| Popis: |
Bronchiectasis, one of the most neglected chronic lung conditions, has a high individual disease burden and economic cost and causes poor quality of life in children/adolescents and adults. Advances in image quality and a dramatic reduction in acquisition times, multiple high-resolution chest tomography (HRCT) acquisitions, and reconstructions of the lung have resulted in accurate categorization and determination of the extent of lung parenchyma and airway abnormality. For bronchiectasis, the diagnosis is confirmed using the key feature of abnormally increased broncho-arterial (B.A.) ratio (BAR), with or without other abnormalities, e.g., bronchial wall thickening, lack of bronchial tapering, and mucus plugging. Most of these features require shape analysis of the airway and artery regions to perform various assessments that can have inter-rater variability and are time-consuming. This challenge is amplified in pediatric patients due to age-related anatomical variations. The anatomical differences and variations in airway structures between Infants, Early Childhood, and middle Childhood can impact how the images can be processed and analyzed. To address this, we proposed two novel image-processing methods to detect and measure the B.A. pairs. The first method uses an optimized connected component labelling (CCL) algorithm to construct bounding boxes around the objects (airway, artery) and extract the regions of interest (ROIs) for potential B.A. pairs. The second method allows us to calculate 4 or 6 diameters for each object in the ROIs and use their mean value as the final diameter, demonstrating agreement with manual readings. Evaluating against a diverse set of HRCT scans from various categories validates the significance and practical utility of our proposed methods in detecting and measuring the disjointed B.A. pairs to assess increased BAR. |
