Time to Exhale: Additional Value of Expiratory Chest CT in Chronic Obstructive Pulmonary Disease

Evaluating the Cumulative Benefit of Inspiratory CT, Expiratory CT, and Clinical Data for COPD Diagnosis and Staging through Deep Learning

Purpose To measure the benefit of single-phase CT, inspiratory-expiratory CT, and clinical data for convolutional neural network (CNN)-based chronic obstructive pulmonary disease (COPD) staging. Materials and Methods This retrospective study included inspiratory and expiratory lung CT images and spirometry measurements acquired between November 2007 and April 2011 from 8893 participants (mean age, 59.6 years ± 9.0 [SD]; 53.3% [4738 of 8893] male) in the COPDGene phase I cohort (ClinicalTrials.gov: NCT00608764). CNNs were trained to predict spirometry measurements (forced expiratory volume in 1 second [FEV1], FEV1 percent predicted, and ratio of FEV1 to forced vital capacity [FEV1/FVC]) using clinical data and either single-phase or multiphase CT. Spirometry predictions were then used to predict Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage. Agreement between CNN-predicted and reference standard spirometry measurements and GOLD stage was assessed using intraclass correlation coefficient (ICC) and compared using bootstrapping. Accuracy for predicting GOLD stage, within-one GOLD stage, and GOLD 0 versus 1-4 was calculated. Results CNN-predicted and reference standard spirometry measurements showed moderate to good agreement (ICC, 0.66-0.79), which improved by inclusion of clinical data (ICC, 0.70-0.85; P ≤ .04), except for FEV1/FVC in the inspiratory-phase CNN model with clinical data (P = .35) and FEV1 in the expiratory-phase CNN model with clinical data (P = .33). Single-phase CNN accuracies for GOLD stage, within-one stage, and diagnosis ranged from 59.8% to 84.1% (682-959 of 1140), with moderate to good agreement (ICC, 0.68-0.70). Accuracies of CNN models using inspiratory and expiratory images ranged from 60.0% to 86.3% (684-984 of 1140), with moderate to good agreement (ICC, 0.72). Inclusion of clinical data improved agreement and accuracy for both the single-phase CNNs (ICC, 0.72; P ≤ .001; accuracy, 65.2%-85.8% [743-978 of 1140]) and inspiratory-expiratory CNNs (ICC, 0.77-0.78; P ≤ .001; accuracy, 67.6%-88.0% [771-1003 of 1140]), except expiratory CNN with clinical data (no change in GOLD stage ICC; P = .08). Conclusion CNN-based COPD diagnosis and staging using single-phase CT provides comparable accuracy with inspiratory-expiratory CT when provided clinical data relevant to staging. Keywords: Convolutional Neural Network, Chronic Obstructive Pulmonary Disease, CT, Severity Staging, Attention Map Supplemental material is available for this article. © RSNA, 2024.

Detection of chronic obstructive pulmonary disease with deep learning using inspiratory and expiratory chest computed tomography and clinical information

Background

In recent years, more and more patients with chronic obstructive pulmonary disease (COPD) have remained undiagnosed despite having undergone medical examination. This study aimed to develop a convolutional neural network (CNN) model for automatically detecting COPD using double-phase (inspiratory and expiratory) chest computed tomography (CT) images and clinical information.

Methods

A total of 2,047 participants, including never-smokers, ex-smokers, and current smokers, were prospectively recruited from three hospitals. The double-phase CT images and clinical information of each participant were collected for training the proposed CNN model which integrated a sequence of residual feature extracting blocks network (RFEBNet) for extracting CT image features and a fully connected feed-forward network (FCNet) for extracting clinical features. In addition, the RFEBNet utilizing double- or single-phase CT images and the FCNet using clinical information were conducted for comparison.

Results

The proposed CNN model, which utilized double-phase CT images and clinical information, outperformed other models in detecting COPD with an area under the receiver operating characteristic curve (AUC) of 0.930 [95% confidence interval (CI): 0.913-0.951] on an internal test set (n=307). The AUC was higher than the RFEBNet using double-phase CT images (AUC =0.912, 95% CI: 0.891-0.932), single inspiratory CT images (AUC =0.888, 95% CI: 0.863-0.915), single expiratory CT images (AUC =0.897, 95% CI: 0.874-0.925), and FCNet using clinical information (AUC =0.805, 95% CI: 0.777-0.841). The proposed model also achieved the best performance on an external test (n=516) with an AUC of 0.896 (95% CI: 0.871-0.931).

