Document Type

Article

Publication Date

3-3-2026

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This article is the author's final published version in Physics in Medicine & Biology, Volume 71, Issue 5, Article Number 055004.

The published version is available at https://doi.org/10.1088/1361-6560/ae4669. Copyright © 2026 The Author(s).

Abstract

Objective.Accurate lung function assessment is essential for diagnosing and managing diseases like chronic obstructive pulmonary disorder, pulmonary emboli, and lung cancer. Single-photon emission computed tomography (SPECT) provides valuable 3D functional imaging of ventilation and perfusion, but is limited by low spatial resolution, availability, additional radiation, and cost. Alternative methods, including CT-based perfusion (CT-P) and deep learning models, require large datasets to validate results that are often scarce. Pulmonary function tests (PFTs) offer rapid and noninvasive global lung function measures and are clinically widely used. While ventilation correlates well with PFTs, perfusion imaging presents challenges due to complex blood flow and difficulty summarizing 3D data into one value. Additionally, commonly employed percentile scaling removes absolute quantitative information, complicating interpretation.Approach.We propose a framework leveraging lung discretizations based on Voronoi diagrams to capture local spatial information from raw-valued and percentile-scaled perfusion maps (SPECT and CT-P). We compute hierarchical descriptive statistics at 3 levels (intra-subvolume, inter-subvolume, left-right lungs) to derive one global value per patient.Main results.Across PFT measures of diffusing capacity of lungs for carbon monoxide, forced expiratory volume after one second (FEV1), and FEV1/forced vital capacity, we find that discretizing perfusion maps into Voronoi subvolumes always yields stronger Spearman correlations than not discretizing. Specifically, our approach demonstrates strong correlations of0.636⩽ρ⩽0.843(P <  0.005) for raw-valued (SPECT and CT-P) maps,0.590⩽ρ⩽0.789(P <  0.005) for percentile-scaled maps, and reliably distinguishes normal from abnormal lung function via logistic regression analysis (0.865⩽AUC⩽0.937for raw-valued maps,0.877⩽AUC⩽0.933for percentile-scaled maps).Significance.This framework bridges regional perfusion imaging and global pulmonary function assessment, enabling meaningful quantitative comparisons between SPECT and CT-P maps. By preserving local spatial variability, the method offers a noninvasive tool for integrating imaging and physiological data, paving the way toward broader clinical and AI-driven applications in lung function evaluation.

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Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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PubMed ID

41698321

Language

English

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