TH‐EF‐BRA‐02: Patient‐Specific Dose Maps for CT Scans Using a Fast, Deterministic Boltzmann Transport Equation Solver

Purpose: To develop a rapid and accurate software tool for computing patient-specific radiation dose maps of dose delivered from kV computed tomography (CT) scans. Methods: Monte Carlo methods currently provide the gold-standard for calculating patient-specific dose maps, but require immense computational resources to achieve sufficiently high statistical accuracy. To overcome this limitation, a deterministic method was implemented to solve the same underlying Boltzmann transport equation (BTE) that governs particle interactions and transport. Phase-space was discretized according to spatial location, energy, and angle, and a deterministic finite element algorithm was applied to compute the object’s photon fluence distribution, which does not exhibit stochastic noise. A computationally efficient GPU implementation for a standard workstation was developed, and comparison was made between the performance of the deterministic BTE solver and a standard Monte Carlo algorithm for a cone-beam projection of a virtual anthropomorphic chest phantom. Results: The BTE solution and Monte Carlo results were in strong agreement with a relative root-mean square error (RMSE) of 3.47%. Some larger differences existed at high-contrast boundaries (e.g., air/water) and within the bone, and are under further investigation. Notably, the computation time of the BTE solver was 8 seconds, while to obtain the same level of statistical uncertainty with conventional Monte Carlo required 1200 CPU-hours. Additionally, unlike Monte Carlo, the BTE computation time is only weakly dependent on the number of sources, making it extremely well-suited for CT dose calculations. Therefore, the BTE-based method is expected to offer a >30,000x speed increase compared to Monte Carlo for entire CT scans, even after application of variance reduction techniques and GPU implementation. Conclusion: The novel deterministic BTE solver offers a significantly faster alternative to Monte Carlo-based methods for computing dose delivered by CT scans, which can enable estimation of patient-specific organ doses for each CT examination performed. Adam Wang, Alex Maslowski, Todd Wareing, and Josh Star-Lack are employees of Varian Medical Systems.