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Reproducibility of airway wall thickness measurements

 
: Schmidt, M.; Kuhnigk, J.-M.; Krass, S.; Owsijewitsch, M.; Hoop, B. de; Peitgen, H.-O.

:

Karssemeijer, N. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Medical Imaging 2010. Computer-Aided Diagnosis. Pt.1 : 16-18 February 2010, San Diego, California, United States
Bellingham, WA: SPIE, 2010 (Proceedings of SPIE 7624)
ISBN: 978-0-8194-8025-5
ISSN: 1605-7422
Paper 76241O
Medical Imaging Symposium <2010, San Diego/Calif.>
Englisch
Konferenzbeitrag
Fraunhofer MEVIS ()
airway; COPD; wall thickness; validation; reproducibility

Abstract
Airway remodeling and accompanying changes in wall thickness are known to be a major symptom of chronic obstructive pulmonary disease (COPD), associated with reduced lung function in diseased individuals. Further investigation of this disease as well as monitoring of disease progression and treatment effect demand for accurate and reproducible assessment of airway wall thickness in CT datasets. With wall thicknesses in the sub-millimeter range, this task remains challenging even with today's high resolution CT datasets. To provide accurate measurements, taking partial volume effects into account is mandatory. The Full-Width-at-Half-Maximum (FWHM) method has been shown to be inappropriate for small airways1,2 and several improved algorithms for objective quantification of airway wall thickness have been proposed.1-8 In this paper, we describe an algorithm based on a closed form solution proposed by Weinheimer et al.7 We locally estimate the lung density parameter required for the closed form solution to account for possible variations of parenchyma density between different lung regions, inspiration states and contrast agent concentrations. The general accuracy of the algorithm is evaluated using basic tubular software and hardware phantoms. Furthermore, we present results on the reproducibility of the algorithm with respect to clinical CT scans, varying reconstruction kernels, and repeated acquisitions, which is crucial for longitudinal observations.

: http://publica.fraunhofer.de/dokumente/N-135495.html