Journal article
Patient-Specific Computational Simulations of Hyperpolarized He-3 MRI Ventilation Defects in Healthy and Asthmatic Subjects
IEEE transactions on biomedical engineering, Vol.66(5), pp.1318-1327
05/01/2019
DOI: 10.1109/TBME.2018.2872845
PMID: 30281426
Abstract
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 3He 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.
Details
- Title: Subtitle
- Patient-Specific Computational Simulations of Hyperpolarized He-3 MRI Ventilation Defects in Healthy and Asthmatic Subjects
- Creators
- Jessica M. Oakes - Northeastern UniversityDavid G. Mummy - University of Wisconsin–MadisonKamran Poorbahrami - Northeastern UniversityWei Zha - University of Wisconsin–MadisonSean B. Fain - University of Wisconsin–Madison
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on biomedical engineering, Vol.66(5), pp.1318-1327
- Publisher
- IEEE
- DOI
- 10.1109/TBME.2018.2872845
- PMID
- 30281426
- ISSN
- 0018-9294
- eISSN
- 1558-2531
- Number of pages
- 10
- Grant note
- R21HL140436 / NATIONAL HEART, LUNG, AND BLOOD INSTITUTE; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Heart Lung & Blood Institute (NHLBI) HL140436-01; NIH/NHLBI R01 HL069116; R01 HL080412; U10 HL109168; NIH/NCATS UL1TR000427 / Severe Asthma Research Program R21 HL140436-01 / NIH/NHLBI; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Heart Lung & Blood Institute (NHLBI)
- Language
- English
- Date published
- 05/01/2019
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Health and Human Physiology
- Record Identifier
- 9984275052802771
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