Conclusions

The proposed CNN model using double-phase CT images and clinical information can automatically detect COPD with high accuracy.

Comparison of lung volumes measured with computed tomography and whole-body plethysmography - a systematic review

Introduction

Whole-body plethysmography is the preferred method for measuring the static lung volumes: total lung capacity (TLC), functional residual capacity (FRC) and residual volume (RV), as it also incorporates trapped gas - a common finding in chronic obstructive pulmonary disease (COPD). Quantitative computed tomography (CT) is a promising alternative to plethysmography, which can be challenging to perform for patients with severely impaired lung function. The present systematic review explores the agreement between lung volumes measured by plethysmography and CT, as well as the attempts being made to optimize alignment between these two methods.

Methods

A literature search was performed on the PubMed database using the block search strategy. Articles were included if they provided both CT based and plethysmography based TLC. Risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) checklist.

Results

22 articles were included. On average, CT-derived TLC (CT-TLC) was 709 mL lower compared to plethysmography TLC (p-TLC) with a 12.1% deviation from the reference standard, p-TLC. This discrepancy (ΔTLC) appeared slightly larger in obstructive patients (obstructive: 781 mL, non-obstructive: 609 mL), whereas percent deviation was slightly smaller (obstructive: 11.4%, non-obstructive: 13.5%). CT-based RV analyses primarily based on COPD patients measured 603 mL higher than plethysmography (p-RV) with 17.8% deviation from p-RV. Studies utilizing spirometry-gating for CT acquisition reported good agreement between modalities (ΔTLC: 70-280 mL), and one study demonstrated noticeable improvements compared to conventional breath-hold instructions in an otherwise identical study setting.

Conclusion

CT quantifications routinely underestimate TLC and overestimate RV in comparison to plethysmography. Spirometry gating reduces the level of disagreement and can be of assistance when patients are already undergoing CT. However, further studies are needed to confirm these results.

Predicting Acute Exacerbation Phenotype in Chronic Obstructive Pulmonary Disease Patients Using VGG-16 Deep Learning

INTRODUCTION

Exacerbations of chronic obstructive pulmonary disease (COPD) have a significant impact on hospitalizations, morbidity, and mortality of patients. This study aimed to develop a model for predicting acute exacerbation in COPD patients (AECOPD) based on deep-learning (DL) features.

METHODS

We performed a retrospective study on 219 patients with COPD who underwent inspiratory and expiratory HRCT scans. By recording the acute respiratory events of the previous year, these patients were further divided into non-AECOPD group and AECOPD group according to the presence of acute exacerbation events. Sixty-nine quantitative CT (QCT) parameters of emphysema and airway were calculated by NeuLungCARE software, and 2,000 DL features were extracted by VGG-16 method. The logistic regression method was employed to identify AECOPD patients, and 29 patients of external validation cohort were used to access the robustness of the results.

RESULTS

The model 3-B achieved an area under the receiver operating characteristic curve (AUC) of 0.933 and 0.865 in the testing cohort and external validation cohort, respectively. Model 3-I obtained AUC of 0.895 in the testing cohort and AUC of 0.774 in the external validation cohort. Model 7-B combined clinical characteristics, QCT parameters, and DL features achieved the best performance with an AUC of 0.979 in the testing cohort and demonstrating robust predictability with an AUC of 0.932 in the external validation cohort. Likewise, model 7-I achieved an AUC of 0.938 and 0.872 in the testing cohort and external validation cohort, respectively.

CONCLUSIONS

DL features extracted from HRCT scans can effectively predict acute exacerbation phenotype in COPD patients.

Pulmonary volumes and signs of chronic airflow limitation in quantitative computed tomography

BACKGROUND

Computed tomography (CT) offers pulmonary volumetric quantification but is not commonly used in healthy individuals due to radiation concerns. Chronic airflow limitation (CAL) is one of the diagnostic criteria for chronic obstructive pulmonary disease (COPD), where early diagnosis is important. Our aim was to present reference values for chest CT volumetric and radiodensity measurements and explore their potential in detecting early signs of CAL.

