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

Table 3 Stopping power ratio modeling and testing root mean square (%) using computed tomography number as presented in Chika[21]

SPR training RMSE (%)	Total	Lung	Soft	Bone	Testing RMSE (%)	Total	Soft	Bone
TSPR,1,0;fr(μ)	1.78	0.04	0.58	2.98		4.72	4.80	4.61
TSPR,1,2;fr(μ)	0.49	0.04	0.58	0.26		5.87	4.82	7.08
TSPR,1,2;fm(μ)	1.04	0.03	1.14	0.87		3.89	3.07	4.80
TSPR,1,2;fL(μ)	0.85	0.00	1.00	0.53		3.14	3.28	3.27
Stochiometric	0.86	0.01	1.00	0.56		3.39	3.35	3.44
Taasti	0.60	0.01	0.22	1.00		6.77	7.19	6.14
H-S method	5.10	0.45	3.42	7.46		4.51	4.47	4.57
TSPR,1,2;fr(μ)4g	0.38	0.04	0.44	0.26		5.87	4.82	7.08

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; RMSE: Root mean square; H-S method: Hunemohr’s and Saito model.