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Advanced kernel methods vs. Monte Carlo-based dose calculation for high energy photon beams.

Irina Fotina1, Peter Winkler, Thomas Künzler

  • 1Department of Radiotherapy, Medical University Vienna/AKH Wien, Vienna, Austria. irina.fotina@akhwien.at

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Monte Carlo (MC) algorithms and advanced kernel-based methods in treatment planning systems (TPS) show accurate dose calculations. MC performed best in heterogeneous phantoms, though differences with kernel methods were minimal.

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Computational Dosimetry

Background:

  • Accurate dose calculation is crucial for effective radiation therapy.
  • Advanced algorithms and Monte Carlo (MC) methods are used in treatment planning systems (TPS).
  • Comparing their accuracy in homogeneous and heterogeneous environments is essential.

Purpose of the Study:

  • To compare the dose calculation accuracy of advanced kernel-based algorithms and MC algorithms in commercial TPS.
  • To evaluate performance in both homogeneous and heterogeneous phantom conditions.

Main Methods:

  • Compared collapsed cone (CC) convolution, XVMC MC, and analytical anisotropic algorithm (AAA) in Oncentra, iPlan/Monaco, and Eclipse TPS.
  • Used ionization chambers and radiochromic films for measurements in polystyrene and lung phantoms.
  • Validated single beams, conformal, and IMRT plans using absolute dosimetry and 1D/2D gamma-evaluations.

Main Results:

  • No significant differences in absolute dose between MC and kernel methods in homogeneous phantoms.
  • 1D gamma-evaluation revealed discrepancies in heterogeneous phantoms, with MC showing better results than AAA.
  • 2D gamma-evaluation of IMRT and four-field plans showed lower mean gamma values for MC (Monaco) compared to AAA.

Conclusions:

  • All investigated TPS demonstrated accurate dose calculations in various phantom types.
  • Commercially available TPS with MC options showed superior performance in heterogeneous phantoms.
  • The performance difference between CC and MC algorithms was found to be small.