CT Measurement of Lung Density

Kalef-Ezra, J.; Karantanas, A. H.; Tsekeris, P.

doi:10.3109/02841859909175564

Cited by 19 publications

(14 citation statements)

References 28 publications

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“…Posterior lung-tobackground ratio (as well as posterior signal-to-noise and contrast-to-noise ratios) was constantly higher than the anterior lung-to-background ratio (respectively anterior signal-tonoise and contrast-to-noise ratios) for all patients independent of age, as previously shown in the literature [34][35][36][37]. In addition, the lung-to-background ratio of the middle lung part appeared to be lower than the anterior lung-to-background ratio, agreeing with previous findings [38] that showed that the average density of the lung region near the thoracic wall was lower than the average density of the whole lung because the middle region-of-interest was placed near the thoracic wall. The linear regression fit for the two age groups, newborns to 2 years and older than 2 years, showed that the posterior intensity was higher than the anterior intensity.…”

Section: Discussionsupporting

confidence: 92%

Three-dimensional magnetic resonance imaging ultrashort echo-time cones for assessing lung density in pediatric patients

Zeimpekis

Geiger

Delso³

et al. 2020

Pediatr Radiol

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Background MRI of lung parenchyma is challenging because of the rapid decay of signal by susceptibility effects of aerated lung on routine fast spin-echo sequences. Objective To assess lung signal intensity in children on ultrashort echo-time sequences in comparison to a fast spin-echo technique. Materials and methods We conducted a retrospective study of lung MRI obtained in 30 patients (median age 5 years, range 2 months to 18 years) including 15 with normal lungs and 15 with cystic fibrosis. On a fast spin-echo sequence with radial readout and an ultrashort echo-time sequence, both lungs were segmented and signal intensities were extracted. We compared lung-to-background signal ratios and histogram analysis between the two patient cohorts using non-parametric tests and correlation analysis. Results On ultrashort echo-time the lung-to-background ratio was age-dependent, ranging from 3.15 to 1.33 with high negative correlation (R s = −0.86). Signal in posterior dependent portions of the lung was 18% and 11% higher than that of the anterior lung for age groups 0-2 and 2-18 years, respectively. The fast spin-echo sequence showed no variation of signal ratios by age or location, with a median of 0.99 (0.98-1.02). Histograms of ultrashort echo-time slices between controls and children with aggravated cystic fibrosis with mucus plugging and wall thickening exhibited significant discrepancies that differentiated between normal and pathological lungs. Conclusion Signal intensity of lung on ultrashort echo-time is higher than that on fast spin-echo sequences, is age-dependent and shows a gravity-dependent anterior to posterior gradient. This signal variation appears similar to lung density described on CT.

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Section: Discussionsupporting

confidence: 92%

Three-dimensional magnetic resonance imaging ultrashort echo-time cones for assessing lung density in pediatric patients

Zeimpekis

Geiger

Delso³

et al. 2020

Pediatr Radiol

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“…These opposing effects of smoking are not surprising, and are probably explained by smoking induced inflammation with increased movement of plasma and inflammatory cells from the circulation into the lung interstitium in the short-term and in the long-term remodelling and loss of lung tissue. The opposing effects of smoking on lung density may also explain the inconsistent results of previous cross-sectional studies [7][8][9]. In order to avoid large inter-individual variations, CT-measurements of participants who changed their smoking habits were excluded from the date of change.…”

Section: Discussionmentioning

confidence: 92%

“…Measurement of lung density by computed tomography (CT) is a surrogate marker for emphysema and reflects both disease severity [2][3][4] and physiological impairment [5][6]. Crosssectional studies of the influence of smoking on lung density by CT have reported inconsistent results, with some researchers finding no influence of smoking habit on lung density [7], while others report decreased lung density with increasing pack-yrs [8][9]. This is not surprising given the fact that smoking has opposing effects on lung density.…”

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confidence: 99%

Factors influencing the decline in lung density in a Danish lung cancer screening cohort

Shaker

Dirksen

et al. 2012

Eur Respir J

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Lung cancer screening trials provide an opportunity to study the natural history of emphysema by using computed tomography (CT) lung density as a surrogate parameter.In the Danish Lung Cancer Screening Trial, 2,052 participants were included. At screening rounds, smoking habits were recorded and spirometry was performed. CT lung density was measured as the volume-adjusted 15th percentile density (PD15). A mixed effects model was used with former smoking males with ,30 pack-yrs and without airflow obstruction (AFO) at entry as a reference group.At study entry, 893 (44%) participants had AFO. For the reference group, PD15 was 72.6 g?L -1

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“…These methods work well for relatively homogeneous lesions, but show the need for intensive user correction for inhomogeneous lesions. For example, most stage I and stage II lung tumours present as homogenous, high-intensity lesions on a background of low-intensity lung parenchyma [ 48 , 49 ] and, therefore, can be automatically segmented with high reproducibility and accuracy. However, for partially solid, ground-glass opacities, nodules attached to vessels and to the pleural surface, automatic segmentation is burdened by low reproducibility [ 50 ].…”

Section: Impact Of Image Segmentationmentioning

confidence: 99%

Radiomics: the facts and the challenges of image analysis

et al. 2018

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Radiomics is an emerging translational field of research aiming to extract mineable high-dimensional data from clinical images. The radiomic process can be divided into distinct steps with definable inputs and outputs, such as image acquisition and reconstruction, image segmentation, features extraction and qualification, analysis, and model building. Each step needs careful evaluation for the construction of robust and reliable models to be transferred into clinical practice for the purposes of prognosis, non-invasive disease tracking, and evaluation of disease response to treatment. After the definition of texture parameters (shape features; first-, second-, and higher-order features), we briefly discuss the origin of the term radiomics and the methods for selecting the parameters useful for a radiomic approach, including cluster analysis, principal component analysis, random forest, neural network, linear/logistic regression, and other. Reproducibility and clinical value of parameters should be firstly tested with internal cross-validation and then validated on independent external cohorts. This article summarises the major issues regarding this multi-step process, focussing in particular on challenges of the extraction of radiomic features from data sets provided by computed tomography, positron emission tomography, and magnetic resonance imaging.

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CT Measurement of Lung Density

Abstract: Quantitative CT provided the means to study noninvasively the electron density of the lung and the factors that influence this value.

Cited by 19 publications

References 28 publications

Three-dimensional magnetic resonance imaging ultrashort echo-time cones for assessing lung density in pediatric patients

Three-dimensional magnetic resonance imaging ultrashort echo-time cones for assessing lung density in pediatric patients

Factors influencing the decline in lung density in a Danish lung cancer screening cohort

Radiomics: the facts and the challenges of image analysis

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