NCSI: Optimizing Proton Beam Therapy through Analytical and Monte Carlo Methods

Status	Completed
Mentor Name	Colin Campbell
Mentor's XSEDE Affiliation	Research Allocation via the Ohio Supercomputer Center
Mentor Has Been in XSEDE Community	Less than 1 year
Project Title	Optimizing Proton Beam Therapy through Analytical and Monte Carlo Methods
Summary	Proton beam therapy is an attractive option compared to traditional X-ray beam therapy when treating deep-seated tumors because the depth in the body at which protons deposit most of their energy can be controlled, while X-ray absorption falls off sharply with depth. Monte Carlo simulations and analytical methods are used to predict energy deposition patterns, as (for example) Coulombic scattering and energy loss make the analysis of even a simple pencil beam highly complex. This project will train students in these methods in the context of optimizing coverage of simulated tumors.
Job Description	This project will train one student in computation and simulation in the context of proton beam therapy. As the position is intended to be formative for the student in terms of computational expertise and knowledge of core physics relevant to proton beam therapy and medical physics, the emphasis will be on the ground-up development of software rather than reliance on simulation environments commonly used in clinical settings. The student will work independently and with the mentor to first simulate multiple Coulomb scattering from a single proton moving through a regular atomic lattice. This implementation will be expanded and generalized in a Monte Carlo setting to capture the depth dose curve, then compared to analytical (e.g. mean field) predictions. The student will then turn to calculating the dose delivered to a simulated irregular tumor. Care will be taken to consider the effect of the proton beam width, energy, and position on dose and an optimization routine will be utilized to identify the most effective beam properties, including energy, width, and rotation pattern. The student will also be responsible for preparing a written report and technical poster summarizing the work undertaken during this project. This media will be submitted to the scholarly community through presentations at appropriate conferences and possibly disseminated through a research journal suitable for undergraduate work.
Computational Resources	The mentor has funding for high performance computing through the Ohio Supercomputer Center (osc.edu), which includes support for cluster computing, parallel processing, and GPU computing. Monte Carlo simulations, in particular, benefit significantly from parallel processing; the student will be trained in interfacing with these systems. XSEDE resources will be used for training, where appropriate.
Contribution to Community	As mentioned in the student job description, the student will be expected to report out on the results of the project through a technical report and scientific poster. The student will therefore be prepared to disseminate the results of this work to the scholarly community. In addition to institutional presentations, likely venues include the Ohio Supercomputer Center, which hosts research conferences for users, regional conferences for physics and computational science such as the Ohio Section of the American Physical Society (APS), and national conferences such as those hosted by APS, the American Association of Physics Teachers (AAPT), and/or the Radiological Society of North America (RSNA). Beyond dissemination of the scholarship generated by this project, the project will train a student researcher in Python, high performance computing, and simulation in the context of medical physics. In addition to these computational skills, the student will receive training in communicating the results of the research through technical writing and presentations. This training is directly related to XSEDE’s vision to support a “world of digitally enabled scholars, researchers, and engineers participating in multidisciplinary collaborations to tackle society’s grand challenges.”
Position Type	Learner
Training Plan	The project is proposed to take place during a summer, which allows for a ten-week experience. Notably, the student identified for this proposal is currently taking a course on computational science (in Python) with the mentor. The course features an intensive, individual project that will be used as a prelude for the proposed summer project. The student will therefore begin the summer appointment with a solid computational and theoretical foundation. The mentor will work closely with the student throughout the training period, with several hours of scheduled meetings each week and additional availability to assist the student when conceptual and/or computational roadblocks are encountered. Training resources will be developed by the mentor, adapted from the primary literature, and employed from resources including XSEDE and the Ohio Supercomputer Center. To meet the ambitious goals laid out in the student job description, the student will be kept to the following timeline: Week 1: Implementation of single proton Coulomb scattering through a lattice. Weeks 2-4: Generalization to multiple protons sampled from a single beam; inclusion of effects leading to the depth dose curve and the so-called “spread out Bragg peak.” Weeks 5: Implementation of analytical solutions (e.g. the Bragg-Kleeman formula in three dimensions) and comparison to Monte Carlo Methods. Weeks 6-9: Optimization of treatment of a simulated tumor in three dimensions. Week 10: Conclusion; wrap up and development of report and poster.
Student Prerequisites/Conditions/Qualifications
Duration	Summer
Start Date	06/01/2021
End Date	08/10/2021