Generalized multi-energy model for computing radiation treatment parameters on image domain and automated model-selection framework

This study developed a general n-energy mathematical model that predicts relative electron density (ρ_e), effective atomic number (Z_ef), mean excitation energy (I) and stopping power ratio (SPR) using computed tomography image, and developed an algorithm for best model selection. Mathematical proof is provided. Some numerical examples were carried out and the results were compared. The proposed method includes most of the existing methods as a special case. All the examples of the proposed method considered have modeling error that's less than or equal to 0.85% and 0.89% for SPR and ρ_e, respectively, with mean error close to 0.00% and low median absolute error. The method was able to achieve modeling root mean square error of 0.85% and 1.61% for I and Z_ef, respectively, with some flexibility for improvement. The mathematical model showed to be more general by including most of the other existing models and presents a direction to formulating new ones. It shows little or no bias on prediction since it has mean error that is close to zero in almost all predictions and low median absolute error. The algorithm gives the leverage to select the best model, improve the model and automation.