A fluence-convolution method to calculate radiation therapy dose distributions that incorporate random set-up error

Area of Science:

• Medical Physics
• Radiation Oncology
• Computational Imaging

Background:

• The International Commission on Radiation Units and Measurements Report 62 (ICRU 1999) defined planning target volume (PTV) expansion from clinical target volume (CTV) using internal and set-up margins.
• Set-up margins (SM) account for patient positioning uncertainties, including systematic and random errors, crucial for accurate radiation dose delivery.
• Current methods for incorporating random set-up errors in treatment planning may not fully capture the complexities of dose calculation in heterogeneous media.

Purpose of the Study:

• To propose and implement a novel fluence-convolution method to replace the random set-up error component in radiation therapy planning.
• To compare the accuracy of the proposed fluence-convolution method against a dose-matrix-convolution algorithm, particularly in heterogeneous phantom calculations.
• To evaluate the impact of incorporating random set-up errors directly into the dose calculation model.

Main Methods:

• Developed and implemented a fluence-convolution method within a Monte Carlo (MC) based treatment-planning system.
• Incorporated random set-up errors by convolving incident photon beam fluence with a Gaussian set-up error kernel.
• Compared results with a dose-matrix-convolution algorithm, evaluating dose profiles in homogeneous and heterogeneous phantom scenarios.

Main Results:

• Fluence-convolution and dose-matrix-convolution methods showed agreement in homogeneous media.
• Discrepancies of up to 5% in dose profiles were observed between the two methods in heterogeneous phantoms with a 0.4 cm set-up error.
• Fluence-convolution accurately predicted dose perturbations at interfaces in heterogeneous media, mimicking reality more closely.

Conclusions:

• The proposed fluence-convolution method offers a more realistic representation of dose distributions by decoupling treatment beams from the patient.
• This method accurately accounts for random set-up errors and dose perturbations at tissue interfaces, improving treatment planning accuracy.
• Fluence-convolution is readily applicable to convolution/superposition-based dose calculation algorithms in radiation oncology.