologically meaningful reconstructed respiratory motion. Registration-derived variables including local air volume and anisotropic deformation index (ADI, an indicator of preferential deformation in response to local force) were employed to assess regional ventilation and lung deformation. Lobar distributions of air volume change during tidal breathing were correlated with those of deep breathing (R(2) ≈ 0.84). Small discrepancies between tidal and deep breathing were shown to be likely due to different distributions of air volume change in the left and the right lungs. We also demonstrated an asymmetric characteristic of flow rate between inhalation and exhalation. With ADI, we were able to quantify nonlinearity and hysteresis of lung deformation that can only be captured in dynamic images. Nonlinearity quantified by ADI is greater during inhalation, and it is stronger in the lower lobes (P < 0.05). Lung hysteresis estimated by the difference of ADI between inhalation and exhalation is more significant in the right lungs than that in the left lungs.

Quantitative assessment of multiscale structural and functional alterations in asthmatic populations.

Relationships between structural and functional variables in asthmatic lungs at local and global (or lobar) levels remain to be discovered. This study aims to investigate local alterations of structural variables [bifurcation angle, circularity, airway wall thickness (WT), and hydraulic diameter (Dh)] in asthmatic subjects, and their correlations with other imaging and pulmonary function test-based global and lobar metrics, including lung shape, air-trapping, regional volume change, and more. Sixty-one healthy subjects, and 67 nonsevere and 67 severe asthmatic subjects were studied. The structural variables were derived from computed tomography images at total lung capacity (TLC). Air-trapping was measured at functional residual capacity, and regional volume change (derived from image registration) was measured between functional residual capacity and TLC. The tracheal diameter and WT predicted by 61 healthy subjects were used to normalize the Dh and WT. New normalization schemes allowed for the dissociation of luminal narrowing and wall thickening effects. In severe asthmatic subjects, the alteration of bifurcation angle was found to be correlated with a global lung shape at TLC, and circularity was significantly decreased in the right main bronchus. While normalized WT increased especially in the upper lobes of severe asthmatic subjects, normalized Dh decreased in the lower lobes. Among local structural variables, normalized Dh was the most representative variable, because it was significantly correlated with alterations of functional variables, including pulmonary function test's data. In conclusion, understanding multiscale phenomena may help to provide guidance in the search for potential imaging-based phenotypes for the development and outcomes assessment of therapeutic intervention.

Adaptive quantification and longitudinal analysis of pulmonary emphysema with a hidden Markov measure field model.

The extent of pulmonary emphysema is commonly estimated from CT scans by computing the proportional area of voxels below a predefined attenuation threshold. However, the reliability of this approach is limited by several factors that affect the CT intensity distributions in the lung. This work presents a novel method for emphysema quantification, based on parametric modeling of intensity distributions and a hidden Markov measure field model to segment emphysematous regions. The framework adapts to the characteristics of an image to ensure a robust quantification of emphysema under varying CT imaging protocols, and differences in parenchymal intensity distributions due to factors such as inspiration level. Compared to standard approaches, the presented model involves a larger number of parameters, most of which can be estimated from data, to handle the variability encountered in lung CT scans. The method was applied on a longitudinal data set with 87 subjects and a total of 365 scans acquired with varying imaging protocols. The resulting emphysema estimates had very high intra-subject correlation values. By reducing sensitivity to changes in imaging protocol, the method provides a more robust estimate than standard approaches. The generated emphysema delineations promise advantages for regional analysis of emphysema extent and progression.