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

Table 4 Stopping power ratio modeling and testing mean error (%) using computed tomography number as presented in Chika[21]

SPR training ME(%)	Total	Lung	Soft	Bone	SPR testing ME(%)	Total	Soft	Bone
TSPR,1,0;fr(μ)	0.28	0.04	0.01	0.84		0.63	1.02	0.09
TSPR,1,2;fr(μ)	0.00	0.04	0.00	0.00		-0.05	1.06	-1.62
TSPR,1,2;fm(μ)	-0.09	-0.03	-0.02	-0.24		-2.08	-1.48	-2.92
TSPR,1,2;fL(μ)	0.00	0.00	-0.01	0.02		-1.04	-0.85	-1.30
Stochiometric	-0.01	-0.01	-0.01	-0.01		-1.15	-0.93	-1.46
Taasti	-0.02	-0.01	-0.01	-0.03		2.19	2.95	1.14
H-S method	0.17	0.45	2.25	-3.84		-0.23	2.66	-4.28
TSPR,1,2;fr(μ)4g	0.00	0.04	0.00	0.00		-0.31	0.62	-1.62

Citation: Chika CE. Machine Learning Approach and Model for Predicting Proton Stopping Power Ratio and Other Parameters Using Computed Tomography Images. J Med Phys 2024; 49: 519-530. 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; ME: Mean error; H-S method: Hunemohr’s and Saito model.