Program Information
Analytical Stopping Power and Range Parameterization for Therapeutic Energy Intervals
W Donahue1*, W Newhauser1,2, J F Ziegler3 , (1) Louisiana State University, Baton Rouge, LA, (2) Mary Bird Perkins Cancer Center, Baton Rouge, LA, (3) United States Naval Academy, Annapolis, MD
Presentations
SU-G-TeP1-2 (Sunday, July 31, 2016) 4:00 PM - 4:30 PM Room: ePoster Theater
Purpose:To develop a simple, analytic parameterization of stopping power and range, which covers a wide energy interval and is applicable to many species of projectile ions and target materials, with less than 15% disagreement in linear stopping power and 1 mm in range.
Methods:The new parameterization was required to be analytically integrable from stopping power to range, and continuous across the range interval of 1 μm to 50 cm. The model parameters were determined from stopping power and range data for hydrogen, carbon, iron, and uranium ions incident on water, carbon, aluminum, lead and copper. Stopping power and range data was taken from SRIM. A stochastic minimization algorithm was used to find model parameters, with 10 data points per energy decade. Additionally, fitting was performed with 2 and 26 data points per energy decade to test the model’s robustness to sparse
Results:6 free parameters were sufficient to cover the therapeutic energy range for each projectile ion species (e.g. 1 keV – 300 MeV for protons). The model agrees with stopping power and range data well, with less than 9% relative stopping power difference and 0.5 mm difference in range. As few as, 4 bins per decade were required to achieve comparable fitting results to the full data set.
Conclusion:This study successfully demonstrated that a simple analytic function can be used to fit the entire energy interval of therapeutic ion beams of hydrogen and heavier elements. Advantages of this model were the small number (6) of free parameters, and that the model calculates both stopping power and range. Applications of this model include GPU-based dose calculation algorithms and Monte Carlo simulations, where available memory is limited.
Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by a research agreement between United States Naval Academy and Louisiana State University: Contract No N00189-13-P-0786. In addition this work was accepted for presentation at the American Nuclear Society Annual Meeting 2016.
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