Assessment of Ventilation Heterogeneity and Particle Deposition in Asthmatics using Combined SPECT/CT Imaging and Computational Modeling Approaches

Xuan Zhang; Prathish K Rajaraman; Frank Li; Sanghun Choi; Alejandro P Comellas; Eric A Hoffman; Sean B Fain; David W Kaczka; Benjamin M Smith; Jiwoong Choi; Mario Castro; Sally E Wenzel; Nizar N Jarjour; Mark L Schiebler; Elliot Israel; Bruce D Levy; John V Fahy; Serpil C Erzurum; Andrew Babiskin; Minori Kinjo; Ross Walenga; Ching-Long Lin

doi:10.1016/j.ejps.2025.107093

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Assessment of Ventilation Heterogeneity and Particle Deposition in Asthmatics using Combined SPECT/CT Imaging and Computational Modeling Approaches

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Assessment of Ventilation Heterogeneity and Particle Deposition in Asthmatics using Combined SPECT/CT Imaging and Computational Modeling Approaches

Xuan Zhang, Prathish K Rajaraman, Frank Li, Sanghun Choi, Alejandro P Comellas, Eric A Hoffman, Sean B Fain, David W Kaczka, Benjamin M Smith, Jiwoong Choi, …

European journal of pharmaceutical sciences, Vol.209, 107093

06/01/2025

DOI: 10.1016/j.ejps.2025.107093

PMCID: PMC12145795

PMID: 40185289

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Abstract

This study investigated asthma phenotypes and their associations with ventilation heterogeneity and particle deposition by utilizing Single-Photon Emission Computed Tomography (SPECT) imaging, quantitative Computed Tomography (qCT) imaging-based subgrouping, and a whole-lung computational model.PURPOSEThis study investigated asthma phenotypes and their associations with ventilation heterogeneity and particle deposition by utilizing Single-Photon Emission Computed Tomography (SPECT) imaging, quantitative Computed Tomography (qCT) imaging-based subgrouping, and a whole-lung computational model.Two datasets were analyzed: one from a combined SPECT and CT (SPECT/CT) study with six asthmatic subjects, and another from the Severe Asthma Research Program (SARP) with 209 asthmatic subjects. Data from 35 previously acquired healthy subjects served as a control group. Each subject underwent CT scans at full inspiration and expiration, along with pulmonary function testing (PFT). The SPECT/CT study included ventilation SPECT imaging. Key qCT variables such as airway diameter, wall thickness, percentage of air trapping (AirT%), and percentage of small airway disease (fSAD%) were assessed. A subject-specific whole-lung computational fluid and particle dynamics (CFPD) model predicted airway resistance, particle deposition fraction, and the coefficient of variation (CV) for ventilation heterogeneity. Subjects were categorized into four predefined asthma imaging subgroups/clusters with increasing severity (C1-C4). CFPD-predicted CVs were validated against SPECT measurements. We compared PFT, qCT, and CFPD variables across SARP clusters and analyzed particle deposition fractions in large conducting, small conducting, and respiratory airways.MATERIALS AND METHODSTwo datasets were analyzed: one from a combined SPECT and CT (SPECT/CT) study with six asthmatic subjects, and another from the Severe Asthma Research Program (SARP) with 209 asthmatic subjects. Data from 35 previously acquired healthy subjects served as a control group. Each subject underwent CT scans at full inspiration and expiration, along with pulmonary function testing (PFT). The SPECT/CT study included ventilation SPECT imaging. Key qCT variables such as airway diameter, wall thickness, percentage of air trapping (AirT%), and percentage of small airway disease (fSAD%) were assessed. A subject-specific whole-lung computational fluid and particle dynamics (CFPD) model predicted airway resistance, particle deposition fraction, and the coefficient of variation (CV) for ventilation heterogeneity. Subjects were categorized into four predefined asthma imaging subgroups/clusters with increasing severity (C1-C4). CFPD-predicted CVs were validated against SPECT measurements. We compared PFT, qCT, and CFPD variables across SARP clusters and analyzed particle deposition fractions in large conducting, small conducting, and respiratory airways.Cluster C4 exhibited a significantly distinct ventilation profile compared to other clusters and health controls. This distinction contrasted with the insignificant differences between ventilation profiles in severity subgroups defined by conventional spirometry-based guidelines. Airway resistance varied significantly across the asthma clusters. Although both C3 and C4 clusters represented severe asthma, only C4 showed a significant increase in AirT%, primarily due to fSAD%. Since inflammatory phenotypes differ - C3 with wall thickening in large and small conducting airways, and C4 with elevated fSAD% and Emph% in small conducting and respiratory airways - fine particles (∼5 μm) and extrafine particles (∼1 μm) are more effective at reaching the respective regions in C3 and C4. Given that C2 and C4 have hyper-responsive phenotypes with narrowed conducting airways, fine particles are more effective in reaching these areas. Airway enlargement in targeted segments of the left lower lobe resulted in improved particle deposition.RESULTSCluster C4 exhibited a significantly distinct ventilation profile compared to other clusters and health controls. This distinction contrasted with the insignificant differences between ventilation profiles in severity subgroups defined by conventional spirometry-based guidelines. Airway resistance varied significantly across the asthma clusters. Although both C3 and C4 clusters represented severe asthma, only C4 showed a significant increase in AirT%, primarily due to fSAD%. Since inflammatory phenotypes differ - C3 with wall thickening in large and small conducting airways, and C4 with elevated fSAD% and Emph% in small conducting and respiratory airways - fine particles (∼5 μm) and extrafine particles (∼1 μm) are more effective at reaching the respective regions in C3 and C4. Given that C2 and C4 have hyper-responsive phenotypes with narrowed conducting airways, fine particles are more effective in reaching these areas. Airway enlargement in targeted segments of the left lower lobe resulted in improved particle deposition.Our cluster-informed CFPD-based approach enhances the understanding of ventilation heterogeneity in asthma and holds potential for refining strategies for inhalational therapies.CONCLUSIONOur cluster-informed CFPD-based approach enhances the understanding of ventilation heterogeneity in asthma and holds potential for refining strategies for inhalational therapies.

