Estimating myocardial blood flow (MBF) is essential for diagnosing and risk stratifying myocardial ischemia. Currently, positron emission tomography (PET) is a gold standard for non-invasive, quantitative MBF measurements. In this work, we compare our machine learning derived MBF estimates to PET derived estimates, and 2-compartmental model derived MBF estimates. Our best performing model (ensemble regression tree) had a root mean squared error (RMSE) of 0.47 ml/min/g. Comparatively, the compartmental model achieved an RMSE of 0.80 ml/min/g. Including CAD risk factors improved flow estimation accuracy for models that trained on feature selected TAC data and worsened accuracy for models that trained on PCA data. Overall, our machine learning approach produces comparable MBF estimations to verified DCE-CT and PET estimates and can provide rapid assessments for myocardial ischemia.
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