METHODS

From the population-based Swedish CArdioPulmonarybioImage Study (SCAPIS), 294 participants aged 50-64, were categorized into non-CAL (n = 258) and CAL (n = 36) groups based on spirometry. From inspiratory and expiratory CT images we compared lung volumes, mean lung density (MLD), percentage of low attenuation volume (LAV%) and LAV cluster volume between groups, and against reference values from static pulmonary function test (PFT).

RESULTS

The CAL group exhibited larger lung volumes, higher LAV%, increased LAV cluster volume and lower MLD compared to the non-CAL group. Lung volumes significantly deviated from PFT values. Expiratory measurements yielded more reliable results for identifying CAL compared to inspiratory. Using a cut-off value of 0.6 for expiratory LAV%, we achieved sensitivity, specificity and positive/negative predictive values of 72%, 85% and 40%/96%, respectively.

CONCLUSION

We present volumetric reference values from inspiratory and expiratory chest CT images for a middle-aged healthy cohort. These results are not directly comparable to those from PFTs. Measures of MLD and LAV can be valuable in the evaluation of suspected CAL. Further validation and refinement are necessary to demonstrate its potential as a decision support tool for early detection of COPD.

Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT

OBJECTIVES

To quantify regional manifestations related to COPD as anomalies from a modeled distribution of normal-appearing lung on chest CT using a deep learning (DL) approach, and to assess its potential to predict disease severity.

MATERIALS AND METHODS

Paired inspiratory/expiratory CT and clinical data from COPDGene and COSYCONET cohort studies were included. COPDGene data served as training/validation/test data sets (N = 3144/786/1310) and COSYCONET as external test set (N = 446). To differentiate low-risk (healthy/minimal disease, [GOLD 0]) from COPD patients (GOLD 1-4), the self-supervised DL model learned semantic information from 50 × 50 × 50 voxel samples from segmented intact lungs. An anomaly detection approach was trained to quantify lung abnormalities related to COPD, as regional deviations. Four supervised DL models were run for comparison. The clinical and radiological predictive power of the proposed anomaly score was assessed using linear mixed effects models (LMM).

RESULTS

The proposed approach achieved an area under the curve of 84.3 ± 0.3 (p < 0.001) for COPDGene and 76.3 ± 0.6 (p < 0.001) for COSYCONET, outperforming supervised models even when including only inspiratory CT. Anomaly scores significantly improved fitting of LMM for predicting lung function, health status, and quantitative CT features (emphysema/air trapping; p < 0.001). Higher anomaly scores were significantly associated with exacerbations for both cohorts (p < 0.001) and greater dyspnea scores for COPDGene (p < 0.001).

CONCLUSION

Quantifying heterogeneous COPD manifestations as anomaly offers advantages over supervised methods and was found to be predictive for lung function impairment and morphology deterioration.

CLINICAL RELEVANCE STATEMENT

Using deep learning, lung manifestations of COPD can be identified as deviations from normal-appearing chest CT and attributed an anomaly score which is consistent with decreased pulmonary function, emphysema, and air trapping.

KEY POINTS

• A self-supervised DL anomaly detection method discriminated low-risk individuals and COPD subjects, outperforming classic DL methods on two datasets (COPDGene AUC = 84.3%, COSYCONET AUC = 76.3%). • Our contrastive task exhibits robust performance even without the inclusion of expiratory images, while voxel-based methods demonstrate significant performance enhancement when incorporating expiratory images, in the COPDGene dataset. • Anomaly scores improved the fitting of linear mixed effects models in predicting clinical parameters and imaging alterations (p < 0.001) and were directly associated with clinical outcomes (p < 0.001).

Lung vessel connectivity map as anatomical prior knowledge for deep learning-based lung lobe segmentation

Purpose

Our study investigates the potential benefits of incorporating prior anatomical knowledge into a deep learning (DL) method designed for the automated segmentation of lung lobes in chest CT scans.

