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Development of the GHOST plugin for voxelized phantom generation in MCNP using 3D Slicer
Harlley Hauradou1, Paula Selvatice Pereira Teles2, Mirta B Torres3
1Nuclear Engineering Program, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil; Dosimagem, Rio de Janeiro, RJ, Brazil.
Summary
GHOST is a new automated tool that generates MCNP input files for voxelized phantoms from 3D medical images, simplifying radiation dosimetry simulations in Nuclear Medicine and Radiotherapy.
Area of Science:
- Medical Physics
- Computational Biology
- Radiological Sciences
Background:
- Monte Carlo simulations are crucial for accurate radiation dose calculations in Nuclear Medicine and Radiotherapy.
- Manual MCNP input file generation for voxelized phantoms is time-consuming and prone to errors.
Purpose of the Study:
- To develop an automated tool, GHOST, for generating MCNP input files using voxelized phantoms from 3D medical images.
- To streamline the process of creating complex simulation models for radiation dosimetry.
Main Methods:
- GHOST was developed as a Python plugin for 3D Slicer, creating lattice format phantoms from segmented volumetric data.
- Users can assign materials based on segmentation labels and include custom materials.
- Validation involved comparing GHOST-generated MCNP inputs with RPP card-based MCNP and GATE simulations.
Main Results:
- Energy deposition simulations showed a maximum discrepancy of 2.3% between GHOST-generated inputs and MCNP-RPP or GATE simulations.
- GHOST successfully generated MCNP input files from MHD format images.
Conclusions:
- GHOST provides an efficient and agile solution for automating the creation of voxelized phantoms for MCNP simulations.
- This tool can significantly reduce the effort and potential errors in dosimetry research.
