The REgistry of Flow and Perfusion Imaging for Artificial INtelligEnce with PET(REFINE PET): Rationale and Design.

Background: The REgistry of Flow and Perfusion Imaging for Artificial Intelligence with PET (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 REgistry of Flow and Perfusion Imaging for Artificial INtelligEnce with PET (REFINE PET): Rationale and Design.

Rationale: The REgistry of Flow and Perfusion Imaging for Artificial INtelligEnce with PET (REFINE PET) was established to aggregate PET and associated computed tomography (CT) images with clinical data from hospitals around the world into one comprehensive research resource.

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).

AI-Derived Splenic Response in Cardiac PET Predicts Mortality: A Multi-Site Study.

Background: Inadequate pharmacologic stress may limit the diagnostic and prognostic accuracy of myocardial perfusion imaging (MPI). The splenic ratio (SR), a measure of stress adequacy, has emerged as a potential imaging biomarker.

Objectives: To evaluate the prognostic value of artificial intelligence (AI)-derived SR in a large multicenter Rb-PET cohort undergoing regadenoson stress testing.

Dynamic frame-by-frame motion correction for F-flurpiridaz PET-MPI using convolution neural network.

Purpose: Precise quantification of myocardial blood flow (MBF) and flow reserve (MFR) in F-flurpiridaz PET significantly relies on motion correction (MC). However, the manual frame-by-frame correction leads to significant inter-observer variability, time-consuming, and requires significant experience. We propose a deep learning (DL) framework for automatic MC of F-flurpiridaz PET.

Multicenter Evaluation of Interpretable AI for Coronary Artery Disease Diagnosis from PET Biomarkers.

Background: Positron emission tomography (PET)/CT for myocardial perfusion imaging (MPI) provides multiple imaging biomarkers, often evaluated separately. We developed an artificial intelligence (AI) model integrating key clinical PET MPI parameters to improve the diagnosis of obstructive coronary artery disease (CAD).

Methods: From 17,348 patients undergoing cardiac PET/CT across four sites, we retrospectively enrolled 1,664 subjects who had invasive coronary angiography within 180 days and no prior CAD.

Multicenter Evaluation of Myocardial Flow Reserve as a Prognostic Marker for Mortality in N-Ammonia PET Myocardial Perfusion Imaging.

Background: Myocardial flow reserve (MFR), measured by PET MPI, provides valuable information on epicardial coronary disease, diffuse atherosclerosis, and microvascular function. Despite its routine use, the prognostic efficacy of N-ammonia PET MFR remains unconfirmed in larger multicenter cohorts of patients with suspected or known coronary artery disease (CAD).

Methods: We considered patients from five sites in the REFINE PET registry who underwent N-ammonia PET MPI for CAD.

AI-based Hepatic Steatosis Detection and Integrated Hepatic Assessment from Cardiac CT Attenuation Scans Enhances All-cause Mortality Risk Stratification: A Multi-center Study.

Background: Hepatic steatosis (HS) is a common cardiometabolic risk factor frequently present but under-diagnosed in patients with suspected or known coronary artery disease. We used artificial intelligence (AI) to automatically quantify hepatic tissue measures for identifying HS from CT attenuation correction (CTAC) scans during myocardial perfusion imaging (MPI) and evaluate their added prognostic value for all-cause mortality prediction.

Methods: This study included 27039 consecutive patients [57% male] with MPI scans from nine sites.

AI-based identification of patients who benefit from revascularization: a multicenter study.

Background And Aims: Revascularization in stable coronary artery disease often relies on ischemia severity, but we introduce an AI-driven approach that uses clinical and imaging data to estimate individualized treatment effects and guide personalized decisions.

Methods: Using a large, international registry from 13 centers, we developed an AI model to estimate individual treatment effects by simulating outcomes under alternative therapeutic strategies. The model was trained on an internal cohort constructed using 1:1 propensity score matching to emulate randomized controlled trials (RCTs), creating balanced patient pairs in which only the treatment strategy-early revascularization (defined as any procedure within 90 days of MPI) versus medical therapy-differed.

Rb positron emission tomography myocardial blood flow quantification: Influence of arterial input curve quality on diagnostic accuracy.

Background: We investigated whether the shape of arterial blood input curves affects the diagnostic performance of myocardial blood flow (MBF) on rubidium-82 (Rb) positron emission tomography (PET) myocardial perfusion imaging (MPI) for obstructive coronary artery disease (CAD).

Methods And Results: We retrospectively enrolled 386 patients without prior CAD who underwent Rb PET-MPI and invasive coronary angiography within 6 months, from 2010 to 2018. Abnormal shapes of stress left atrial blood pool (BP) time activity curve were characterized into five categories based on visual/quantitative assessment: (1) low stress/rest peak ratio (SRPR), (2) slow activity rise, (3) slow activity decline, (4) broad peak and (5) multiple peaks.

Deep learning-quantified body composition from positron emission tomography/computed tomography and cardiovascular outcomes: a multicentre study.

Background And Aims: Positron emission tomography (PET)/computed tomography (CT) myocardial perfusion imaging (MPI) is a vital diagnostic tool, especially in patients with cardiometabolic syndrome. Low-dose CT scans are routinely performed with PET for attenuation correction and potentially contain valuable data about body tissue composition. Deep learning and image processing were combined to automatically quantify skeletal muscle (SM), bone and adipose tissue from these scans and then evaluate their associations with death or myocardial infarction (MI).

Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction.

Low-dose computed tomography attenuation correction (CTAC) scans are used in hybrid myocardial perfusion imaging (MPI) for attenuation correction and coronary calcium scoring, and contain additional anatomic and pathologic information not utilized in clinical assessment. We seek to uncover the full potential of these scans utilizing a holistic artificial intelligence (AI) approach. A multi-structure model segmented 33 structures and quantified 15 radiomics features in each organ in 10,480 patients from 4 sites.

Subendocardial quantification enhances coronary artery disease detection in F-flurpiridaz PET.

Purpose: The new high resolution positron emission tomography (PET) myocardial perfusion imaging tracer, F-flurpiridaz, is set to enter clinical use soon following its recent regulatory approval. We developed an approach for evaluating subendocardial analysis for stress total perfusion deficit (TPD) and ischemic TPD, assessed its performance for detection of coronary artery disease (CAD) and compared these measures to transmural analysis and expert physician assessments.

Methods: Myocardial perfusion image data from the F-flurpiridaz phase III clinical trial (NCT01347710) were used.

The Updated Registry of Fast Myocardial Perfusion Imaging with Next-Generation SPECT (REFINE SPECT 2.0).

The Registry of Fast Myocardial Perfusion Imaging with Next-Generation SPECT (REFINE SPECT) has been expanded to include more patients and CT attenuation correction imaging. We present the design and initial results from the updated registry. The updated REFINE SPECT is a multicenter, international registry with clinical data and image files.