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. 2025 Apr 25:16:1555230.
doi: 10.3389/fphys.2025.1555230. eCollection 2025.

Longitudinal study of COPD phenotypes using integrated SPECT and qCT imaging

Frank Li  1   2 , Xuan Zhang  2   3 , Alejandro P Comellas  4 , Eric A Hoffman  1   5 , Michael M Graham  5 , Ching-Long Lin  1   2   3   5
Affiliations

Longitudinal study of COPD phenotypes using integrated SPECT and qCT imaging

Frank Li et al. Front Physiol. .

Abstract

Introduction: The aim of this research is to elucidate chronic obstructive pulmonary disease (COPD) progression by quantifying lung ventilation heterogeneities using single-photon emission computed tomography (SPECT) images and establishing correlations with quantitative computed tomography (qCT) imaging-based metrics. This approach seeks to enhance our understanding of how structural and functional changes influence ventilation heterogeneity in COPD.

Methods: Eight COPD subjects completed a longitudinal study with three visits, spaced about a year apart. CT scans were performed at each visit and qCT-based variables were derived to measure the structural and functional characteristics of the lungs, while the SPECT-based variables were used to quantify lung ventilation heterogeneity. The correlations between key qCT-based variables and SPECT-based variables were examined.

Results: The SPECT-based ventilation heterogeneity (CVTotal) showed strong correlations with the qCT-based functional small airway disease percentage (fSAD%Total) and emphysematous tissue percentage (Emph%Total) in the total lung, based on cross-sectional data. Over the 2-year period, changes in SPECT-based hot spots (TCMax) exhibited strong negative correlations with changes in fSAD%Total, Emph%Total, and the average airway diameter in the left upper lobe, as well as a strong positive correlation with alternations in airflow distribution between the upper and lower lobes.

Discussion: In conclusion, this study found strong positive cross-sectional correlations between CVTotal and both fSAD% and Emph%, suggesting that these markers primarily reflect static disease severity at a single time point. In contrast, longitudinal correlations between changes in TCMax and other variables over 2 years may capture the dynamic process of hot spot formation, independent of disease severity. These findings suggest that changes in TCMax may serve as a more sensitive biomarker than changes in CVTotal for tracking the underlying mechanisms of COPD progression.

Keywords: COPD; CT; SPECT; small airway disease; ventilation.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

FIGURE 1
FIGURE 1
The multiscale structural and functional qCT variables.
FIGURE 2
FIGURE 2
(a) A scatter plot showing the strong correlation between TC% and ΔVair F of the lobes (r = 0.73). (b) A scatter plot showing the strong correlations between CVTotal and PFT results (FEV1% predicted: r = −0.74, FEV1/FVC (%): r = −0.80). Data points are derived from all three visits.
FIGURE 3
FIGURE 3
Normalized SPECT images transformed to the TLC domain at three visits. Subjects 1 and 2 did not have ventilation scans at V0. Intensities were normalized using the minimum and maximum values, resulting in contour values ranging from 0 to 1. The localized red spots in Subjects 2 and 5 indicate high-intensity hot spots where inhaled aerosols tend to deposit.
FIGURE 4
FIGURE 4
The fSAD-voxel maps (left column) and normalized TC SPECT images (right column) for each subject at V1, where fSAD stands for functional small airway disease and TC represents tracer concentration. These images were plotted in the coronal planes of the CT images. The relationship between qCT-based fSAD maps and SPECT-based ventilation patterns reveals distinct groupings among subjects: Subjects 2 and 5 exhibit high fSAD%Total, Subjects 1, 3, and 4 display moderate fSAD%Total, and Subjects 6, 7, and 8 demonstrate low fSAD%Total. SPECT intensities were normalized using the minimum and maximum values, resulting in contour values ranging from 0 to 1.
FIGURE 5
FIGURE 5
The correlation maps between the SPECT variables and the qCT variables for both cross-sectional and longitudinal data. Correlation magnitudes exceeding 0.7 were highlighted using blue boxes.
FIGURE 6
FIGURE 6
The cross-lagged panel analysis estimated a total of six correlations. The correlations colored in red were significantly greater than zero. The result that rfSAD1,CV2 was greater than zero suggested that fSAD cause the heterogeneity of lung ventilation.
FIGURE 7
FIGURE 7
A constricted segmental airway in the LUL (red arrow) of Subject 3 at V2, which may contribute to the TC hot spot observed in the same region. In addition, a constricted segmental airway in the LUL (red arrow) of Subject 5, present at V0, had resolved by V2, potentially increasing ventilation homogeneity.
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