Normal spectrum of pulmonary parametric response map to differentiate lung collapsibility: distribution of densitometric classifications in healthy adult volunteers

Silva, Mario; Nemec, Stefan F.; Dufresne, Valérie; Occhipinti, Mariaelena; Heidinger, Benedikt H.; Chamberlain, Ryan; Bankier, Alexander A.

doi:10.1007/s00330-015-4133-1

Cited by 11 publications

(12 citation statements)

References 17 publications

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“…Alternatively, based on the present data that the range of E/I-LV in smokers with ECAC was 0.40 to 0.74, E/I-LV ≥0.75 was used to identify smokers with air-trapping. Since E/I-LV <0.6 was used as the threshold of sufficient expiration during CT scan in healthy subjects in a previous study [37], the cut-off value of 0.75 in this study might be higher than the normal range of E/I-EV. Finally, many smokers were male.…”

Section: Discussionmentioning

confidence: 95%

Expiratory central airway collapse and symptoms in smokers

Tanabe

Terada²,

Shima

et al. 2021

Respiratory Investigation

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Section: Discussionmentioning

confidence: 95%

Expiratory central airway collapse and symptoms in smokers

Tanabe

Terada²,

Shima

et al. 2021

Respiratory Investigation

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“…Any voxel with attenuation values <−950 HU in inspiratory CT and <−856 HU in expiratory CT represents emphysema (PRM Emph ). All voxels with attenuation values >−950 HU in inspiratory CT and <−856 HU in expiratory CT represent AT as functional regions of small airway disease (PRM fSAD ), and all voxels above both thresholds in the 2 scans represent normal lung regions (PRM Norml ) 56–59. The PRM technique is shown in Figure 3.…”

Section: Recent Qct Methodsmentioning

confidence: 99%

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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“…86 Nonetheless, it should be realized that there is a normal range of air trapping even in young healthy subjects, which should be acknowledged for clinical application of QCT. 87,88…”

Section: Obstructive Pulmonary Diseasementioning

confidence: 99%

Pulmonary quantitative CT imaging in focal and diffuse disease: current research and clinical applications

Silva

Milanese

Seletti

et al. 2018

The British Journal of Radiology

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The frenetic development of imaging technology-both hardware and software-provides exceptional potential for investigation of the lung. In the last two decades, CT was exploited for detailed characterization of pulmonary structures and description of respiratory disease. The introduction of volumetric acquisition allowed increasingly sophisticated analysis of CT data by means of computerized algorithm, namely quantitative CT (QCT). Hundreds of thousands of CTs have been analysed for characterization of focal and diffuse disease of the lung. Several QCT metrics were developed and tested against clinical, functional and prognostic descriptors. Computer-aided detection of nodules, textural analysis of focal lesions, densitometric analysis and airway segmentation in obstructive pulmonary disease and textural analysis in interstitial lung disease are the major chapters of this discipline. The validation of QCT metrics for specific clinical and investigational needs prompted the translation of such metrics from research field to patient care. The present review summarizes the state of the art of QCT in both focal and diffuse lung disease, including a dedicated discussion about application of QCT metrics as parameters for clinical care and outcomes in clinical trials.

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Normal spectrum of pulmonary parametric response map to differentiate lung collapsibility: distribution of densitometric classifications in healthy adult volunteers

Cited by 11 publications

References 17 publications

Expiratory central airway collapse and symptoms in smokers

Expiratory central airway collapse and symptoms in smokers

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

Pulmonary quantitative CT imaging in focal and diffuse disease: current research and clinical applications

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