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

Figure 2 Tissue classification using f(μ). A: Four regions of classification (lung tissues if f_r ≤ 0.3, soft tissue 1 if 0.3 < f_r ≤ 1.02, soft tissue 2 if 1.02 < f_r ≤ 1.4, and bone tissue if f_r > 1.4); B: Three regions of classification (lung tissues if f_L ≤ 0.3, soft tissue if 0.3 < f_L ≤ 1.4, and bone tissue if f_L > 1.4). 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.