Iterative reconstruction with boundary detection for carbon ion computed tomography.

Area of Science:

• Medical physics
• Radiotherapy technology
• Imaging science

Background:

• Accurate ion range prediction is crucial for effective heavy ion radiation therapy.
• Precise Bragg peak localization optimizes tumor coverage and minimizes damage to healthy tissues.

Purpose of the Study:

• To investigate the feasibility of using carbon ions for computed tomography (carbon CT) for improved range prediction in heavy ion therapy.
• To develop and evaluate a carbon CT reconstruction method for creating relative stopping power maps.

Main Methods:

• Monte Carlo simulations using Geant4 to model carbon ion trajectories and deflections in a water phantom.
• Acquisition of carbon CT projections from contrast and spatial resolution phantoms using a 430 MeV/u carbon ion beam.
• Image reconstruction using an iterative algebraic reconstruction technique with total variation minimization (ART-TV) and analysis of projection count and ion number effects.

Main Results:

• Developed a carbon CT method capable of reconstructing phantoms with varying materials and spatial resolutions.
• Investigated the impact of the number of projections and ions on image quality.
• Demonstrated that carbon CT-derived range estimation offers improved accuracy over traditional calibration curves.

Conclusions:

• Carbon CT is a promising technique for generating relative stopping power maps essential for precise range prediction in heavy ion therapy.
• The developed reconstruction method and simulation approach provide a foundation for clinical implementation.
• This advancement has the potential to enhance treatment planning and patient outcomes in particle therapy.