A theoretical framework to predict the most likely ion path in particle imaging.

Area of Science:

• Physics
• Medical Physics
• Computational Physics

Background:

• Accurate prediction of ion trajectories is crucial for applications like particle therapy and imaging.
• Current methods often rely on extensive simulations or approximations.

Purpose of the Study:

• Introduce a rigorous Bayesian formalism to predict ion paths through a medium.
• Compare the precision of estimated paths with Monte Carlo simulations for various ions.

Main Methods:

• Developed a Bayesian framework combining particle scattering and measurement data (position, direction, energy).
• Simulated hydrogen to carbon ions in two fixed-range and fixed-velocity scenarios.
• Calculated root-mean-square error between estimated and simulated paths.

Main Results:

• In fixed-range scenario, root-mean-square error decreased from 0.50 mm (proton) to 0.18 mm (helium) and 0.09 mm (carbon).
• In fixed-velocity scenario, error decreased from 0.29 mm (proton) to 0.09 mm (helium) then increased to 0.12 mm (carbon).
• Helium ions showed the most accurate path estimation with the lowest dose.

Conclusions:

• The Bayesian approach provides a robust method for ion path prediction.
• Helium ions are optimal for accurate path estimation with minimal dose, suggesting potential for high-resolution medical imaging.
• The study highlights trade-offs between dose and path resolution in different experimental configurations.