Correcting Nonpathological Variation in Longitudinal Parametric Response Maps of CT Scans in COPD Subjects: SPIROMICS

Antonio Fernández-Baldera; Charles R Hatt; Susan Murray; Eric A Hoffman; Ella A Kazerooni; Fernando J Martinez; MeiLan K Han; Craig J Galbán

doi:10.18383/j.tom.2017.00013

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Correcting Nonpathological Variation in Longitudinal Parametric Response Maps of CT Scans in COPD Subjects: SPIROMICS

Journal article

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Correcting Nonpathological Variation in Longitudinal Parametric Response Maps of CT Scans in COPD Subjects: SPIROMICS

Antonio Fernández-Baldera, Charles R Hatt, Susan Murray, Eric A Hoffman, Ella A Kazerooni, Fernando J Martinez, MeiLan K Han and Craig J Galbán

Tomography (Ann Arbor), Vol.3(3), pp.138-145

09/2017

DOI: 10.18383/j.tom.2017.00013

PMCID: PMC5812694

PMID: 29457137

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Abstract

Small airways disease (SAD) is one of the leading causes of airflow limitations in patients diagnosed with chronic obstructive pulmonary disease (COPD). Parametric response mapping (PRM) of computed tomography (CT) scans allows for the quantification of this previously invisible COPD component. Although PRM is being investigated as a diagnostic tool for COPD, variability in the longitudinal measurements of SAD by PRM has been reported. Here, we show a method for correcting longitudinal PRM data because of nonpathological variations in serial CT scans. In this study, serial whole-lung high-resolution CT scans over a 30-day interval were obtained from 90 subjects with and without COPD accrued as part of SPIROMICS. It was assumed in all subjects that the COPD did not progress between examinations. CT scans were acquired at inspiration and expiration, spatially aligned to a single geometric frame, and analyzed using PRM. By modeling variability in longitudinal CT scans, our method could identify, at the voxel-level, shifts in PRM classification over the 30-day interval. In the absence of any correction, PRM generated serial percent volumes of functional SAD with differences as high as 15%. Applying the correction strategy significantly mitigated this effect with differences ∼1%. At the voxel-level, significant differences were found between baseline PRM classifications and the follow-up map computed with and without correction ( P < .01 over GOLD). This strategy of accounting for nonpathological sources of variability in longitudinal PRM may improve the quantification of COPD phenotypes transitioning with disease progression.

computed tomography

longitudinal

parametric response mapping

COPD

Details

Title: Subtitle: Correcting Nonpathological Variation in Longitudinal Parametric Response Maps of CT Scans in COPD Subjects: SPIROMICS
Creators: Antonio Fernández-Baldera - Department of Radiology, University of Michigan, Ann Arbor, MI
Charles R Hatt - Imbio, LLC. Minneapolis, MN
Susan Murray - Department of Public Health, University of Michigan, Ann Arbor, MI
Eric A Hoffman - Departments of Radiology and Biomedical Engineering, University of Iowa, IA
Ella A Kazerooni - Department of Radiology, University of Michigan, Ann Arbor, MI
Fernando J Martinez - Department of Medicine, Cornell University, NY; and
MeiLan K Han - Department of Internal Medicine, University of Michigan, Ann Arbor, MI
Craig J Galbán - Department of Radiology, University of Michigan, Ann Arbor, MI
Resource Type: Journal article
Publication Details: Tomography (Ann Arbor), Vol.3(3), pp.138-145
DOI: 10.18383/j.tom.2017.00013
PMID: 29457137
PMCID: PMC5812694
NLM abbreviation: Tomography
ISSN: 2379-1381
eISSN: 2379-139X
Publisher: Grapho Publications, LLC; Ann Abor, Michigan
Language: English
Date published: 09/2017
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Internal Medicine
Record Identifier: 9984051768402771

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