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Feasibility Studies of a New Event Selection Method to Improve Spatial Resolution of Compton Imaging for Medical Applications.

ABSTRACT: A Compton imaging method for medical applications that includes new energy determination and data filtering techniques has been tested using several point sources with known emission lines. Using a prototype Compton camera, a distance-of-closest approach technique has been employed to determine the initial energy of the incoming ?s and to ensure the reconstructed source position is within an acceptable distance from the known ? source location. Further analysis is done by implementing a Compton line filtering technique, keeping only those interactions whose deposited energy in the first interaction matches the theoretical energy deposition predicted by the Compton equation. Using this new event filtering method, we see improvements in the full width at half maximum in the lateral profiles of ? point sources of up to 70% over standard Compton imaging methods, as well as achievable spatial resolutions in the reconstructed images of better than 2 mm. In addition, this new Compton imaging method was able to reconstruct an extended source of ? rays emitted during irradiation of a water tank with a clinical proton radiotherapy beam.

SUBMITTER: Draeger E

PROVIDER: S-EPMC5515251 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Feasibility Studies of a New Event Selection Method to Improve Spatial Resolution of Compton Imaging for Medical Applications.

Draeger E E Peterson S S Mackin D D Chen H H Beddar S S Polf J C JC

IEEE transactions on radiation and plasma medical sciences 20170510 4

A Compton imaging method for medical applications that includes new energy determination and data filtering techniques has been tested using several point sources with known emission lines. Using a prototype Compton camera, a distance-of-closest approach technique has been employed to determine the initial energy of the incoming <i>γ</i>s and to ensure the reconstructed source position is within an acceptable distance from the known <i>γ</i> source location. Further analysis is done by implement ...[more]

PMID: 28736766

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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.

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Dataset Information

Feasibility Studies of a New Event Selection Method to Improve Spatial Resolution of Compton Imaging for Medical Applications.

Publications

Feasibility Studies of a New Event Selection Method to Improve Spatial Resolution of Compton Imaging for Medical Applications.

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