Asthma

SPECT

Clusters

CFPD

Ventilation heterogeneity

Particle deposition

Details

Title: Subtitle: Assessment of Ventilation Heterogeneity and Particle Deposition in Asthmatics using Combined SPECT/CT Imaging and Computational Modeling Approaches
Creators: Xuan Zhang - University of Iowa
Prathish K Rajaraman - University of Iowa
Frank Li - University of Iowa
Sanghun Choi - Kyungpook National University
Alejandro P Comellas - University of Iowa
Eric A Hoffman - University of Iowa
Sean B Fain - University of Iowa
David W Kaczka - University of Iowa
Benjamin M Smith - Columbia University
Jiwoong Choi
Mario Castro
Sally E Wenzel
Nizar N Jarjour
Mark L Schiebler
Elliot Israel
Bruce D Levy
John V Fahy
Serpil C Erzurum
Andrew Babiskin
Minori Kinjo
Ross Walenga
Ching-Long Lin - University of Iowa
Resource Type: Journal article
Publication Details: European journal of pharmaceutical sciences, Vol.209, 107093
DOI: 10.1016/j.ejps.2025.107093
PMID: 40185289
PMCID: PMC12145795
NLM abbreviation: Eur J Pharm Sci
ISSN: 1879-0720
eISSN: 1879-0720
Publisher: ELSEVIER
Grant note: FDA: U01-FD005837 NIH: R01HL168116, R01-HL130506, ED P116S210005 Office of the Assistant Secretary of Defense for Health Affairs, Peer Reviewed Medical Research Program: W81XWH-21-1-0507, W911NF23-1-0004
This work was supported in part by FDA U01-FD005837, NIH R01HL168116, NIH R01-HL130506, ED P116S210005, and the Office of the Assistant Secretary of Defense for Health Affairs, Peer Reviewed Medical Research Program, Awards W81XWH-21-1-0507 and W911NF23-1-0004. Views expressed in this work do not necessarily reflect the official policies of the Department of Health and Human Services and may not be quoted as being made on behalf of a reflecting the position of the U.S. Food and Drug Administration; nor does any mention of trade names, commercial practices, or organization imply endorsement by the United States Government. Opinions, interpretations, conclusions, and recommendations are those of the authors, and are not necessarily endorsed by the Department of Defense. The authors would like to thank the SARP clinical coordinators and laboratory personnel for their contribution to fulfilling SARP's scientific mission. The research findings resulting from SARP would not have been possible without their dedication and assistance with study visits, data collection, and biological sample processing. The following companies provided financial support for study activities at the Coordinating and Clinical Centers beyond the third year of patient followup: AstraZeneca, Boehringer-Ingelheim, Genentech, GlaxoSmithKline, Sanofi-Genzyme-Regeneron, and TEVA.
Language: English
Electronic publication date: 04/02/2025
Date published: 06/01/2025
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Pulmonary, Critical Care, and Occupational Medicine; ICTS; IIHR--Hydroscience and Engineering; Anesthesia; Health, Sport, and Human Physiology ; Mechanical Engineering; Internal Medicine
Record Identifier: 9984805010602771

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