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Quantitative Computed Tomography Imaging of Interstitial Lung Diseases

Authors :
Fabien Maldonado
Teng Moua
Sushravya Raghunath
Ronald A. Karwoski
Richard A. Robb
Brian J. Bartholmai
Srinivasan Rajagopalan
Paul A. Decker
Source :
Journal of Thoracic Imaging. 28:298-307
Publication Year :
2013
Publisher :
Ovid Technologies (Wolters Kluwer Health), 2013.

Abstract

High-resolution chest computed tomography (HRCT) is essential in the characterization of interstitial lung disease. The HRCT features of some diseases can be diagnostic. Longitudinal monitoring with HRCT can assess progression of interstitial lung disease; however, subtle changes in the volume and character of abnormalities can be difficult to assess. Accuracy of diagnosis can be dependent on expertise and experience of the radiologist, pathologist, or clinician. Quantitative analysis of thoracic HRCT has the potential to determine the extent of disease reproducibly, classify the types of abnormalities, and automate the diagnostic process.Novel software that utilizes histogram signatures to characterize pulmonary parenchyma was used to analyze chest HRCT data, including retrospective processing of clinical CT scans and research data from the Lung Tissue Research Consortium. Additional information including physiological, pathologic, and semiquantitative radiologist assessment was available to allow comparison of quantitative results, with visual estimates of the disease, physiological parameters, and measures of disease outcome.Quantitative analysis results were provided in regional volumetric quantities for statistical analysis and a graphical representation. These results suggest that quantitative HRCT analysis can serve as a biomarker with physiological, pathologic, and prognostic significance.It is likely that quantitative analysis of HRCT can be used in clinical practice as a means to aid in identifying a probable diagnosis, stratifying prognosis in early disease, and consistently determining progression of the disease or response to therapy. Further optimization of quantitative techniques and longitudinal analysis of well-characterized subjects would be helpful in validating these methods.

Details

ISSN :
08835993
Volume :
28
Database :
OpenAIRE
Journal :
Journal of Thoracic Imaging
Accession number :
edsair.doi.dedup.....ccacb93ec8055747683b3d6bc2894a74
Full Text :
https://doi.org/10.1097/rti.0b013e3182a21969