High resolution multidetector CT aided tissue analysis and quantification of lung fibrosis

Abstract

Rational and Objectives-Volumetric high-resolution scans can be acquired of the lungs with multi-detector CT (MDCT). Such scans have potential to facilitate useful visualization, characterization, and quantification of the extent of diffuse lung diseases, such as Usual Interstitial Pneumonitis or Idiopathic Pulmonary Fibrosis (UIP/IPF). There is a need to objectify, standardize and improve the accuracy and repeatability of pulmonary disease characterization and quantification from such scans. This paper presents a novel texture analysis approach toward classification and quantification of various pathologies present in lungs with UIP/IPF. The approach integrates a texture matching method with histogram feature analysis.

Automated quantification of radiological patterns predicts survival in idiopathic pulmonary fibrosis

“…First, the CALIPER technology was developed based on ''lung pattern signatures'' derived from standardised CT analyses and acquisition protocols used in the LTRC database [9]. Scan parameters used for CT in this retrospective study were different to those used in the LTRC database and were not always identical for the scans at the two time-points.…”

“…Unfortunately, assessment of fibrosis volumes by expert radiologists has been hampered by substantial intra-and interobserver variability, and quantitative CT indices using fractal analysis and global histogram-based methods have not been validated or found helpful in clinical practice thus far [1,4,[19][20][21][22][23][24]. CALIPER is based on a texture-sensitive volumetric analysis that allows automated classification of lung parenchyma according to a database of HRCT volumes of interest validated by radiologists using data from the LTRC [9]. The majority of existing expert systems and associated quantitative tools depend on strictly controlled image acquisition protocols to provide consistent results.…”

“…The detection and quantification of pulmonary parenchyma by CALIPER is based on histogram signature mapping techniques trained through expert radiologist consensus assessment of pathologically confirmed training sets obtained through the Lung Tissue Research Consortium (LTRC) [9]. We hypothesised that this novel computer-aided method of analysing pulmonary parenchymal features on chest HRCT would provide a reproducible and accurate assessment of disease that may correlate with the semi-quantitative assessment of radiologists.…”

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.…”

Appreciating the shades of gray: a case for Computer-Aided Nodule Assessment and Risk Yield (CANARY)-based risk stratification of lung adenocarcinomas

“…We strongly encourage all to read our articles to understand if not appreciate the nuances and robustness of the algorithmic components of CANARY. From a pragmatic standpoint, however, this may be a moot point as the performance of CANARY does not appear to be affected by these technical considerations (6,(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22).…”