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.
Not Applicable / None Entered.