The REgistry of Flow and Perfusion Imaging for Artificial Intelligence with positron emission tomography (REFINE PET):Rationale and design

Abstract

Background: The REgistry of Flow and Perfusion Imaging for Artificial Intelligence with positron emission tomography (REFINE PET) was established to collect multicenter PET and associated computed tomography (CT) images, together with clinical data and outcomes, into a comprehensive research resource. REFINE-PET will enable validation and development of both standard and novel cardiac PET/CT processing methods.

Methods: REFINE-PET is a multicenter, international registry that contains both clinical and imaging data. The PET scans were processed using QPET software (Cedars-Sinai Medical Center, Los Angeles, CA), while the CT scans were processed using deep learning (DL) to detect coronary artery calcium (CAC). Patients were followed up for the occurrence of major adverse cardiovascular events (MACE), which include death, myocardial infarction, unstable angina, and late revascularization (>90 days from PET).

Results: The REFINE-PET registry currently contains data for 35595 patients from 14 sites, with additional patient data and sites anticipated. Comprehensive clinical data (including demographics, medical history, and stress test results) were integrated with more than 2100 imaging variables across 34 categories. The registry is poised to address a broad range of clinical questions, supported by correlating invasive angiography (within 6 months of PET myocardial perfusion imaging [MPI]) in 5955 patients and a total of 9278 major adverse cardiovascular events during a median follow-up of 4.2 years.

Conclusions: The REFINE-PET registry leverages the integration of clinical, multimodality imaging, and novel quantitative and AI tools to advance the role of PET/CT MPI in diagnosis and risk stratification.

Keywords: Artificial intelligence; Coronary artery disease; Myocardial perfusion imaging; PET; Quantitative analysis.