Patient-Specific Computational Simulations of Hyperpolarized ^3He MRI Ventilation Defects in Healthy and Asthmatic Subjects

• Published in: IEEE transactions on biomedical engineering 2019-05, Vol.66 (5), p.1318-1327
• Main Authors: Oakes, Jessica M., Mummy, David G., Poorbahrami, Kamran, Zha, Wei, Fain, Sean B.
• Format: Article
• Language: English
• Subjects: Atmospheric modeling; Computational fluid dynamics (CFD); Computational modeling; Computed tomography; Lung; Magnetic resonance imaging; multi-scale modeling; resistance; Ventilation
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2018.2872845

Combined, medical imaging data and respiratory computer simulations may facilitate novel insight into pulmonary disease phenotypes, including the structure/function relationships within the airways. This integration may ultimately enable improved classification and treatment of asthma. Severe asthma (15% of asthmatics) is particularly challenging to treat, as these patients do not respond well to inhaled therapeutics. Methods: This study combines medical image data with patient-specific computational models to predict gas distributions and airway mechanics in healthy and asthmatic subjects. We achieve this by integrating segmental volume defect percent (SVDP), measured from hyperpolarized 3 He MRI and computed tomography images, to create models of patient-specific gas flow within the conducting airways. Predicted and measured SVDP distributions are achieved when the prescribed resistances are increased systematically. Results: Because of differences in airway morphology and regional function, airway resistances and flow structures varied between the asthmatic subjects. Specifically, while mean SVDP was similar between the severe asthmatics (4.30 ± 5.22 versus 3.54 ± 5.98%), one subject exhibited abnormal flow structures, high near wall flow gradients, and enhanced conducting airway resistances (17.3E-3 versus 1.1E-3 cmH 2 O-s/mL) in comparison to the other severe asthmatic subject. Conclusion: By coupling medical imaging data with computer simulations, we provide detailed insight into pathological flow characteristics and airway mechanics in asthmatics, beyond what could be inferred independently.