Assessment of data-driven gating for cardiac motion extraction and LVEF estimation from routine [ 18 F]FDG LAFOV PET

OBJECTIVE: Chemotherapy-induced cardiotoxicity can lead to irreversible heart failure. Left ventricular ejection fraction (LVEF) is routinely monitored during treatment, but conventional assessment requires dedicated cardiac imaging and clinical resources. This study evaluated a deviceless data-driven gating (DDG) framework for extracting cardiac motion from routine [18F]FDG PET emission data and, secondary, assessed its feasibility for LVEF estimation. Approach. The DDG framework is based on histo images and uses anatomical masking for frequency-domain filtering to obtain the cardiac signals of 169 [18F]FDG PET/CT examinations. The cardiac gating performance was evaluated, and LVEF estimates were compared with the patient's respective clinical reference method; echocardiography or multigated acquisition (MUGA). Main results. The DDG framework successfully extracted a cardiac gating signal in 138 of 169 examinations (81.7%). Sufficient myocardial [18F]FDG uptake for software-based LVEF estimation was present in 101 patients (60%), and LVEF was successfully estimated in all cases. Compared with the reference methods, the DDG-based LVEF estimates demonstrated a mean bias of 3.4% relative to echocardiography and -2.6% relative to MUGA. Agreement was strongest with echocardiography, although the limits of agreement exceeded the threshold required for interchangeable clinical use. The extracted pulse frequencies were physiologically plausible and showed good agreement with the corresponding reference examinations, supporting the validity of the DDG-derived cardiac signal. Significance. The deviceless DDG framework can successfully extract clinically relevant cardiac motion directly from routine [18F]FDG PET acquisitions without external hardware. The method achieved a high success rate with respect to gating the images and provided LVEF estimates that showed good agreement with established clinical reference methods, supporting the feasibility of deriving functional cardiac information from standard PET examinations. The proposed approach provides a promising foundation for retrospective functional cardiac assessment and has the potential to complement conventional LVEF evaluation while reducing additional patient burden and simplifying clinical workflows.