title: Clinical implementation of full Monte Carlo dose calculation in proton beam therapy.
abstract: 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.
authors.affiliation: Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
authors.forename: Harald
authors.initials: H
authors.lastname: Paganetti
authors.forename: Hongyu
authors.initials: H
authors.lastname: Jiang
authors.forename: Katia
authors.initials: K
authors.lastname: Parodi
authors.forename: Roelf
authors.initials: R
authors.lastname: Slopsema
authors.forename: Martijn
authors.initials: M
authors.lastname: Engelsman
mesh_terms: D000465:Algorithms; D001254:Astrocytoma / Q000532:radiotherapy; D002817:Chordoma / Q000532:radiotherapy; D006801:Humans; D008280:Magnetics; D015233:Models, Statistical; D009010:Monte Carlo Method; D009303:Nasopharyngeal Neoplasms / Q000532:radiotherapy; D019047:Phantoms, Imaging; D061766:Proton Therapy; D011874:Radiometry / Q000379:methods; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D013101:Sphenoid Sinus; D013120:Spinal Cord Neoplasms / Q000532:radiotherapy; D011522:Protons
chemical_list: D011522:Protons