An inception network for positron emission tomography based dose estimation in carbon ion therapy

Simple item page
dc.contributor.authorRutherford, Hen_AU
dc.contributor.authorTurai, RSen_AU
dc.contributor.authorChacon, Aen_AU
dc.contributor.authorFranklin, DRen_AU
dc.contributor.authorMohammadi, Aen_AU
dc.contributor.authorTashima, Hen_AU
dc.contributor.authorYamaya, Ten_AU
dc.contributor.authorParodi, Ken_AU
dc.contributor.authorRosenfeld, ABen_AU
dc.contributor.authorGuatelli, Sen_AU
dc.contributor.authorSafavi-Naeini, Men_AU
dc.date.accessioned2025-01-10T04:40:11Zen_AU
dc.date.available2025-01-10T04:40:11Zen_AU
dc.date.issued2022-09-23en_AU
dc.date.statistics2024-10-23en_AU
dc.description.abstractObjective. We aim to evaluate a method for estimating 1D physical dose deposition profiles in carbon ion therapy via analysis of dynamic PET images using a deep residual learning convolutional neural network (CNN). The method is validated using Monte Carlo simulations of 12C ion spread-out Bragg peak (SOBP) profiles, and demonstrated with an experimental PET image. Approach. A set of dose deposition and positron annihilation profiles for monoenergetic 12C ion pencil beams in PMMA are first generated using Monte Carlo simulations. From these, a set of random polyenergetic dose and positron annihilation profiles are synthesised and used to train the CNN. Performance is evaluated by generating a second set of simulated 12C ion SOBP profiles (one 116 mm SOBP profile and ten 60 mm SOBP profiles), and using the trained neural network to estimate the dose profile deposited by each beam and the position of the distal edge of the SOBP. Next, the same methods are used to evaluate the network using an experimental PET image, obtained after irradiating a PMMA phantom with a 12C ion beam at QST’s Heavy Ion Medical Accelerator in Chiba facility in Chiba, Japan. The performance of the CNN is compared to that of a recently published iterative technique using the same simulated and experimental 12C SOBP profiles. Main results. The CNN estimated the simulated dose profiles with a mean relative error (MRE) of 0.7% ± 1.0% and the distal edge position with an accuracy of 0.1 mm ± 0.2 mm, and estimate the dose delivered by the experimental 12C ion beam with a MRE of 3.7%, and the distal edge with an accuracy of 1.7 mm. Significance. The CNN was able to produce estimates of the dose distribution with comparable or improved accuracy and computational efficiency compared to the iterative method and other similar PET-based direct dose quantification techniques. © 2022 Institute of Physics and Engineering in Medicine.en_AU
dc.description.sponsorshipThis research is supported by the Australian Government Research Training Program (AGRTP) scholarship. The authors would like to thank AINSE Limited for providing financial assistance (Awards—Residential Student Scholarship 2018 and Honours Scholarship 2021) to enable work on this research. This research was undertaken with the assistance of resources from the National Computational Infrastructure (NCI Australia), an NCRIS enabled capability supported by the Australian Government. This work was supported by the Multi-modal Australian ScienceS Imaging and Visualisation Environment (MASSIVE). This work was performed as part of the Research Project with Heavy Ions at QST-HIMAC.en_AU
dc.format.mediumElectronicen_AU
dc.identifier.articlenumber194001en_AU
dc.identifier.citationRutherford, H., Saha Turai, R., Chacon, A., Franklin, D. R., Mohammadi, A., Tashima, H., Yamaya, T., Parodi, K., Rosenfeld, A. B., Guatelli, S., & Safavi-Naeini, M. (2022). An inception network for positron emission tomography based dose estimation in carbon ion therapy. Physics in Medicine & Biology, 67(19), 194001. doi:10.1088/1361-6560/ac88b2en_AU
dc.identifier.issn0031-9155en_AU
dc.identifier.issn1361-6560en_AU
dc.identifier.issue19en_AU
dc.identifier.journaltitlePhysics in Medicine and Biologyen_AU
dc.identifier.urihttps://doi.org/10.1088/1361-6560/ac88b2en_AU
dc.identifier.urihttps://apo.ansto.gov.au/handle/10238/15903en_AU
dc.identifier.volume67en_AU
dc.languageengen_AU
dc.language.isoenen_AU
dc.publisherIOP Publishingen_AU
dc.subjectTomographyen_AU
dc.subjectDosesen_AU
dc.subjectCarbonen_AU
dc.subjectIonsen_AU
dc.subjectTherapyen_AU
dc.subjectNeural networksen_AU
dc.subjectMonte Carlo Methoden_AU
dc.subjectSimulationen_AU
dc.subjectPositron computed tomographyen_AU
dc.titleAn inception network for positron emission tomography based dose estimation in carbon ion therapyen_AU
dc.typeJournal Articleen_AU
dcterms.dateAccepted2022-08-10en_AU
Files
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.66 KB
Format:
Plain Text
Description:
Download
Collections
Journal Articles