Tapering analysis of airways with bronchiectasis

Kin Quan; Rebecca J. Shipley; Ryutaro Tanno; Graeme McPhillips; Vasileios Vavourakis; David Edwards; Joseph Jacob; John R. Hurst; David J. Hawkes

doi:10.1117/12.2292306

2 March 2018 Tapering analysis of airways with bronchiectasis

Kin Quan, Rebecca J. Shipley, Ryutaro Tanno, Graeme McPhillips, Vasileios Vavourakis, David Edwards, Joseph Jacob, John R. Hurst, David J. Hawkes

Author Affiliations +

Proceedings Volume 10574, Medical Imaging 2018: Image Processing; 105742G (2018) https://doi.org/10.1117/12.2292306
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Bronchiectasis is the permanent dilation of airways. Patients with the disease can suffer recurrent exacerbations, reducing their quality of life. The gold standard to diagnose and monitor bronchiectasis is accomplished by inspection of chest computed tomography (CT) scans. A clinician examines the broncho-arterial ratio to determine if an airway is brochiectatic. The visual analysis assumes the blood vessel diameter remains constant, although this assumption is disputed in the literature. We propose a simple measurement of tapering along the airways to diagnose and monitor bronchiectasis. To this end, we constructed a pipeline to measure the cross-sectional area along the airways at contiguous intervals, starting from the carina to the most distal point observable. Using a phantom with calibrated 3D printed structures, the precision and accuracy of our algorithm extends to the sub voxel level. The tapering measurement is robust to bifurcations along the airway and was applied to chest CT images acquired in clinical practice. The result is a statistical difference in tapering rate between airways with bronchiectasis and controls.

Citation Download Citation

Kin Quan, Rebecca J. Shipley, Ryutaro Tanno, Graeme McPhillips, Vasileios Vavourakis, David Edwards, Joseph Jacob, John R. Hurst, and David J. Hawkes "Tapering analysis of airways with bronchiectasis", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105742G (2 March 2018); https://doi.org/10.1117/12.2292306

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