medRxiv : the preprint server for health sciences · 2026
Virtual myocardial blood flow and flow reserve from static PET imaging using artificial intelligence.
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Urs Meghana, Kwieciński Jacek, Lemley Mark, Chareonthaitawee Panithaya, Ramirez Giselle, Shanbhag Aakash, Killekar Aditya, deKemp Robert, Acampa Wanda, Le Viet T, Mason Steve, Knight Stacey, Packard Rene R S, Al-Mallah Mouaz, Berman Daniel S, Dey Damini, Miller Robert J H, Di Carli Marcelo, Slomka Piotr J
Аннотация
BACKGROUND: Quantitative myocardial blood flow (MBF) and myocardial flow reserve (MFR) provide incremental diagnostic and prognostic value in cardiac PET, but their widespread use is limited by the technical demands of dynamic imaging protocols. We evaluated the feasibility of using artificial intelligence (AI) to predict MBF and MFR from static and gated PET images, without the need for dynamic acquisition.
METHODS: A machine learning (XGBoost) model was trained on 82Rb PET multi-center dataset using static perfusion imaging, injected dose, hemodynamic measures, clinical data and CT-derived features (including body composition) from the hybrid CT attenuation scan. Model performance was evaluated externally in an independent cohort.
RESULTS: In total, 10,566 (derivation-cohort) and 7,842 (external-cohort) patients were included in this multi-center study. On the external-cohort, AI approach achieved an Area under the curve (AUC) of 0.92 (0.92-0.93) for abnormal stress MBF and 0.91 (0.90-0.92) for abnormal MFR; Intra-class correlation (ICC) 0.80 (0.78-0.82) and 0.78 (0.76-0.79), respectively. AI MFR closely mirrored the prognostic performance of measured MFR, showing nearly identical Kaplan-Meier risk stratification (both p<0.0001) and maintaining strong, and independently significant associations with all-cause mortality (HR 3.4 [2.8-4.2] vs. 4.6 [3.6-5.8]; both p<0.001), and demonstrated similar added value to perfusion for mortality prediction.
CONCLUSION: AI-predicted virtual stress MBF and MFR assessment using static and gated PET data is feasible and generalizable across cohorts. By removing the dependency on dynamic acquisitions, this approach has the potential to broaden the clinical adoption of flow quantification.
METHODS: A machine learning (XGBoost) model was trained on 82Rb PET multi-center dataset using static perfusion imaging, injected dose, hemodynamic measures, clinical data and CT-derived features (including body composition) from the hybrid CT attenuation scan. Model performance was evaluated externally in an independent cohort.
RESULTS: In total, 10,566 (derivation-cohort) and 7,842 (external-cohort) patients were included in this multi-center study. On the external-cohort, AI approach achieved an Area under the curve (AUC) of 0.92 (0.92-0.93) for abnormal stress MBF and 0.91 (0.90-0.92) for abnormal MFR; Intra-class correlation (ICC) 0.80 (0.78-0.82) and 0.78 (0.76-0.79), respectively. AI MFR closely mirrored the prognostic performance of measured MFR, showing nearly identical Kaplan-Meier risk stratification (both p<0.0001) and maintaining strong, and independently significant associations with all-cause mortality (HR 3.4 [2.8-4.2] vs. 4.6 [3.6-5.8]; both p<0.001), and demonstrated similar added value to perfusion for mortality prediction.
CONCLUSION: AI-predicted virtual stress MBF and MFR assessment using static and gated PET data is feasible and generalizable across cohorts. By removing the dependency on dynamic acquisitions, this approach has the potential to broaden the clinical adoption of flow quantification.