AI-Driven Model for Automatic Emphysema Detection in Low-Dose Computed Tomography Using Disease-Specific Augmentation

The objective of this study is to evaluate the feasibility of a disease-specific deep learning (DL) model based on minimum intensity projection (minIP) for automated emphysema detection in low-dose computed tomography (LDCT) scans. LDCT scans of 240 individuals from a population-based cohort in the Netherlands (ImaLife study, mean age ± SD = 57 ± 6 years) were retrospectively chosen for training and internal validation of the DL model. For independent testing, LDCT scans of 125 individuals from a lung cancer screening cohort in the USA (NLST study, mean age ± SD = 64 ± 5 years) were used. Dichotomous emphysema diagnosis based on radiologists' annotation was used to develop the model. The automated model included minIP processing (slab thickness range: 1 mm to 11 mm), classification, and detection maps generation. The data-split for the pipeline evaluation involved class-balanced and imbalanced settings. The proposed DL pipeline showed the highest performance (area under receiver operating characteristics curve) for 11 mm slab thickness in both the balanced (ImaLife = 0.90 ± 0.05) and the imbalanced dataset (NLST = 0.77 ± 0.06). For ImaLife subcohort, the variation in minIP slab thickness from 1 to 11 mm increased the DL model's sensitivity from 75 to 88% and decreased the number of false-negative predictions from 10 to 5. The minIP-based DL model can automatically detect emphysema in LDCTs. The performance of thicker minIP slabs was better than that of thinner slabs. LDCT can be leveraged for emphysema detection by applying disease specific augmentation

Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means.

International audienceThe x-ray exposure to patients has become a major concern in computed tomography (CT) and minimizing the radiation exposure has been one of the major efforts in the CT field. Due to plenty high-attenuation tissues in the human chest, under low-dose scan protocols, thoracic low-dose CT (LDCT) images tend to be severely degraded by excessive mottled noise and non-stationary streak artifacts. Their removal is rather a challenging task because the streak artifacts with directional prominence are often hard to discriminate from the attenuation information of normal tissues. This paper describes a two-step processing scheme called 'artifact suppressed large-scale nonlocal means' for suppressing both noise and artifacts in thoracic LDCT images. Specific scale and direction properties were exploited to discriminate the noise and artifacts from image structures. Parallel implementation has been introduced to speed up the whole processing by more than 100 times. Phantom and patient CT images were both acquired for evaluation purpose. Comparative qualitative and quantitative analyses were both performed that allows conclusion on the efficacy of our method in improving thoracic LDCT data

The relationship between lung function impairment and quantitative computed tomography in chronic obstructive pulmonary disease

Contains fulltext : 109919.pdf (Publisher’s version ) (Open Access)OBJECTIVES: To determine the relationship between lung function impairment and quantitative computed tomography (CT) measurements of air trapping and emphysema in a population of current and former heavy smokers with and without airflow limitation. METHODS: In 248 subjects (50 normal smokers; 50 mild obstruction; 50 moderate obstruction; 50 severe obstruction; 48 very severe obstruction) CT emphysema and CT air trapping were quantified on paired inspiratory and end-expiratory CT examinations using several available quantification methods. CT measurements were related to lung function (FEV(1), FEV(1)/FVC, RV/TLC, Kco) by univariate and multivariate linear regression analysis. RESULTS: Quantitative CT measurements of emphysema and air trapping were strongly correlated to airflow limitation (univariate r-squared up to 0.72, p < 0.001). In multivariate analysis, the combination of CT emphysema and CT air trapping explained 68-83% of the variability in airflow limitation in subjects covering the total range of airflow limitation (p < 0.001). CONCLUSIONS: The combination of quantitative CT air trapping and emphysema measurements is strongly associated with lung function impairment in current and former heavy smokers with a wide range of airflow limitation.01 januari 201

Derivation of a test statistic for emphysema quantification

Density masking is the de-facto quantitative imaging phenotype for emphysema that is widely used by the clinical community. Density masking defines the burden of emphysema by a fixed threshold, usually between -910 HU and -950 HU, that has been experimentally validated with histology. In this work, we formalized emphysema quantification by means of statistical inference. We show that a non-central Gamma is a good approximation for the local distribution of image intensities for normal and emphysema tissue. We then propose a test statistic in terms of the sample mean of a truncated noncentral Gamma random variable. Our results show that this approach is well-suited for the detection of emphysema and superior to standard density masking. The statistical method was tested in a dataset of 1337 samples obtained from 9 different scanner models in subjects with COPD. Results showed an increase of 17% when compared to the density masking approach, and an overall accuracy of 94.09%

