Monte Carlo Modeling of Dynamic Tumor Tracking on a Gimbaled Linear Accelerator.

Purpose and Aim: The Vero4DRT (Brainlab AG) linear accelerator is capable of dynamic tumor tracking (DTT) by panning/tilting the radiation beam to follow respiratory-induced tumor motion in real time. In this study, the panning/tilting motion is modeled in Monte Carlo (MC) for quality assurance (QA) of four-dimensional (4D) dose distributions created within the treatment planning system (TPS). Materials and Methods: Step-and-shoot intensity-modulated radiation therapy plans were optimized for 10 previously treated liver patients. These plans were recalculated on multiple phases of a 4D computed tomography (4DCT) scan using MC while modeling panning/tilting. The dose distributions on each phase were accumulated to create a respiratory-weighted 4D dose distribution. Differences between the TPS and MC modeled doses were examined. Results: On average, 4D dose calculations in MC showed the maximum dose of an organ at risk (OAR) to be 10% greater than the TPS' three-dimensional dose calculation (collapsed cone [CC] convolution algorithm) predicted. MC's 4D dose calculations showed that 6 out of 24 OARs could exceed their specified dose limits, and calculated their maximum dose to be 4% higher on average (up to 13%) than the TPS' 4D dose calculations. Dose differences between MC and the TPS were greatest in the beam penumbra region. Conclusion: Modeling panning/tilting for DTT has been successfully modeled with MC and is a useful tool to QA respiratory-correlated 4D dose distributions. The dose differences between the TPS and MC calculations highlight the importance of using 4D MC to confirm the safety of OAR doses before DTT treatments.

Monte Carlo based continuous aperture optimization for VMAT on conventional and MR-linacs: A proof of concept.

BACKGROUND: Currently, the commercial treatment planning systems for magnetic-resonance guided linear accelerators (MR-linacs) only support step-and-shoot intensity-modulated radiation therapy (IMRT). However, recent studies have shown the feasibility of delivering arc therapy on MR-linacs, which is expected to improve dose distributions and delivery speed. By accurately accounting for the electron return effect in the presence of a magnetic field, a Monte Carlo (MC) algorithm is ideally suited for the inverse treatment planning of this technique. PURPOSE: We propose a novel MC-based continuous aperture optimization (MCCAO) algorithm for volumetric modulated arc therapy (VMAT), including applications to VMAT on MR-linacs and trajectory-based VMAT. A unique feature of MCCAO is that the continuous character of gantry rotation and multileaf collimator (MLC) motion is accounted for at every stage of the optimization. METHODS: The optimization process uses a multistage simulation of 4D dose distribution. A phase space is scored at the top surface of the MLC and the energy deposition of each particle history is mapped to its position in this phase space. A progressive sampling method is used, where both MLC leaf positions and monitor unit (MU) weights are randomly changed, while respecting the linac mechanical limits. Due to the continuous nature of the leaf motion, such changes affect not only a single control point, but propagate to the adjacent ones as well, and the corresponding dose distribution changes are accounted for. A dose-volume cost function is used, which includes the MC statistical uncertainty. RESULTS: We applied our optimization technique to various treatment sites, using standard and flattening-filter-free (FFF) 6 MV beam models, with and without a 1.5 T magnetic field. MCCAO generates deliverable plans, whose dose distributions are in good agreement with measurements on ArcCHECK and stereotactic radiosurgery End-To-End Phantom. CONCLUSIONS: We show that the novel MCCAO method generates VMAT plans that meet clinical objectives for both conventional and MR-linacs.

Design and production of 3D printed bolus for electron radiation therapy.

This is a proof-of-concept study demonstrating the capacity for modulated electron radiation therapy (MERT) dose distributions using 3D printed bolus. Previous reports have involved bolus design using an electron pencil beam model and fabrication using a milling machine. In this study, an in-house algorithm is presented that optimizes the dose distribution with regard to dose coverage, conformity, and homogeneity within the planning target volume (PTV). The algorithm takes advantage of a commercial electron Monte Carlo dose calculation and uses the calculated result as input. Distances along ray lines from the distal side of 90% isodose line to distal surface of the PTV are used to estimate the bolus thickness. Inhomogeneities within the calculation volume are accounted for using the coefficient of equivalent thickness method. Several regional modulation operators are applied to improve the dose coverage and uniformity. The process is iterated (usually twice) until an acceptable MERT plan is realized, and the final bolus is printed using solid polylactic acid. The method is evaluated with regular geometric phantoms, anthropomorphic phantoms, and a clinical rhabdomyosarcoma pediatric case. In all cases the dose conformity are improved compared to that with uniform bolus. For geometric phantoms with air or bone inhomogeneities, the dose homogeneity is markedly improved. The actual printed boluses conform well to the surface of complex anthropomorphic phantoms. The correspondence of the dose distribution between the calculated synthetic bolus and the actual manufactured bolus is shown. For the rhabdomyosarcoma patient, the MERT plan yields a reduction of mean dose by 38.2% in left kidney relative to uniform bolus. MERT using 3D printed bolus appears to be a practical, low-cost approach to generating optimized bolus for electron therapy. The method is effective in improving conformity of the prescription isodose surface and in sparing immediately adjacent normal tissues.