1. J.S. Loeffler, M. Durante, Charged particle therapy--optimization, challenges and future directions. Nature Reviews Clinical Oncology, 10(7), 411-424 (2013).
2. E. Pedroni, et al, Initial experience of using an active beam delivery technique at PSI, Strahlentherapie Und Onkologie, 175(2), 18-20 (1999).
3. D. Nichiporov, et al, Range shift and dose perturbation with high-density materials in proton beam therapy, Nuclear Instruments & Methods in Physics Research Section B-beam Interactions With Materials and Atoms, 269(22), 2685-2692 (2011).
4. A. Bijan, et al, Verification of patient-specific dose distributions in proton therapy using a commercial two-dimensional ion chamber array, Medical Physics, 37(11), 5831-5837 (2010).
5. X.R. Zhu, et al, Patient-specific Quality Assurance for Prostate Cancer Patients Receiving Spot Scanning Proton Therapy Using Single-Field Uniform Dose, International Journal of Radiation Oncology Biology Physics, 81(2), 552-559 (2011).
6. G. Battistoni, et al, The FLUKA Code: An Accurate Simulation Tool for Particle Therapy, Frontiers in Oncology, 6(2) (2016).
7. A. Ferrari, et al, FLUKA: a multi-particle transport code, CERN, Geneva Report No. CERN-2005-10 (2005).
8. L.S. Walter, Monte Carlo N-Particle Transport Code System for Multiparticle and High Energy Applications, Version 2.5.d (2003).
9. J. Allison, Geant4 developments and applications, IEEE Transactions on Nuclear Science, 53(1), 270-278 (2006).
10. J. Perl, TOPAS: an innovative proton Monte Carlo platform for research and clinical applications, Medical Physics, 39(11), 6818-6837 (2012).
11. W. Newhauser, et al, Monte Carlo simulations for configuring and testing an analytical proton dose-calculation algorithm, Physics in Medicine & Biology, 52(15), 4569-4584 (2007).
12. H. Paganetti, Monte Carlo method to study the proton fluence for treatment planning, Medical Physics, 25(12), 2370-2375 (1998).
13. H. Paganetti, Monte Carlo calculations for absolute dosimetry to determine machine outputs for proton therapy fields, Physics in Medicine & Biology, 51(11), 2801-2812 (2006).
14. H. Paganetti, et al, Accurate Monte Carlo simulations for nozzle design, commissioning and quality assurance for a proton radiation therapy facility, Medical Physics, 31(7), 2107-2118 (2004).
15. K. Parodi, et al, Monte Carlo simulations to support start-up and treatment planning of scanned proton and carbon ion therapy at a synchrotron-based facility, Physics in Medicine & Biology, 57(12), 3759-3784 (2012).
16. E. Almhagen, et al, A beam model for focused proton pencil beams, Physica Medica, 52, 27-32 (2018).
17. Adam H. Aitkenhead, et al., Automated Monte-Carlo re-calculation of proton therapy plans using Geant4/Gate: implementation and comparison to plan-specific quality assurance measurements, The British Journal of Radiology, 93, 1114 (2020): 20200228.
18. L. Grevillot, A Monte Carlo pencil beam scanning model for proton treatment plan simulation using GATE/GEANT4, Physics in Medicine & Biology, 56(16), 5203-5219 (2011).
19. H. Shu, Scanned Proton Beam Performance and Calibration of the Shanghai Advanced Proton Therapy Facility, MethodsX, 6, 1933-1943 (2019).
20. S. Jan, GATE V6: a major enhancement of the GATE simulation platform enabling modelling of CT and radiotherapy, Physics in Medicine & Biology, 56(4), 881 (2011).
21. Geant4-Website 2012b http://geant4.cern.ch/
22. I. Pshenichnov, I. Mishustin, W. Greiner, Distributions of positron-emitting nuclei in proton and carbon-ion therapy studied with GEANT4, Physics in Medicine & Biology, 51(23), 6099 (2006).
23. E. Seravalli, et al, Monte Carlo calculations of positron emitter yields in proton radiotherapy, Physics in Medicine & Biology, 57(6), 1659 (2012).
24. Sheng, Yinxiangzi, et al., Development of a Monte Carlo beam model for raster scanning proton beams and dosimetric comparison, International Journal of Radiation Biology, 96.11, 1435-1442 (2020)