Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.
Generalized multi-energy model for computing radiation treatment parameters on image domain and automated model-selection framework
Charles E Chika, Department of Mathematics, University of Nigeria, Nsukka 410001, Nigeria
Author contributions: Chika CE conceptualized and wrote the manuscript as well as completed all the research and manuscript processes.
AI contribution statement: No AI tool was used.
Conflict-of-interest statement: The author declares that there is no known conflict of interest.
Corresponding author: Charles E Chika, PhD, Department of Mathematics, University of Nigeria, Obukpa Road, Nsukka 410001, Nigeria. charles.chika@unn.edu.ng
Received: July 30, 2026
Revised: September 2, 2026
Accepted: October 8, 2026
Published online: October 28, 2026
Processing time: 90 Days and 9.8 Hours
Revised: September 2, 2026
Accepted: October 8, 2026
Published online: October 28, 2026
Processing time: 90 Days and 9.8 Hours
Core Tip
Core Tip: This study presents a mathematical model for improving the precision of relative electron density, mean excitation energy, effective atomic number and stopping power ratio using computed tomography image. It exposes the need for machine learning algorithm that will help with best model selection. Based on the observation that the performance of the models differ based on the type of linear attenuation used (i.e., spectrally averaged linear attenuation or linear attenuation at effective energy), this study presents machine learning algorithm for best model selection. It also discusses the mathematical proof of the presented generalized model.