Approach

We introduce an automated DL-based approach that leverages anatomical information from the lung's vascular system to guide and enhance the segmentation process. This involves utilizing a lung vessel connectivity (LVC) map, which encodes relevant lung vessel anatomical data. Our study explores the performance of three different neural network architectures within the nnU-Net framework: a standalone U-Net, a multitasking U-Net, and a cascade U-Net.

Results

Experimental findings suggest that the inclusion of LVC information in the DL model can lead to improved segmentation accuracy, particularly, in the challenging boundary regions of expiration chest CT volumes. Furthermore, our study demonstrates the potential for LVC to enhance the model's generalization capabilities. Finally, the method's robustness is evaluated through the segmentation of lung lobes in 10 cases of COVID-19, demonstrating its applicability in the presence of pulmonary diseases.

Conclusions

Incorporating prior anatomical information, such as LVC, into the DL model shows promise for enhancing segmentation performance, particularly in the boundary regions. However, the extent of this improvement has limitations, prompting further exploration of its practical applicability.

Clinical evaluation of pulmonary quantitative computed tomography parameters for diagnosing eosinophilic chronic obstructive pulmonary disease: Characteristics and diagnostic performance

Aims

To investigate the characteristics and diagnostic performance of quantitative computed tomography (QCT) parameters in eosinophilic chronic obstructive pulmonary disease (COPD) patients.

Methods

High-resolution CT scans of COPD patients were retrospectively analyzed, and various emphysematous parenchyma measurements, including lung volume (LC), lung mean density (LMD), lung standard deviation (LSD), full-width half maximum (FWHM), and lung relative voxel number (LRVN) were performed. The QCT parameters were compared between eosinophilic and noneosinophilic COPD patients, using a definition of eosinophilic COPD as blood eosinophil values ≥ 300 cells·µL-1 on at least three times. Receiver operating characteristic curves and area under the curve (ROC-AUC) and python were used to evaluate discriminative efficacy of QCT.

Results

Noneosinophilic COPD patients had a significantly lower TLMD (-846.3 ± 47.9 Hounsfield Unit [HU]) and TFWHM(162.5 ± 30.6 HU) compared to eosinophilic COPD patients (-817.8 ± 54.4, 177.3 ± 33.1 HU, respectively) (p = 0.018, 0.03, respectively). Moreover, the total LC (TLC) and TLSD were significantly lower in eosinophilic COPD group (3234.4 ± 1145.8, 183.8 ± 33.9 HU, respectively) than the noneosinophilic COPD group (5600.2 ± 1248.4, 203.5 ± 20.4 HU, respectively) (p = 0.009, 0.002, respectively). The ROC-AUC values for TLC, TLMD, TLSD, and TFWHM were 0.91 (95% confidence interval [CI], 0.828-0.936), 0.66 (95% CI, 0.546-0.761), 0.64 (95% CI, 0.524-0.742), and 0.63 (95% CI, 0.511-0.731), respectively. When the TLC value was 4110 mL, the sensitivity was 90.7% (95% CI, 79.7-96.9), specificity was 77.8% (95% CI, 57.7-91.4) and accuracy was 86.4%. Notably, TLC demonstrated the highest discriminative efficiency with an F1 Score of 0.79, diagnostic Odds Ratio of 34.3 and Matthews Correlation Coefficient of 0.69, surpassing TLMD (0.55, 3.66, 0.25), TLSD (0.56, 3.95, 0.26), and TFWHM (0.56, 4.16, 0.33).

Conclusion

Eosinophilic COPD patients exhibit lower levels of emphysema and a more uniform density distribution throughout the lungs compared to noneosinophilic COPD patients. Furthermore, TLC demonstrated the highest diagnostic efficiency and may serve as a valuable diagnostic marker for distinguishing between the two groups.

Expiratory CT scan: When to do it and how to interpret it

Expiratory CT scan is a complementary technique of inspiratory CT that provide valuable physiological information and may be more sensitive to detect air trapping than pul-monary function tests. It is useful in many obstructive airway diseases, including obliterative bronchiolitis, asthma, Swyer-James syndrome, tracheomalacia, hypersensitivity pneumonitis and sarcoidosis. In obliterative bronchiolitis, expiratory CT scan may be the only imaging technique that shows abnormalities in the early phase of disease. In order to obtain a good quality study, we should explain the procedure to the patient, use precise instructions and do some practice before image acquisition. Here we describe strategies to optimize the techni-que and propose an algorithm that help in interpretation of imaging findings in patients with obstructive airway disease.

