Program Information
Patient-Specific Dose Maps for CT Scans Using a Fast, Deterministic Boltzmann Transport Equation Solver
A Wang1*, A Maslowski1 , T Wareing1 , T Schmidt2 , J Star-Lack1 , (1) Varian Medical Systems, Palo Alto, CA, (2) Marquette University, Milwaukee, WI
Presentations
TH-EF-BRA-2 (Thursday, July 16, 2015) 1:00 PM - 2:50 PM Room: Ballroom A
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.
Funding Support, Disclosures, and Conflict of Interest: Adam Wang, Alex Maslowski, Todd Wareing, and Josh Star-Lack are employees of Varian Medical Systems.
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