Browsing by Author "Maggi, Paul"
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Item Deep Learning for Classification of Compton Camera Data in the Reconstruction of Proton Beams in Cancer Treatment(UMBC, 2020-06-12) Basalyga, Jonathan N.; Barajas, Carlos A.; Gobbert, Matthias K.; Maggi, Paul; Polf, JerimyReal-time imaging has potential to greatly increase the effectiveness of proton beam therapy in cancer treatment. One promising method of real-time imaging is the use of a Compton camera to detect prompt gamma rays, which are emitted by the beam, in order to reconstruct their origin. However, because of limitations in the Compton camera’s ability to detect prompt gammas, the data are often ambiguous, making reconstructions based on them unusable for practical purposes. Deep learning’s ability to detect subtleties in data that traditional models do not use make it one possible candidate for the improvement of classification of Compton camera data. We show that a suitably designed neural network can reduce false detections and misorderings of interactions, thereby improving reconstruction qualityItem Improvements to the Deep Learning Classification of Compton Camera Based Prompt Gamma Imaging for Proton Radiotherapy(2020-05) Basalyga, Jonathan N.; Barajas, Carlos A.; Kroiz, Gerson C.; Gobbert, Matthias K.; Maggi, Paul; Polf, JerimyReal-time imaging has potential to greatly increase the effectiveness of proton beam therapy for cancer treatment. One promising method of real-time imaging is the use of a Compton camera to detect prompt gamma rays, which are emitted along the path of the beam, in order to reconstruct their origin. However, because of limitations in the Compton camera’s ability to detect prompt gammas, the data are often ambiguous, making reconstructions based on them unusable for practical purposes. Deep learning’s ability to detect subtleties in data that traditional models do not use make it one possible candidate for the improvement of classification of Compton camera data. The base network can be made cheaper via reducing hidden layer count while maintaining comparable classification performance. Additionally, even a simple training schedule can show improvements in the training process. Several variations of the network showed promise in their ability to classify multiple beam energies. However more improvements need to be made to the network for the performance on multiple beam energies to meet our goal of 90% classification accuracy.Item A Study of The Clinical Viability of A Prototype Compton Camera for Prompt Gamma Imaging Based Proton Beam Range Verification(AAPM, 2021-06-25) Polf, Jerimy; Barajas, Carlos A.; Kroiz, Gerson C.; Peterson, Stephen W.; Maggi, Paul; Mackin, Dennis S.; Beddar, Sam; Gobbert, MatthiasWe present Compton camera (CC) based PG imaging for proton range verification at clinical dose rates. PG emission from a tissue-equivalent phantom during irradiation with clinical proton beams was measured with a prototype CC. Images were reconstructed of the raw measured data and of data processed with a neural network (NN) trained to identify “true” and “false” PG events. From these images, we determine if PG images produced by the prototype CC could provide clinically useful information about the in vivo range of the proton beams delivered during proton beam radiotherapy. NN processing of the data was found necessary to allow identification of the proton beam path from the PG images. Furthermore, to allow the localization of the end of the proton beam range with a precision of ≤ 3mm with the prototype CC, ~1 x 10⁹ protons would need to be delivered, which is on the order of magnitude delivered for a standard proton radiotherapy treatment field. To obtain higher precision in beam range determination and to allow imaging a single proton pencil beam delivered within the full treatment field, further improvements in PG detection rates by the CC, NN data processing, and image reconstruction algorithms are needed.Item Use of Deep Learning to Classify Compton Camera Based Prompt Gamma Imaging for Proton Radiotherapy(UMBC) Basalyga, Jonathan N.; Kroiz, Gerson C.; Barajas, Carlos A.; Gobbert, Matthias K.; Maggi, Paul; Polf, JerimyReal-time imaging has potential to greatly increase the effectiveness of proton beam therapy for cancer treatment. One promising method of real-time imaging is the use of a Compton camera to detect prompt gamma rays, which are emitted along the path of the beam, in order to reconstruct their origin. However, because of limitations in the Compton camera’s ability to detect prompt gammas, the data are often ambiguous, making reconstructions based on them unusable for practical purposes. Deep learning’s ability to detect subtleties in data that traditional models do not use make it one possible candidate for the improvement of classification of Compton camera data. We show that a suitably designed neural network can reduce false detections and misorderings of interactions, thereby improving reconstruction quality.