Predicting Material Composition and Density From Basis-Vector Model Weights for Dual-Energy CT-Based Monte Carlo Proton-Beam Dose Calculations
M Medrano1*, X Chen2, L Burigo3, T Zhao4, J O'Sullivan5, J Williamson6, (1) Washington University in St. Louis, St. Louis, MO, (2) Washington University, St. Louis, MO, (3) ,Heidelberg, ,DE, (4) Washington University School of Medicine, St. Louis, MO, (5) Washington University, St. Louis, MO, (6) Washington University, Richmond, VA
Presentations
TU-I345-IePD-F7-3 (Tuesday, 7/12/2022) 3:45 PM - 4:15 PM [Eastern Time (GMT-4)]
Exhibit Hall | Forum 7
Purpose: Previous studies demonstrate that a linear basis-vector model (BVM) accurately predicts proton stopping-power ratio (SPR) maps in simulated and experimental data. While SPR is sufficient for pencil-beam dose calculations, Monte Carlo (MC) simulation requires the atomic composition and density of each medium to compute multiple elastic, inelastic, and nuclear scattering cross-sections. Herein we propose a method for predicting atomic composition and mass density from the two independent BVM weights derived from dual-energy CT imaging.
Methods: Our method, called BVM material indexing, uses multiple linear regression on the BVM weights and their quotient to predict the percent by weight concentration of elements for Z=1:20 and mass density of 69 representative tissue-compositions derived from the literature. The predicted compositions and densities were imported to the TOPAS MC codes and used to simulate a single 200 MeV proton beam delivered to uniform cylinder phantoms composed of the 69 tissues. MC dose distributions based on the BVM and a standard single-energy CT (SECT) material indexing approaches were compared to those derived from ground-truth tissue atomic compositions. The SPR, range (RBP), and depth of 80% of maximum dose (R80) were utilized to quantify dose-estimation errors.
Results: Root-mean-square (RMS)/Max error in estimated SPR and RBP were 0.6/2.1% and 1.3/5.2 mm for SECT and 0.1/0.3% and 0.3/0.6 mm for BVM material-indexing schemes. Similarly, RMS/Max R80 errors for bony (soft) tissues for the SECT and BVM approaches were 0.7/1.5 mm (1.6/5.3 mm) and 0.1/0.3 mm (0.3/0.7 mm), respectively.
Conclusion: Our results show that fully exploiting the two-parameter BVM space for material indexing dramatically improves TOPAS MC dose-calculation accuracy (by factors of 4 to 7 in RMS) compared to the standard SECT single-parameter indexing process.
Keywords
Not Applicable / None Entered.
Taxonomy
TH- External Beam- Particle/high LET therapy: Proton therapy – computational dosimetry-Monte Carlo
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