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Proceedings Paper

An evaluation of automated broncho-arterial ratios for reliable assessment of bronchiectasis
Author(s): Benjamin L. Odry; Atilla P. Kiraly; Carol L. Novak; David P. Naidich; Jean-Francois Lerallut
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Paper Abstract

Bronchiectasis, the permanent dilatation of the airways, is frequently evaluated by computed tomography (CT) in order to determine disease progression and response to treatment. Normal airways have diameters of approximately the same size as their accompanying artery, and most scoring systems for quantifying bronchiectasis severity ask physicians to estimate the broncho-arterial ratio. However, the lack of standardization coupled with inter-observer variability limits diagnostic sensitivity and the ability to make reliable comparisons with follow-up CT studies. We have developed a Computer Aided Diagnosis method to detect airway disease by locating abnormal broncho-arterial ratios. Our approach is based on computing a tree model of the airways followed by automated measurements of broncho-arterial ratios at peripheral airway locations. The artery accompanying a given bronchus is automatically determined by correlation of its orientation and proximity to the airway, while the diameter measurements are based on the full-width half maximum method. This method was previously evaluated subjectively; in this work we quantitatively evaluate the airway and vessel measurements on 9 CT studies and compare the results with three independent readers. The automatically selected artery location was in agreement with the readers in 75.3% of the cases compared with 65.6% agreement of the readers with each other. The reader-computer variability in lumen diameters (7%) was slightly lower than that of the readers with respect to each other (9%), whereas the reader-computer variability in artery diameter (18%) was twice that of the readers (8%), but still acceptable for detecting disease. We conclude that the automatic system has comparable accuracy to that of readers, while providing greater speed and consistency.

Paper Details

Date Published: 17 March 2008
PDF: 9 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69152M (17 March 2008); doi: 10.1117/12.772579
Show Author Affiliations
Benjamin L. Odry, Siemens Corporate Research, Inc. (United States)
Atilla P. Kiraly, Siemens Corporate Research, Inc. (United States)
Carol L. Novak, Siemens Corporate Research, Inc. (United States)
David P. Naidich, New York Univ. Medical Ctr. (United States)
Jean-Francois Lerallut, Univ. de Technologie de Compiègne (France)

Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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