Reconstruction of electron spectra from depth doses with adaptive regularization

Area of Science:

• Medical Physics
• Radiation Oncology
• Computational Dosimetry

Background:

• Electron spectral reconstruction is crucial for Monte Carlo treatment planning systems, providing essential initial phase space data.
• Standard spectral reconstruction methods using variational approaches with regularization can introduce nonphysical oscillations or overly smooth critical features like energy peaks.
• Accurate characterization of the sharp energy peak in electron spectra is essential for reliable treatment planning.

Purpose of the Study:

• To develop and validate an improved method for electron spectral reconstruction from measured depth doses.
• To address the limitations of existing regularization techniques in preserving the sharp energy peak of electron spectra.
• To enhance the accuracy of initial phase space distributions for Monte Carlo simulations in radiotherapy.

Main Methods:

• Utilized a variational method combined with a novel adaptive regularization term, a specific form of Tikhonov regularization.
• Applied the method to reconstruct electron spectra for 6, 9, and 18 MeV beams from a Varian Clinac 2100C accelerator.
• Validated the reconstructed spectra against measured depth dose distributions.

Main Results:

• The adaptive regularization technique effectively removed oscillations without significant smoothing of the energy peak.
• Reconstructed electron spectra closely matched the actual spectra, demonstrating high fidelity.
• The method proved to be simple, effective, and accurate for spectral reconstruction.

Conclusions:

• The proposed variational method with adaptive regularization offers a superior approach for electron spectral reconstruction.
• This technique significantly improves the accuracy of initial phase space distributions for Monte Carlo treatment planning.
• The findings have direct implications for enhancing the precision and reliability of radiation therapy planning.