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

Figure 1 Normalized energy spectra used: The blue line is for 150 kVp/Sn and the red line is for 80 kVp.

Figure 2 The training data three (3) and four (4) group regions. A: Tissues were classified into lung tissues, soft tissues and bone tissues contained in left, middle and right region, respectively; B: This is four (4) region classification that divided the middle region from the previous three (3) regions into two regions. This divides the soft tissues into two more classes. ρ_e: Relative electron density.

Figure 3 Showed that the methods have close performance on bone tissues with relative errors of less than 1% and T_SPR,1,2 performs slightly better than stoichiometric method in terms of single energy. A: Stopping power ratio modeling relative error for each human tissue using single energy computed tomography (CT) methods and one dual-energy CT method that performs well. It errors are as presented in the graph for individual tissues; B: Stopping power ratio modeling relative error for each human tissue using dual-energy CT methods. SPR: Stopping power ratio; H-S: Hunemohr-Saito.

Figure 4 Predicts relative electron density modeling relative error for each human tissue using dual-energy computed tomography methods. The errors for each method for individual tissues are as presented on the graph. ρ_e: Relative electron density; H-S: Hunemohr-Saito.

Figure 5 Z modeling relative error for each human tissue using dual-energy computed tomography methods. The errors for each method for individual tissues are as presented on the graph; H-S: Hunemohr-Saito.

Figure 6 Mean excitation energy modeling relative error for each human tissue using dual-energy computed tomography methods. The errors for each method for individual tissues are as presented on the graph. I: Mean excitation energy; H-S: Hunemohr-Saito.