The goal of this work was to facilitate the clinical use of Monte Carlo proton dose calculation to support routine treatment planning and delivery. The Monte Carlo code Geant4 was used to simulate the treatment head setup, including a time-dependent simulation of modulator wheels (for broad beam modulation) and magnetic field settings (for beam scanning). Any patient-field-specific setup can be modeled according to the treatment control system of the facility. The code was benchmarked against phantom measurements. Using a simulation of the ionization chamber reading in the treatment head allows the Monte Carlo dose to be specified in absolute units (Gy per ionization chamber reading). Next, the capability of reading CT data information was implemented into the Monte Carlo code to model patient anatomy. To allow time-efficient dose calculation, the standard Geant4 tracking algorithm was modified. Finally, a software link of the Monte Carlo dose engine to the patient database and the commercial planning system was established to allow data exchange, thus completing the implementation of the proton Monte Carlo dose calculation engine ('DoC++'). Monte Carlo re-calculated plans are a valuable tool to revisit decisions in the planning process. Identification of clinically significant differences between Monte Carlo and pencil-beam-based dose calculations may also drive improvements of current pencil-beam methods. As an example, four patients (29 fields in total) with tumors in the head and neck regions were analyzed. Differences between the pencil-beam algorithm and Monte Carlo were identified in particular near the end of range, both due to dose degradation and overall differences in range prediction due to bony anatomy in the beam path. Further, the Monte Carlo reports dose-to-tissue as compared to dose-to-water by the planning system. Our implementation is tailored to a specific Monte Carlo code and the treatment planning system XiO (Computerized Medical Systems Inc.). However, this work describes the general challenges and considerations when implementing proton Monte Carlo dose calculation in a clinical environment. The presented solutions can be easily adopted for other planning systems or other Monte Carlo codes.

Measurements show the proton eyeline meets the requirements to effectively treat ocular disease.

The reliable prediction of output factors for spread-out proton Bragg peak (SOBP) fields in clinical practice remained unrealized due to a lack of a consistent theoretical framework and the great number of variables introduced by the mechanical devices necessary for the production of such fields. These limitations necessitated an almost exclusive reliance on manual calibration for individual fields and empirical, ad hoc, models. We recently reported on a theoretical framework for the prediction of output factors for such fields. In this work, we describe the implementation of this framework in our clinical practice. In our practice, we use a treatment delivery nozzle that uses a limited, and constant, set of mechanical devices to produce SOBP fields over the full extent of clinical penetration depths, or ranges, and modulation widths. This use of a limited set of mechanical devices allows us to unfold the physical effects that affect the output factor. We describe these effects and their incorporation into the theoretical framework. We describe the calibration and protocol for SOBP fields, the effects of apertures and range-compensators and the use of output factors in the treatment planning process.

No abstract