Purpose: Develop a dosimetric method to simulate the local dose distribution and the biological effect of nucleus targeted AuPd¹⁰³ nanoparticles using a local effective model (LEM) and Voxel Dose Kernel (VDK) convolution
Methods: The VDK of electrons, photons from AuPd¹⁰³NP decays, and gold shell ionization were simulated using TOPAS MC. A Cubic water phantom with 10µm sidelength was created and 1000 AuPd¹⁰³NP were randomly distributed, and coordinates were recorded. The total number of gold shell ionization events and AuPd¹⁰³NP decays in a phantom for a target dose was simulated using TOPAS. Decay and gold shell ionization events were randomly assigned to NPs in a phantom. VDKs were convoluted to each NP location to create a three-dimensional dose and Isodose map. TOPAS was used to create an isodose map using the same geometry as a comparison. Dose and computation time differences were measured. Using the same method, three-dimensional dose map of targeted and nontargeted AuPd¹⁰³NP with known NP concentration in cell and media from previous experiments. Dose-response was calculated using a LEM with a linear quadratic linear (LQL) equation. Relative biological effectiveness (RBE) of two different NPs were calculated compared to the conventional LDR Pd103 seed with a dose rate of 2Gy/hr.
Results: The differences were less than 5% for both isodose map and average dose of phantom generated using 3D convolution and MC simulation methods. The computation time for the direct MC method was over 30hrs, while the convolution method took less than 40 minutes. RBE values of targeted and nontargeted AuPd¹⁰³NP were 4.59 and 3.47.
Conclusion: We showed that three-dimensional VDK convolution combined with a LEM can be a dosimetric method to assess the biological effects of radionuclide nanoparticles with a reasonable uncertainty. The nucleus targeted AuPd¹⁰³NP demonstrated the potential to be a great candidate for radionuclide brachytherapy source
Monte Carlo, Pd-103, Radiation Dosimetry
IM/TH- Radiopharmaceutical Therapy: Dose estimation: Monte Carlo