24 February 2017 Fully automated lobe-based airway taper index calculation in a low dose MDCT CF study over 4 time-points
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Abstract
Cystic Fibrosis (CF) results in severe bronchiectasis in nearly all cases. Bronchiectasis is a disease where parts of the airways are permanently dilated. The development and the progression of bronchiectasis is not evenly distributed over the entire lungs – rather, individual functional units are affected differently. We developed a fully automated method for the precise calculation of lobe-based airway taper indices. To calculate taper indices, some preparatory algorithms are needed. The airway tree is segmented, skeletonized and transformed to a rooted acyclic graph. This graph is used to label the airways. Then a modified version of the previously validated integral based method (IBM) for airway geometry determination is utilized. The rooted graph, the airway lumen and wall information are then used to calculate the airway taper indices. Using a computer-generated phantom simulating 10 cross sections of airways we present results showing a high accuracy of the modified IBM. The new taper index calculation method was applied to 144 volumetric inspiratory low-dose MDCT scans. The scans were acquired from 36 children with mild CF at 4 time-points (baseline, 3 month, 1 year, 2 years). We found a moderate correlation with the visual lobar Brody bronchiectasis scores by three raters (r2 = 0.36, p < .0001). The taper index has the potential to be a precise imaging biomarker but further improvements are needed. In combination with other imaging biomarkers, taper index calculation can be an important tool for monitoring the progression and the individual treatment of patients with bronchiectasis.
Conference Presentation
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Oliver Weinheimer, Mark O. Wielpütz, Philip Konietzke, Claus P. Heussel, Hans-Ulrich Kauczor, Christoph Brochhausen, David Hollemann, Dasha Savage, Craig J. Galbán, Terry E. Robinson, "Fully automated lobe-based airway taper index calculation in a low dose MDCT CF study over 4 time-points", Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101330U (24 February 2017); doi: 10.1117/12.2254387; https://doi.org/10.1117/12.2254387
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