Quantitative analysis of changes in lung density by dynamic chest radiography in association with CT values: a virtual imaging study and initial clinical corroboration

Dynamic chest radiography (DCR) identifies pulmonary impairments as decreased changes in radiographic lung density during respiration (Δpixel values), but not as scaled/standardized computed tomography (CT) values. Quantitative analysis correlated with CT values is beneficial for a better understanding of Δpixel values in DCR-based assessment of pulmonary function. The present study aimed to correlate Δpixel values from DCR with changes in CT values during respiration (ΔCT values) through a computer-based phantom study. A total of 20 four-dimensional computational phantoms during forced breathing were created to simulate both CT and projection images of the same virtual patients. The Δpixel and ΔCT values of the lung fields were correlated on a regression line, and the inclination was statistically evaluated to determine whether there were significant differences among physical types, sex, and breathing methods. The resulting conversion expression was also assessed in the DCR images of 37 patients. The resulting Δpixel values for 30/37 (81%) real patients, 6/7 (86%) normal controls, and 24/30 (80%) chronic obstructive pulmonary disorder patients were within the range of ΔCT values ± standard deviation (SD) reported in a previous study. In addition, no significant differences were detected for each condition of thoracic breathing, suggesting that the same regression line inclination values measured across the entire lung can be used for the conversion of Δpixel values, providing a quantitative analysis that can be correlated with ΔCT values. The developed conversion expression may be helpful for improving the understanding of respiratory changes using radiographic lung densities from DCR-based assessments of pulmonary function.

Potential Value of Expiratory CT in Quantitative Assessment of Pulmonary Vessels in COPD

Objective: To investigate the associations between intrapulmonary vascular volume (IPVV) depicted on inspiratory and expiratory CT scans and disease severity in COPD patients, and to determine which CT parameters can be used to predict IPVV.

Methods: We retrospectively collected 89 CT examinations acquired on COPD patients from an available database. All subjects underwent both inspiratory and expiratory CT scans. We quantified the IPVV, airway wall thickness (WT), the percentage of the airway wall area (WA%), and the extent of emphysema (LAA%₋₉₅₀) using an available pulmonary image analysis tool. The underlying relationship between IPVV and COPD severity, which was defined as mild COPD (GOLD stage I and II) and severe COPD (GOLD stage III and IV), was analyzed using the Student's t-test (or Mann-Whitney U-test). The correlations of IPVV with pulmonary function tests (PFTs), LAA%₋₉₅₀, and airway parameters for the third to sixth generation bronchus were analyzed using the Pearson or Spearman's rank correlation coefficients and multiple stepwise regression.

Results: In the subgroup with only inspiratory examinations, the correlation coefficients between IPVV and PFT measures were −0.215 ~ −0.292 (p < 0.05), the correlation coefficients between IPVV and WT₃₋₆ were 0.233 ~ 0.557 (p < 0.05), and the correlation coefficient between IPVV and LAA%₋₉₅₀ were 0.238 ~ 0.409 (p < 0.05). In the subgroup with only expiratory scan, the correlation coefficients between IPVV and PFT measures were −0.238 ~ −0.360 (p < 0.05), the correlation coefficients between IPVV and WT₃₋₆ were 0.260 ~ 0.566 (p < 0.05), and the correlation coefficient between IPVV and LAA%₋₉₅₀ were 0.241 ~ 0.362 (p < 0.05). The multiple stepwise regression analyses demonstrated that WT were independently associated with IPVV (P < 0.05).

Conclusion: The expiratory CT scans can provide a more accurate assessment of COPD than the inspiratory CT scans, and the airway wall thickness maybe an independent predictor of pulmonary vascular alteration in patients with COPD.

