Mahalingam Neeraja,  Bates Alister,  Higano Nara,  Gunatilaka Chamindu,  Woods Jason,  Somasundaram Elanchezhian

Final Pr. ID: Poster #: SCI-010

To develop an optimized AI model to automatically segment lung volumes from pulmonary magnetic resonance images (MRI) and generate tidal volume calculations for neonatal patients with chronic lung disease of prematurity (bronchopulmonary dysplasia, BPD). Read More

Authors:  Mahalingam Neeraja , Bates Alister , Higano Nara , Gunatilaka Chamindu , Woods Jason , Somasundaram Elanchezhian

Keywords:  Bronchopulmonary Dysplasia, MRI, Deep Learning

Gunatilaka Chamindu,  Bates Alister,  Higano Nara,  Hahn Andrew,  Fain Sean,  Hysinger Erik,  Fleck Robert,  Woods Jason

Final Pr. ID: Paper #: 035

Tracheomalacia (TM) is a common morbidity associated with prematurity and manifests as dynamic collapse of the trachea lumen due to cyclic changes of intrathoracic pressure during breathing. Premature infants often have elevated work of breathing (WOB) related to their distal, small airway and lung abnormalities. The large airway contribution to WOB can be determined using computational fluid dynamics (CFD), which is a well-known technique to calculate the resistance and WOB in the human airway. However, previous studies are based on static airway geometry without motion. Using the novel technique of ultrashort echo time (UTE) magnetic resonance imaging (MRI), the tidal volume and airway motion can be used to create a dynamic model for use in CFD. Our aim is to calculate the estimated WOB in a dynamic trachea with neonatal TM compared with a stable, static trachea.
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Authors:  Gunatilaka Chamindu , Bates Alister , Higano Nara , Hahn Andrew , Fain Sean , Hysinger Erik , Fleck Robert , Woods Jason

Keywords:  Bronchopulmonary Dysplasia, MRI, Prematurity