A source model for modulated electron radiation therapy using dynamic jaw movements

The development of fast and accurate source models (SMs) might be of crucial importance for the future clinical implementation of modulated electron radiation therapy (MERT). In this study, a SM is presented for reconstructing phase-space information of modulated electron beams using a few-leaf electron collimator (FLEC) and the photon jaws.

During a FLEC-based delivery, two collimation devices (jaws and FLEC) modulate the electron beam characteristics dynamically. The SM separates the beam into a primary and a scattered component. The primary component is derived by a fast Monte Carlo (MC) transport calculation in air using the EGSnrc/BEAMnrc code. The scattered beam is modeled analytically. The accelerator was decomposed into its individual leaf components and the scattered beam was characterized at various levels of the accelerator. Scattered particles are assigned an energy and position by sampling pre-calculated probability distributions. The direction is estimated by geometrical arguments. Particles were assumed to emerge from tunable virtual sources on the side of each collimator leaf. A leaf-hit algorithm was developed to dynamically reject particles that are incident on any collimating leaf. Electron transport in air between the two collimation levels was calculated based on a MC-modified version of the Fermi-Eyges scattering theory. Correlations between direction and position were observed and taken into account at the final collimation level.

To validate the model, reconstructed phase-space data were compared with the full accelerator MC phase-space data. The model accurately reproduced the beam characteristics and preserved important correlations. Depth and profile dose distributions in water were derived for square, rectangular, and off-axis field sizes and for a range of clinical energies. Discrepancies in the dose distributions and dose output were within 3% in all cases.

Fast and accurate SMs open the possibility for fast treatment planning in MERT, based on an inverse optimization MC treatment planning scheme.