Dataset Information

Carbon range verification with 718 keV Compton imaging.

ABSTRACT: Carbon ion radiotherapy is a sophisticated radiation treatment modality because of its superiority in achieving precise dosage distribution and high biological effectiveness. However, there exist beam range uncertainties that affect treatment efficiency. This problem can be resolved if the clinical beam could be monitored precisely in real-time, such as by imaging the prompt gamma emission from the target. In this study, we performed real-time detection and imaging of 718 keV prompt gamma emissions using a Si/CdTe Compton camera. We conducted experiments on graphite phantoms using clinical carbon ion beams of 290 MeV/u energy. Compton images were reconstructed using simple back-projection methods from the energy events of 718 keV prompt gamma emissions. The peak intensity position in reconstructed 718 keV prompt gamma images was few millimeters below the Bragg peak position. Moreover, the dual- and triple-energy window images for all positions of phantoms were not affected by scattered gammas, and their peak intensity positions were approximately similar to those observed in the reconstructed 718 keV prompt gamma images. In conclusion, the findings of the current study demonstrate the feasibility of using our Compton camera for real-time beam monitoring of carbon ion beams under clinical beam intensity.

SUBMITTER: Parajuli RK

PROVIDER: S-EPMC8569035 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Carbon range verification with 718 keV Compton imaging.

Parajuli Raj Kumar RK Sakai Makoto M Arakawa Kazuo K Kubota Yoshiki Y Kubo Nobuteru N Tashiro Mutsumi M

Scientific reports 20211104 1

Carbon ion radiotherapy is a sophisticated radiation treatment modality because of its superiority in achieving precise dosage distribution and high biological effectiveness. However, there exist beam range uncertainties that affect treatment efficiency. This problem can be resolved if the clinical beam could be monitored precisely in real-time, such as by imaging the prompt gamma emission from the target. In this study, we performed real-time detection and imaging of 718 keV prompt gamma emissi ...[more]

PMID: 34737390

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Project description:We studied the application of a deep, fully connected Neural Network (NN) to process prompt gamma (PG) data measured by a Compton camera (CC) during the delivery of clinical proton radiotherapy beams. The network identifies 1) recorded "bad" PG events arising from background noise during the measurement, and 2) the correct ordering of PG interactions in the CC to help improve the fidelity of "good" data used for image reconstruction. PG emission from a tissue-equivalent target during irradiation with a 150 MeV proton beam delivered at clinical dose rates was measured with a prototype CC. Images were reconstructed from both the raw measured data and the measured data that was further processed with a neural network (NN) trained to identify "good" and "bad" PG events and predict the ordering of individual interactions within the good PG events. We determine if NN processing of the CC data could improve the reconstructed PG images to a level in which they could provide clinically useful information about the in vivo range and range shifts of the proton beams delivered at full clinical dose rates. Results showed that a deep, fully connected NN improved the achievable contrast to noise ratio (CNR) in our images by more than a factor of 8x. This allowed the path, range, and lateral width of the clinical proton beam within a tissue equivalent target to easily be identified from the PG images, even at the highest dose rates of a 150 MeV proton beam used for clinical treatments. On average, shifts in the beam range as small as 3 mm could be identified. However, when limited by the amount of PG data measured with our prototype CC during the delivery of a single proton pencil beam (~1 × 109 protons), the uncertainty in the reconstructed PG images limited the identification of range shift to ~5 mm. Substantial improvements in CC images were obtained during clinical beam delivery through NN pre-processing of the measured PG data. We believe this shows the potential of NNs to help improve and push CC-based PG imaging toward eventual clinical application for proton RT treatment delivery verification.

Dataset Information

Carbon range verification with 718 keV Compton imaging.

Publications

Carbon range verification with 718 keV Compton imaging.

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