Quantitative computed tomography (QCT) indices calculated on paired inspiratory/expiratory multidetector computer tomography (MDCT) can deliver valuable information about functional changes in airway diseases like cystic fibrosis (CF). Air trapping is an important early sign of CF which can only be quantified on expiratory CTs. An accurate lobe segmentation is needed for a regional analysis. Direct lobe segmentation (DLS) is more challenging to perform on expiratory CT images than on inspiratory images. We suggest a registration-based lobe segmentation (RLS) procedure for expiratory CTs if paired inspiratory/expiratory CTs are available. Firstly, our existing fully automated lobe segmentation algorithm was applied to the inspiratory images. Secondly, inspiratory and expiratory images were aligned by a deformable image registration algorithm. Thirdly, the calculated transformation between inspiratory and expiratory images was applied on the lobe segmentation determined on the inspiratory images. Finally, the transferred lobe segmentation was slightly adjusted to the expiratory CT. Validation of the procedure was performed on 128 paired inspiratory/expiratory CTs. The scans were acquired from 16 children with mild CF at 4 time points reconstructed with two different kernels. 6 lobes were segmented, the lingula was treated as separate lobe. We validated the registration-based lobe masks against manually corrected lobe masks. The mean spatial overlap (Dice Index) for DLS was 0.97±0.02 on the inspiratory CTs, and 0.82 ± 0.09 on expiratory CTs, determined in a previous study. In the present study the overlap was significantly improved for the expiratory CTs by the new RLS approach to 0.91 ± 0.05 (p < 2.2e − 16). This significant improvement brings the quality of lung lobe segmentation on expiratory CTs closer to the already very good lobe segmentation results on inspiratory CTs by DLS, thus reducing the need for manual post-processing.
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.