Calculating air volume fractions from computed tomography images for chronic obstructive pulmonary disease diagnosis

Chuang, Chun Chao; Chou, Ying Hsiang; Peng, Shin‐Lei; Tai, Jou Erh; Lee, Shan-Chih; Tyan, Yeu Sheng; Shih, Chuen-Ming

doi:10.1371/journal.pone.0231730

Cited by 4 publications

(9 citation statements)

References 29 publications

(57 reference statements)

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“…Since the pathologic evaluation during the disease course of COVID-19 has not been established, computed tomography (CT) can reveal ground glass opacity and consolidation, which may reflect pathologic changes in these patients. Estimation of the volumetric quantification of chest CT images has been used in patients with various lung diseases including asthma, chronic obstructive pulmonary disease, interstitial lung disease, and oncological disease ( 9 10 11 ). Several studies have shown a potential role of chest CT volumetric quantification in predicting the mortality of ARDS ( 12 13 14 15 ).…”

Section: Introductionmentioning

confidence: 99%

Prognostic Implication of Volumetric Quantitative CT Analysis in Patients with COVID-19: A Multicenter Study in Daegu, Korea

Park

Lim

et al. 2020

Korean J Radiol

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Objective Lung segmentation using volumetric quantitative computed tomography (CT) analysis may help predict outcomes of patients with coronavirus disease (COVID-19). The aim of this study was to investigate the relationship between CT volumetric quantitative analysis and prognosis in patients with COVID-19. Materials and Methods CT images from patients diagnosed with COVID-19 from February 18 to April 15, 2020 were retrospectively analyzed. CT with a negative finding, failure of quantitative analysis, or poor image quality was excluded. CT volumetric quantitative analysis was performed by automated volumetric methods. Patients were stratified into two risk groups according to CURB-65: mild (score of 0–1) and severe (2–5) pneumonia. Outcomes were evaluated according to the critical event-free survival (CEFS). The critical events were defined as mechanical ventilator care, ICU admission, or death. Multivariable Cox proportional hazards analyses were used to evaluate the relationship between the variables and prognosis. Results Eighty-two patients (mean age, 63.1 ± 14.5 years; 42 females) were included. In the total cohort, male sex (hazard ratio [HR], 9.264; 95% confidence interval [CI], 2.021–42.457; p = 0.004), C-reactive protein (CRP) (HR, 1.080 per mg/dL; 95% CI, 1.010–1.156; p = 0.025), and COVID-affected lung proportion (CALP) (HR, 1.067 per percentage; 95% CI, 1.033–1.101; p < 0.001) were significantly associated with CEFS. CRP (HR, 1.164 per mg/dL; 95% CI, 1.006–1.347; p = 0.041) was independently associated with CEFS in the mild pneumonia group (n = 54). Normally aerated lung proportion (NALP) (HR, 0.872 per percentage; 95% CI, 0.794–0.957; p = 0.004) and NALP volume (NALPV) (HR, 1.002 per mL; 95% CI, 1.000–1.004; p = 0.019) were associated with a lower risk of critical events in the severe pneumonia group (n = 28). Conclusion CRP in the mild pneumonia group; NALP and NALPV in the severe pneumonia group; and sex, CRP, and CALP in the total cohort were independently associated with CEFS in patients with COVID-19.

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Section: Introductionmentioning

confidence: 99%

Prognostic Implication of Volumetric Quantitative CT Analysis in Patients with COVID-19: A Multicenter Study in Daegu, Korea

Park

Lim

et al. 2020

Korean J Radiol

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“…However, in reality, for air‐rich tissues such as lungs, their CT density values vary widely depending on air inspiratory and exhaustive phases 24 . The CT density value of the lung can be derived by considering the mixture of lung parenchyma and air in a voxel; 27 in the inspiratory phase, the proportion of air in the lung increases and the CT density value becomes lower, while in the exhaust phase, it becomes higher. Therefore, if the CT density value of air changes due to the pixel value truncation, the CT density value of lung parenchyma can be anticipated to be impacted even if the noise does not reach 100 HU.…”

