Creation of a Standardized Myocardial Perfusion Single-Photon Emission Computed Tomography Database to Enhance Coronary Artery Disease Research Publisher

Summary: Could a shared SPECT dataset advance cardiac AI? Researchers released annotated heart scans with strong reviewer agreement to support reproducible model testing. #CardiacImaging #AI

Rahimian A ; Moslehi M ; Rabbani H ; Etehadtavakol M ; Dehghani T
Source: Journal of Medical Signals and Sensors Published:2026

Abstract

Background: Coronary artery disease (CAD) remains the leading global cause of cardiovascular mortality. Although single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is widely used, progress in artificial intelligence (AI)-based diagnostic tools is constrained by the limited availability of modern, high-quality, and consistently labeled imaging datasets. Methods: We retrospectively analyzed 144 rest–stress MIBI (99m Tc-methoxy isobutyl isonitrile) SPECT MPI studies acquired using ASNC/EANM-compliant protocols. Images were reconstructed using filtered back projection and independently interpreted by three nuclear cardiology specialists. Clinical, demographic, and imaging variables were analyzed using SPSS v26, with P < 0.05 defining statistical significance. Results: Significant sex-specific differences in CAD presentation were observed. Women with CAD were older and more frequently demonstrated perfusion patterns compatible with microvascular dysfunction, whereas men exhibited larger territorial defects. Diabetes prevalence was significantly higher among CAD-positive patients, whereas smoking patterns differed markedly by sex. Family history of CAD was significantly more common among CAD-positive subjects. Perfusion abnormalities correlated strongly with cumulative cardiovascular risk burden. Interobserver agreement was excellent (Intraclass correlation coefficient (ICC) = 0.87). Conclusions: We introduce a rigorously standardized, clinically annotated SPECT MPI dataset tailored for developing and validating explainable AI models in nuclear cardiology. Its sex-stratified structure supports research into CAD phenotypes and enhances reproducibility. Public Access: https://misp.mui.ac.ir/fa/t2-dual-head-spectct-coronary-artery-disease-cad. © 2026 Journal of Medical Signals & Sensors.