Clinical validation of perfusion imaging with pulmonary function test data using Voronoi-based discretization.
Authors
Affiliations (6)
Affiliations (6)
- Department of Internal Medicine, UT Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, Texas, 75390, United States.
- The University of Texas at Austin, 107 W Dean Keeton St, Austin, Texas, 78712-1139, United States.
- Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, 1025 Walnut St #100, Philadelphia, Pennsylvania, 19107, United States.
- School of Medicine, Emory University, 1440 Clifton Rd N E, Atlanta, Georgia, 30322, United States.
- University of Kentucky College of Medicine, 780 Rose St MN 150, Lexington, Kentucky, 40536-0298, United States.
- Biomedical Engineering, The University of Texas at Austin, 107 W. Dean Keaton Street, Austin, Texas, 78712-1139, United States.
Abstract
Accurate lung function assessment is essential for diagnosing and managing diseases like COPD, 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 DLCO, FEV1, and FEV1/FVC, we find that discretizing perfusion maps into Voronoi subvolumes always yields stronger Spearman correlations than not discretizing. Specifically, our approach demonstrates strong correlations of 0.636 ≤ 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 ≤ 0.937 for raw-valued maps, 0.877 ≤ AUC ≤ 0.933 for percentile-scaled maps). 
Significance: This framework bridges regional perfusion imaging and global pulmonary function assessment, enabling meaningful quantitative comparisons between SPECT and CT-based perfusion 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.