Local weighting of nanometric track structure properties in macroscopic voxel geometries for particle beam treatment planning. (12th November 2015)

• Record Type: Journal Article
• Authors: Alexander, F; Villagrasa, C; Rabus, H; Wilkens, J J
• Is Part Of: Physics in medicine & biology. Volume 60:Number 23(2015:Dec.)
• Page Start: 9145
• Page End: 9156
• Publication Date: 2015-11-12
• Subjects: Geant4-DNA -- nanodosimetry -- proton therapy; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/0031-9155/60/23/9145
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: The research project BioQuaRT within the European Metrology Research Programme aimed at correlating ion track structure characteristics with the biological effects of radiation and developed measurement and simulation techniques for determining ion track structure on different length scales from about 2 nm to about 10 μ m. Within this framework, we investigated methods to translate track-structure quantities derived on a nanometre scale to macroscopic dimensions. Here we make use of parameterizations that link the energy of the projectile to the ionization pattern of the track using nanodosimetric ionization cluster size distributions. They were defined with data generated by simulations of ion tracks in liquid water using the Geant4 Monte Carlo toolkit with the Geant4-DNA processes. For the clinical situation with a mixed radiation field, where particles of various energies hit a cell from several directions, we have to find macroscopic relevant mean values. They can be determined by appropriate local weighting functions for the identified parameterization. We show that a stopping power weighted mean value of the mentioned track structure properties can describe the overall track structure in a cell exposed to a mixed radiation field. The parameterization, together with the presented stopping power weighting approach, show how nanometric track structure properties could be integrated into treatment planning systems without the need to perform time consuming simulations on the nanometer level for each individual patient.