Project Details
Abstract
Proton therapy is a promising modality in clinical radiation oncology due to their well-defined
range and favorable depth dose characteristics. However, because of the steep dose gradient at the
distal edge of the Bragg peak, uncertainties in the determination of this range have a profound
impact on the actually applied dose distribution. Therefore, tools to monitor and control these
uncertainties are therefore highly desirable to optimize the proton therapy on an individual level for
fully exploiting the superior characteristic of proton beams. During the nuclear interaction between
the proton beam and tissues, the elements within the patient were activated and transformed to the
positron emitters. Positron emission tomography (PET) has been suggested as a promising tool for
verifying the delivery location of the planned dose by imaging the proton-activated β+ isotope. The
central goal of the project is to develop a PET-based range verification tool for proton therapy by
leveraging the artificial intelligence and deep learning method. Three sets of studies are going to be
performed. First, to establish a GATE/GEANT4 Monte Carlo simulation platform towards imaging
in proton therapy. Based on this platform, the relationships between proton dose and positron
emitters are modeled using the deep learning method. Second, a novel proton dose reconstruction
algorithm will be developed and verified with physical phantom experiments. Third, to construct a
visualizable verification tool for proton range by integrating the developed deep learning model and
dose reconstruction algorithm. In order to mimic a realistic scenario in patients receive proton
therapy and PET imaging, an anthropomorphic phantom will be used to validate and evaluate the
developed tool. Overall, the new physical experiment data and methodological outputs of the
project will provide with a rigorous frame and efficient tools that are like to have a strong impact on
particle therapy as a new proton range verification approach.
Project IDs
Project ID:PC10701-1157
External Project ID:MOST106-2314-B182-062-MY2
External Project ID:MOST106-2314-B182-062-MY2
Status | Finished |
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Effective start/end date | 01/08/18 → 31/07/19 |
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