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Updated: Jan 12, 2026

Four-Dimensional Computed Tomography-Guided Valve Sizing for Transcatheter Pulmonary Valve Replacement
Published on: January 20, 2022
4D-computed tomography in the evaluation of dynamic tracheal obstruction in children: A case series
Cameron P Worden1, Elena Quinonez Del Cid1, Yining Jiao1
1University of North Carolina School of Medicine, 321 S Columbia St, Chapel Hill, NC, 27599, USA.
Introduction:
In this study we aim to prove we can compare 4D CT findings in children with clinically suspected tracheomalacia to a normalized 3D Pediatric Airway Atlas to evaluate for potentially clinically useful diagnostic metrics.
Methods:
This retrospective analysis included patients from the ongoing DYMOSA study who underwent 4D CT imaging for clinically suspected tracheomalacia. Tracheal 4D CT images were analyzed and compared to a weight-matched Pediatric Airway Atlas of children with radiographically normal airways. Key metrics included minimum airway cross sectional area, Airway Percentage Collapse, zMin-cross sectional area, zMax-cross sectional area, and Delta z.
Results:
Five male children (mean age: 65 months, mean weight: 26.0 kg) were included. Three were diagnosed with tracheomalacia via bronchoscopy, while two were diagnosed with no tracheomalacia. A difference in Airway Percentage Collapse between non-tracheomalacia and tracheomalacia cases was noted when comparing their mean percentages: 25 % for non-tracheomalacia versus 61 % for tracheomalacia cases. Utilizing the Pediatric Airway Atlas for comparison, mean zMin-cross sectional area for the tracheomalacia cases was lower for those without tracheomalacia (-2.5 versus -1.1). zMax-cross sectional area was also lower for those with tracheomalacia versus for those without tracheomalacia (-1.79 vs 0.4).
Conclusion:
Results of this study show potential utility of novel metrics derived from non-invasive 4D CT imaging in the evaluation of children with suspected tracheomalacia. Future work is needed to evaluate these metrics on larger blinded datasets, to investigate the potential for these metrics to discriminate patients with tracheomalacia from those without tracheomalacia, and to stratify disease severity.
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