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Proceedings Paper

Emphysema quantification from CT scans using novel application of diaphragm curvature estimation: comparison with standard quantification methods and pulmonary function data
Author(s): Brad M. Keller; Anthony P. Reeves; David F. Yankelevitz; Claudia I. Henschke; R. Graham Barr
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Paper Abstract

Emphysema is a disease of the lungs that destroys the alveolar air sacs and induces long-term respiratory dysfunction. CT scans allow for the imaging of the anatomical basis of emphysema and quantification of the underlying disease state. Several measures have been introduced for the quantification emphysema directly from CT data; most,however, are based on the analysis of density information provided by the CT scans, which vary by scanner and can be hard to standardize across sites and time. Given that one of the anatomical variations associated with the progression of emphysema is the flatting of the diaphragm due to the loss of elasticity in the lung parenchyma, curvature analysis of the diaphragm would provide information about emphysema from CT. Therefore, we propose a new, non-density based measure of the curvature of the diaphragm that would allow for further quantification methods in a robust manner. To evaluate the new method, 24 whole-lung scans were analyzed using the ratios of the lung height and diaphragm width to diaphragm height as curvature estimates as well as using the emphysema index as comparison. Pearson correlation coefficients showed a strong trend of several of the proposed diaphragm curvature measures to have higher correlations, of up to r=0.57, with DLCO% and VA than did the emphysema index. Furthermore, we found emphysema index to have only a 0.27 correlation to the proposed measures, indicating that the proposed measures evaluate different aspects of the disease.

Paper Details

Date Published: 27 February 2009
PDF: 9 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 726032 (27 February 2009); doi: 10.1117/12.812247
Show Author Affiliations
Brad M. Keller, Cornell Univ. (United States)
Anthony P. Reeves, Cornell Univ. (United States)
David F. Yankelevitz, Cornell Univ. (United States)
Claudia I. Henschke, Weill Medical College, Cornell Univ. (United States)
R. Graham Barr, Columbia Univ. (United States)


Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)

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