Section: Discussionmentioning

confidence: 99%

Impact of pixel value truncation on image quality of low dose chest CT

et al. 2022

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Purpose In some noisy low dose CT lung cancer screening images, we noticed that the CT density values of air were increased and the visibility of emphysema was distinctly decreased. By examining histograms of these images, we found that the CT density values were truncated at −1024 HU. The purpose of this study was to investigate the effect of pixel value truncation on the visibility of emphysema using mathematical models. Methods and materials Assuming CT noise follows a normal distribution, we derived the relationship between the mean CT density value and the standard deviation (SD) when the pixel values below −1024 HU are truncated and replaced by −1024 HU. To validate our mathematical model, 20 untruncated phantom CT images were truncated by simulation, and the mean CT density values and SD of air in the images were measured and compared with the theoretical values. In addition, the mean CT density values and SD of air were measured in 100 cases of real clinical images obtained by GE, Siemens, and Philips scanners, respectively, and the agreement with the theoretical values was examined. Next, the contrast‐to‐noise ratio (CNR) between air (−1000 HU) and lung parenchyma (−850 HU) was derived from the mathematical model in the presence and absence of truncation as a measure of the visibility of emphysema. In addition, the radiation dose ratios required to obtain the same CNR in the case with and without truncation were also calculated. Results The mathematical model revealed that when the pixel values are truncated, the mean CT density values are proportional to the noise magnitude when the magnitude exceeds a certain level. The mean CT density values and SD measured in the images with pixel values truncated by simulation and in the real clinical images acquired by GE and Philips scanners agreed well with the theoretical values from our mathematical model. In the Siemens images, the measured and theoretical values agreed well when a portion of the truncated values were replaced by random values instead of simply replacing by −1024 HU. The CNR of air and lung parenchyma was lowered by truncating CT density values compared to that of no truncation. Furthermore, it was found that higher radiation dose was required to obtain the same CNR with truncation as without. As an example, when the noise SD was 60 HU, the radiation dose required for the GE and Philips truncation method was about 1.2 times higher than that without truncation, and that for the Siemens truncation method was about 1.4 times higher. Conclusions It was demonstrated mathematically that pixel value truncation causes a brightening of the mean CT density value and decreases the CNR of emphysema. Our results indicate that it is advisable to turn off truncation at −1024 HU, especially when scanning at low and ultra‐low radiation doses in the thorax.

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“…Muscle and blood can be used as the representative tissue. The ROIs for muscle and blood can be specifically defined around or between ribs and in the aorta or ventricle, respectively 79. Air fraction obtained by this method is a dimensionless parameter, representing the air content (range, 0 to 1).…”

Section: Recent Qct Methodsmentioning

confidence: 99%

“…Therefore, air fraction is expected to be less sensitive to differences in scanners as compared with density-based methods. Air factions of 90% and 98.5% are set as thresholds for defining AT and emphysema, respectively 13,79. Furthermore, considering the fraction threshold for AT and the subject-specific HU of air and tissue, we can simply obtain a subject-specific threshold to define AT regions.…”

Section: Recent Qct Methodsmentioning

confidence: 99%

“…They constructed a relative air volume histogram using air volume fractions to describe the air characteristics of the lung and define normal and damaged lung tissues. The results showed that the relative volume of normal and damaged lung tissues and relative damaged-to-normal volume ratio could be reliable biomarkers for differentiating COPD patients from the controls 79…”

Section: Recent Qct Methodsmentioning

confidence: 99%

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A Computer-based Analysis for Identification and Quantification of Small Airway Disease in Lung Computed Tomography Images

Mahdavi

Arabfard

Rafati

et al. 2022

Journal of Thoracic Imaging

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Computed tomography (CT) imaging is being increasingly used in clinical practice for detailed characterization of lung diseases. Respiratory diseases involve various components of the lung, including the small airways. Evaluation of small airway disease on CT images is challenging as the airways cannot be visualized directly by a CT scanner. Small airway disease can manifest as pulmonary air trapping (AT). Although AT may be sometimes seen as mosaic attenuation on expiratory CT images, it is difficult to identify diffuse AT visually. Computer technology advances over the past decades have provided methods for objective quantification of small airway disease on CT images. Quantitative CT (QCT) methods are being rapidly developed to quantify underlying lung diseases with greater precision than subjective visual assessment of CT images. A growing body of evidence suggests that QCT methods can be practical tools in the clinical setting to identify and quantify abnormal regions of the lung accurately and reproducibly. This review aimed to describe the available methods for the identification and quantification of small airway disease on CT images and to discuss the challenges of implementing QCT metrics in clinical care for patients with small airway disease.

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Calculating air volume fractions from computed tomography images for chronic obstructive pulmonary disease diagnosis

Cited by 4 publications

References 29 publications

Prognostic Implication of Volumetric Quantitative CT Analysis in Patients with COVID-19: A Multicenter Study in Daegu, Korea

Prognostic Implication of Volumetric Quantitative CT Analysis in Patients with COVID-19: A Multicenter Study in Daegu, Korea

Impact of pixel value truncation on image quality of low dose chest CT

A Computer-based Analysis for Identification and Quantification of Small Airway Disease in Lung Computed Tomography Images

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