Computed tomography-identified phenotypes of small airway obstructions in chronic obstructive pulmonary disease

ABSTRACT

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease characteristic of small airway inflammation, obstruction, and emphysema. It is well known that spirometry alone cannot differentiate each separate component. Computed tomography (CT) is widely used to determine the extent of emphysema and small airway involvement in COPD. Compared with the pulmonary function test, small airway CT phenotypes can accurately reflect disease severity in patients with COPD, which is conducive to improving the prognosis of this disease. CT measurement of central airway morphology has been applied in clinical, epidemiologic, and genetic investigations as an inference of the presence and severity of small airway disease. This review will focus on presenting the current knowledge and methodologies in chest CT that aid in identifying discrete COPD phenotypes.

A pilot study: Quantify lung volume and emphysema extent directly from two-dimensional scout images

PURPOSE

The potential to compute volume metrics of emphysema from planar scout images was investigated in this study. The successful implementation of this concept will have a wide impact in different fields, and specifically, maximize the diagnostic potential of the planar medical images.

METHODS

We investigated our premise using a well-characterized chronic obstructive pulmonary disease (COPD) cohort. In this cohort, planar scout images from computed tomography (CT) scans were used to compute lung volume and percentage of emphysema. Lung volume and percentage of emphysema were quantified on the volumetric CT images and used as the "ground truth" for developing the models to compute the variables from the corresponding scout images. We trained two classical convolutional neural networks (CNNs), including VGG19 and InceptionV3, to compute lung volume and the percentage of emphysema from the scout images. The scout images (n = 1,446) were split into three subgroups: (1) training (n = 1,235), (2) internal validation (n = 99), and (3) independent test (n = 112) at the subject level in a ratio of 8:1:1. The mean absolute difference (MAD) and R-square (R2) were the performance metrics to evaluate the prediction performance of the developed models.

RESULTS

The lung volumes and percentages of emphysema computed from a single planar scout image were significantly linear correlated with the measures quantified using volumetric CT images (VGG19: R2 = 0.934 for lung volume and R2 = 0.751 for emphysema percentage, and InceptionV3: R2 = 0.977 for lung volume and R2 = 0.775 for emphysema percentage). The mean absolute differences (MADs) for lung volume and percentage of emphysema were 0.302 ± 0.247L and 2.89 ± 2.58%, respectively, for VGG19, and 0.366 ± 0.287L and 3.19 ± 2.14, respectively, for InceptionV3.

CONCLUSIONS

Our promising results demonstrated the feasibility of inferring volume metrics from planar images using CNNs.

Computed tomography based measurements to evaluate lung density and lung growth after congenital diaphragmatic hernia

Emphysema-like-change of lung is one aspect of lung morbidity in children after congenital diaphragmatic hernia (CDH). This study aims to evaluate if the extent of reduced lung density can be quantified through pediatric chest CT examinations, if side differences are present and if emphysema-like tissue is more prominent after CDH than in controls. Thirty-seven chest CT scans of CDH patients (mean age 4.5 ± 4.0 years) were analyzed semi-automatically and compared to an age-matched control group. Emphysema-like-change was defined as areas of lung density lower than - 950 HU in percentage (low attenuating volume, LAV). A p-value lower than 0.05 was regarded as statistically significant. Hypoattenuating lung tissue was more frequently present in the ipsilateral lung than the contralateral side (LAV 12.6% vs. 5.7%; p < 0.0001). While neither ipsilateral nor contralateral lung volume differed between CDH and control (p > 0.05), LAV in ipsilateral (p = 0.0002), but not in contralateral lung (p = 0.54), was higher in CDH than control. It is feasible to quantify emphysema-like-change in pediatric patients after CDH. In the ipsilateral lung, low-density areas are much more frequently present both in comparison to contralateral and to controls. Especially the ratio of LAV ipsilateral/contralateral seems promising as a quantitative parameter in the follow-up after CDH.

