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Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
Published on: January 27, 2023
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Virtual myocardial blood flow and flow reserve from static PET imaging using artificial intelligence.
Meghana Urs1, Jacek Kwieciński1,2, Mark Lemley1
1Artificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Medrxiv : the Preprint Server for Health Sciences
|February 12, 2026
Summary
Artificial intelligence (AI) can now predict myocardial blood flow (MBF) and myocardial flow reserve (MFR) using static PET images, eliminating the need for dynamic scans. This AI approach shows strong accuracy and prognostic performance, potentially increasing the clinical use of flow quantification.
Area of Science:
- Cardiovascular Imaging
- Artificial Intelligence in Medicine
- Nuclear Cardiology
Background:
- Quantitative myocardial blood flow (MBF) and myocardial flow reserve (MFR) are valuable in cardiac PET but limited by dynamic imaging requirements.
- This study explores the feasibility of using AI to predict MBF and MFR from static and gated PET images.
Purpose of the Study:
- To evaluate the accuracy and generalizability of an AI model in predicting MBF and MFR from non-dynamic PET data.
- To assess the potential of AI-driven flow quantification to overcome limitations of current dynamic imaging protocols.
Main Methods:
- An XGBoost machine learning model was trained on a multi-center 82Rb PET dataset using static perfusion imaging, hemodynamic, clinical, and CT-derived data.
- The model's performance was validated externally on an independent patient cohort.
Main Results:
- The AI approach achieved high accuracy in predicting abnormal stress MBF (AUC 0.92) and MFR (AUC 0.91) in the external cohort.
- AI-predicted MFR demonstrated strong correlation (ICC 0.78) with measured MFR and mirrored its prognostic performance, including risk stratification for all-cause mortality.
Conclusions:
- AI-predicted virtual stress MBF and MFR from static/gated PET data are feasible and generalizable.
- This AI method, by removing the need for dynamic acquisitions, can potentially expand the clinical application of quantitative myocardial flow assessment.
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