Advances in dual energy computed tomography approach for proton stopping power ratio computation in radiotherapy

Table 2 Stopping power ratio training and testing root mean square error (%) using estimated ρ_e as presented in Chika[20]

SPR training RMSE (%)	Total	Lung	Soft	Bone	Testing RMSE (%)	Total	Soft	Bone
BVM SPR from I1,0	0.26	0.14	0.32	0.06		0.86	0.99	0.63
BVM SPR1,1	0.23	0.07	0.28	0.05		0.98	1.18	0.61
BVM SPR from Ifc	0.29	-0.09	0.31	0.28		1.59	0.61	2.36
BVM SPR from Irc	0.25	0.09	0.30	0.13		1.54	0.61	2.27
H-S SPR from I1,0	0.98	0.94	1.00	0.95		1.40	1.45	1.33
H-S SPR1,1	0.22	0.01	0.27	0.05		0.97	1.15	0.63
H-S Method	0.39	0.28	0.41	0.35		0.85	0.91	0.77

Citation: Chika CE. Estimation of Proton Stopping Power Ratio and Mean Excitation Energy Using Electron Density and Its Applications via Machine Learning Approach. J Med Phys 2024; 49: 155-166. Copyright© The Authors 2024. Published by Wolters Kluwer Medknow Publications. This article is available under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike License (CC BY-NC-SA), which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. Permission only needs to be obtained for commercial use. SPR: Stopping power ratio; RMSE: Root mean square error; BVM: Basis vector method; H-S: Hunemohr’s and Saito.