COPD Imaging on a 3rd Generation Dual-Source CT: Acquisition of Paired Inspiratory-Expiratory Chest Scans at an Overall Reduced Radiation Risk

As stated by the Fleischner Society, an additional computed tomography (CT) scan in expiration is beneficial in patients with chronic obstructive pulmonary disease (COPD). It was thus the aim of this study to evaluate the radiation risk of a state-of-the-art paired inspiratory-expiratory chest scan compared to inspiration-only examinations. Radiation doses to 28 organs were determined for 824 COPD patients undergoing routine chest examinations at three different CT systems-a conventional multi-slice CT (MSCT), a 2nd generation (2nd-DSCT), and 3rd generation dual-source CT (3rd-DSCT). Patients examined at the 3rd-DSCT received a paired inspiratory-expiratory scan. Organ doses, effective doses, and lifetime attributable cancer risks (LAR) were calculated. All organ and effective doses were significantly lower for the paired inspiratory-expiratory protocol (effective doses: 4.3 ± 1.5 mSv (MSCT), 3.0 ± 1.2 mSv (2nd-DSCT), and 2.0 ± 0.8 mSv (3rd-DSCT)). Accordingly, LAR was lowest for the paired protocol with an estimate of 0.025 % and 0.013% for female and male patients (50 years) respectively. Image quality was not compromised. Paired inspiratory-expiratory scans can be acquired on 3rd-DSCT systems at substantially lower dose and risk levels when compared to inspiration-only scans at conventional CT systems, offering promising prospects for improved COPD diagnosis.

From infancy to adulthood-Developmental changes in pulmonary quantitative computed tomography parameters

Purpose

Quantified computed tomography (qCT) is known for correlations with airflow obstruction and fibrotic changes of the lung. However, as qCT studies often focus on diseased and elderly subjects, current literature lacks physiological qCT values during body development. We evaluated chest CT examinations of a healthy cohort, reaching from infancy to adulthood, to determine physiological qCT values and changes during body development.

Method

Dose-optimized chest CT examinations performed over the last 3 years using a dual-source CT were retrospectively analysed. Exclusion criteria were age >30 years and any known or newly diagnosed lung pathology. Lung volume, mean lung density, full-width-at-half-maximum and low attenuated volume (LAV) were semi-automated quantified in 151 patients. qCT values between different age groups as well as unenhanced (Group 1) and contrast-enhanced (Group 2) protocols were compared. Models for projection of age-dependant changes in qCT values were fitted.

Results

Significant differences in qCT parameters were found between the age groups from 0 to 15 years (p < 0.05). All parameters except LAV merge into a plateau level above this age as shown by polynomial models (r2 between 0.85 and 0.67). In group 2, this plateau phase is shifted back around five years. Except for the volume, significant differences in all qCT values were found between group 1 and 2 (p < 0.01).

Conclusion

qCT parameters underly a specific age-dependant dynamic. Except for LAV, qCT parameters reach a plateau around adolescence. Contrast-enhanced protocols seem to shift this plateau backwards.

Predicting Pulmonary Function Testing from Quantified Computed Tomography Using Machine Learning Algorithms in Patients with COPD

Introduction: Quantitative computed tomography (qCT) is an emergent technique for diagnostics and research in patients with chronic obstructive pulmonary disease (COPD). qCT parameters demonstrate a correlation with pulmonary function tests and symptoms. However, qCT only provides anatomical, not functional, information. We evaluated five distinct, partial-machine learning-based mathematical models to predict lung function parameters from qCT values in comparison with pulmonary function tests. Methods: 75 patients with diagnosed COPD underwent body plethysmography and a dose-optimized qCT examination on a third-generation, dual-source CT with inspiration and expiration. Delta values (inspiration—expiration) were calculated afterwards. Four parameters were quantified: mean lung density, lung volume low-attenuated volume, and full width at half maximum. Five models were evaluated for best prediction: average prediction, median prediction, k-nearest neighbours (kNN), gradient boosting, and multilayer perceptron. Results: The lowest mean relative error (MRE) was calculated for the kNN model with 16%. Similar low MREs were found for polynomial regression as well as gradient boosting-based prediction. Other models led to higher MREs and thereby worse predictive performance. Beyond the sole MRE, distinct differences in prediction performance, dependent on the initial dataset (expiration, inspiration, delta), were found. Conclusion: Different, partially machine learning-based models allow the prediction of lung function values from static qCT parameters within a reasonable margin of error. Therefore, qCT parameters may contain more information than we currently utilize and can potentially augment standard functional lung testing.