Extracting Tree-structures in CT data by Tracking Multiple Statistically Ranked Hypotheses

In this work, we adapt a method based on multiple hypothesis tracking (MHT) that has been shown to give state-of-the-art vessel segmentation results in interactive settings, for the purpose of extracting trees. Regularly spaced tubular templates are fit to image data forming local hypotheses. These local hypotheses are used to construct the MHT tree, which is then traversed to make segmentation decisions. However, some critical parameters in this method are scale-dependent and have an adverse effect when tracking structures of varying dimensions. We propose to use statistical ranking of local hypotheses in constructing the MHT tree, which yields a probabilistic interpretation of scores across scales and helps alleviate the scale-dependence of MHT parameters. This enables our method to track trees starting from a single seed point. Our method is evaluated on chest CT data to extract airway trees and coronary arteries. In both cases, we show that our method performs significantly better than the original MHT method.Comment: Accepted for publication at the International Journal of Medical Physics and Practic

Distribution of emphysema in heavy smokers: Impact on pulmonary function

SummaryPurposeTo investigate impact of distribution of computed tomography (CT) emphysema on severity of airflow limitation and gas exchange impairment in current and former heavy smokers participating in a lung cancer screening trial.Materials and MethodsIn total 875 current and former heavy smokers underwent baseline low-dose CT (30mAs) in our center and spirometry and diffusion capacity testing on the same day as part of the Dutch–Belgian Lung Cancer Screening Trial (NELSON). Emphysema was quantified for 872 subjects as the number of voxels with an apparent lowered X-ray attenuation coefficient. Voxels attenuated <−950HU were categorized as representing severe emphysema (ES950), while voxels attenuated between −910HU and −950HU represented moderate emphysema (ES910). Impact of distribution on severity of pulmonary function impairment was investigated with logistic regression, adjusted for total amount of emphysema.ResultsFor ES910 an apical distribution was associated with more airflow obstruction and gas exchange impairment than a basal distribution (both p<0.01). The FEV1/FVC ratio was 1.6% (95% CI 0.42% to 2.8%) lower for apical predominance than for basal predominance, for Tlco/VA the difference was 0.12% (95% CI 0.076–0.15%). Distribution of ES950 had no impact on FEV1/FVC ratio, while an apical distribution was associated with a 0.076% (95% CI 0.038–0.11%) lower Tlco/VA (p<0.001).ConclusionIn a heavy smoking population, an apical distribution is associated with more severe gas exchange impairment than a basal distribution; for moderate emphysema it is also associated with a lower FEV1/FVC ratio. However, differences are small, and likely clinically irrelevant

Quantification of Pulmonary Ventilation using Hyperpolarized 3He Magnetic Resonance Imaging

Smoking-related lung diseases including chronic obstructive pulmonary disease (COPD) and lung cancer are projected to have claimed the lives of more than 30,000 Canadians in 2010. The poor prognosis and lack of new treatment options for lung diseases associated with smoking are largely due to the inadequacy of current techniques for evaluating lung function. Hyperpolarized 3He magnetic resonance imaging (MRI) is a relatively new technique, and quantitative measurements derived from these images, specifically the ventilation defect volume (VDV) and ventilation defect percent (VDP) have the potential to provide new sensitive measures of lung function. Here, we evaluate the reproducibility of VDV, and explore the sensitivity of these measurements in healthy young and elderly volunteers, and subjects with smoking-related lung disease (COPD and radiation-induced lung injury (RILI)). Our results show that 3He MRI measurements of ventilation have high short-term reproducibility in both healthy volunteers and subjects with COPD. Additionally, we report that these measurements are sensitive to age-related changes in lung function. Finally, in RILI we show that measurements of lung function derived from 3He MRI are sensitive to longitudinal changes in lung function following treatment, while in COPD we report that using VDP in conjunction with structural measurements of disease (using the apparent diffusion coefficient (ADC) derived from diffusion-weighted images) may provide a new method for phenotyping this smoking-related lung disease