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The accuracy and variability of quantifications in Computed Tomography Angiography (CTA) are affected by imaging parameters and patient attributes. While patient attributes cannot usually be altered for a scan, imaging parameters can be optimized to improve the accuracy and precision of the procedure. This study developed a mathematical approach to find the optimal controllable parameters that maximize an ideal estimator, namely the Estimability index (e′), for quantifying coronary stenosis in cardiac CTA. We applied one-hot encoding to the categorical features and normalized the numerical features within 0 to 1. We applied a ridge regression model to the transformed data with polynomial feature transforms with degrees of 1 to 5. A grid search identified the polynomial model with the highest accuracy to predict the e′ value. We then evaluated the influence of each parameter and its permutation on the accuracy of the model. We formulated the corresponding optimization problem as maximization of e′ where the decision parameters are subject to linear constraints defining the upper bound and lower bound of each decision variable. We mathematically calculated the deterministic and probabilistic optimal controllable parameters across a range of deterministic and probabilistic uncontrollable parameters. The results showed that a reduced noise (less than 17 HU) and sharper MTFf50 (greater than 0.45 𝑚𝑚−1) maximizes e′. Moreover, the cardiac motion velocity had a higher impact on the deviation of the optimal decision variables compared to percent stenosis, vessel radius, plaque material, and lumen contrast.
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