Machine learned approach for estimating myocardial blood flow from dynamic CT and coronary artery disease risk factors

Ethan Tu; Muneeza Azmat; Kelley Branch; Adam Alessio

doi:10.1117/12.2581703

15 February 2021 Machine learned approach for estimating myocardial blood flow from dynamic CT and coronary artery disease risk factors

Ethan Tu, Muneeza Azmat, Kelley Branch, Adam Alessio

Author Affiliations +

Proceedings Volume 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging; 116001I (2021) https://doi.org/10.1117/12.2581703
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

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.

Conference Presentation

Citation Download Citation

Ethan Tu, Muneeza Azmat, Kelley Branch, and Adam Alessio "Machine learned approach for estimating myocardial blood flow from dynamic CT and coronary artery disease risk factors", Proc. SPIE 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging, 116001I (15 February 2021); https://doi.org/10.1117/12.2581703

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