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Jonas Ringholz1, Sonja Wegener1, Otto Andreas Sauer1

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A new pencil beam model accurately calculates equivalent squares for radiation therapy fields, outperforming older methods. This method is valid for all field shapes and is available as a user-friendly Python tool.

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Dosimetry

Background:

  • Accurate calculation of equivalent square fields is crucial for radiation therapy dose calculations.
  • Existing methods like the Sterling equation and geometric mean have limitations in their validity range.
  • Flattening Filter Free (FFF) beams are increasingly used, necessitating accurate dosimetry for these fields.

Purpose of the Study:

  • To develop and validate a physical pencil beam model for accurate equivalent square calculations.
  • To assess the method's performance across various definitions of equivalent squares, beam types (with/without flattening filter), photon energies, and depths.
  • To provide a versatile and accurate tool for clinical dosimetry.

Main Methods:

  • Utilized an analytical pencil beam model to integrate pencil beam kernels.
  • Defined equivalent squares based on equal dose at the beam axis and equal depth dose (Tissue Phantom Ratio at 20 and 10 cm).
  • Validated results against experimental measurements, Sterling equation, geometric mean, and published data (BJR Suppl. 25).

Main Results:

  • The pencil beam model demonstrated the highest accuracy compared to measurements.
  • Observed energy and depth dependencies for specific field configurations (small fields, elongated fields).
  • The method showed agreement with previous studies for Flattening Filter Free (FFF) to With Flattening Filter (WFF) square field assignments.

Conclusions:

  • The validity of the geometric mean and Sterling equation is limited; the pencil beam method offers broader applicability.
  • The developed pencil beam method is valid for all aspect ratios, including elongated fields, in the primary dose-dominated region.
  • The method is publicly available as a Python library and GUI, supporting quality assurance tasks like monitor unit checks.