Speedup Potential for Reconstruction Techniques for Prompt Gamma Imaging During Proton Radiotherapy
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Type of Work18 pages
undergraduate journal article
Citation of Original PublicationJames Della-Giustina, Johnlemuel Casilag, Elizabeth Gregorio, Aniebet Jacob, Carlos Barajas, Matthias K. Gobbert, Dennis S. Mackin, and Jerimy Polf, Speedup Potential for Reconstruction Techniques for Prompt Gamma Imaging During Proton Radiotherapy. American Journal on Undergraduate Research, vol. 14, iss. 4, pp. 22-37, 2018.
RightsThis item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
SubjectsProton beam radiation treatment
gamma ray imaging
Compton Camera (CC)
High performance computing
High Performance Computing Facilty (HPCF)
speeding up the gamma ray image reconstruction software
Proton beam radiation treatment was first proposed by Robert Wilson in 1946. The advantage of proton beam radiation is that the lethal dose of radiation is delivered by a sharp increase toward the end of the beam range. This sharp increase, known as the Bragg peak, allows for the possibility of reducing the exposure of healthy tissue to radiation when comparing to x-ray radiation treatment. As the proton beam interacts with the molecules in the body, gamma rays are emitted. The origin of the gamma rays gives the location of the proton beam in the body, therefore, gamma ray imaging allows physicians to better take advantage of the benefits of proton beam radiation. These gamma rays are detected using a Compton Camera (CC) while the SOE algorithm is used to reconstruct images of these gamma rays as they are emitted from the patient. This imaging occurs while the radiation dose is delivered, which would allow the physician to make adjustments in real time in the treatment room, provided the image reconstruction is computed fast enough. This project focuses on speeding up the image reconstruction software with the use of of parallel computing techniques involving MPI. Additionally, we demonstrate the use of the VTune performance analyzer to identify bottlenecks in a parallel code.