Quantitative Analysis for Computed Tomography Findings of Various Diffuse Lung Diseases Using Volume Histogram Analysis

Abstract

: Volume histogram analysis is a promising method for the evaluation of diffuse lung diseases and the effectiveness of treatment.

Extrapolation from ten sections can make CT-based quantification of lung aeration more practicable

“…Software developed for automatic segmentation of CT images of pigs with experimental lung injury awaits validation for the use in patients or other research animals [35,36]. Automatic segmentation using thresholds around -250 HU is also no alternative to the manual processing of images with opacifications because the pathophysiologically most important nonaerated lung compartment with densities between -100 and ?100 HU would a priori be excluded from analysis [6,15,26,34]. Therefore, although manual segmentation is highly inefficient, it is often inevitable.…”

“…Quantitative computed tomography-based (qCT) assessment of lung aeration and its alterations (e.g., consolidation or hyperaeration) can help to assess and monitor severity and course of pulmonary disease [1][2][3][4][5][6][7][8][9], to clarify the etiology and pathophysiology of acute respiratory failure [3,[10][11][12][13], to individualize mechanical ventilation [4,5,11,12,[14][15][16], or to identify patients at risk of developing complications such as ventilatorassociated lung injury [3,5,11,16,17].…”

Computer-aided diagnosis of lung cancer: definition and detection of ground-glass opacity type of nodules by high-resolution computed tomography

“…However, to detect the GGO area more accurately, other quantitative parameters such as variance or entropy might be necessary. 27 In the present study, selected pulmonary nodules were nonconsecutive. The fact that a chest radiologist selected 100 nodules could possibly have biased the data.…”

Automated Quantification of High-Resolution CT Scan Findings in Individuals at Risk for Pulmonary Fibrosis

“…[4][5][6][7][8][9] Given the role of HRCT scans in evaluating people with lung disease, programs providing automated readings to facilitate the diagnosis and quantifi cation of parenchymal lung diseases have been developed. [10][11][12][13][14][15] However, studies focusing on ILDs have been limited by small numbers of subjects, analysis of scans performed in a supine position, and lack of independent validation of computer program fi ndings. Most studies have included cohorts with symptomatic disease; a paucity of information is available regarding use of automated computer readings of HRCT scans to identify early radiographic abnormalities in populations at risk for pulmonary fi brosis.…”