Towards real-time EPID-based 3D in vivo dosimetry for IMRT with Deep Neural Networks: A feasibility study.

We investigate the potential of the Deep Dose Estimate (DDE) neural network to predict 3D dose distributions inside patients with Monte Carlo (MC) accuracy, based on transmitted EPID signals and patient CTs. The network was trained using as input patient CTs and first-order dose approximations (FOD). Accurate dose distributions (ADD) simulated with MC were given as training targets.

Optimization of Phase Space files from clinical linear accelerators.

This work proposes a methodology to produce an optimized phase-space (PhSp) for the Elekta Synergy linac by tuning the energy and direction of particles inside the 6-MV Elekta Precise PhSp, provided by the International Atomic Energy Agency (IAEA), for Monte Carlo (MC) simulations. First, the energies of the particles emerging from the original PhSp were increased by different factors, producing new PhSps. Percentage depth dose (PDD) profiles were simulated and compared to measured data from a Synergy linac for 6-MV photon beam.

A novel approach to EPID-based 3D volumetric dosimetry for IMRT and VMAT QA.

Intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) are relatively complex treatment delivery techniques and require quality assurance (QA) procedures. Pre-treatment dosimetric verification represents a fundamental QA procedure in daily clinical routine in radiation therapy. The purpose of this study is to develop an EPID-based approach to reconstruct a 3D dose distribution as imparted to a virtual cylindrical water phantom to be used for plan-specific pre-treatment dosimetric verification for IMRT and VMAT plans.