High density scintillating glass proton imaging detector

Wilkinson, Collin J.; Goranson, K.; Turney, A.; Xie, Qiangqiang; Tillman, I. J.; Thune, Z.; Dong, Anhua; Pritchett, Donald G.; McInally, W.; Potter, A.; Wang, D.; Akgun, U.

doi:10.1117/12.2252777

Cited by 2 publications

(6 citation statements)

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“…The absolute quantum yield for the total integrated emission within 450 nm and 650 nm was determined to be 30%. The PL spectrum of the Eu doped scintillating glass is very similar to our previous report [11]; weak emission bands at 591 nm, 653 nm, and 700 nm, were observed, with peak emission at 612 nm (see figure 1 Right). The PL measurements showed that 6% Eu and 4% Tb doped glasses yield more scintillation light than the other samples, hence these samples were taken to the beam tests.…”

Section: Developing the Boron-10 Enriched Glasssupporting

confidence: 86%

“…This shows that Cerium-doped Borosilicate glasses, even with natural Boron, can be effectively used for neutron detection [3,4]. This also allows for the potential usage of other high efficiency scintillators such as Terbium and Europium [11]. Since these initial results, many of the following studies in the area have focused on 6 Li as the neutron-capture mechanism due to the significant α-particle peak in 10 B events.…”

Section: Introductionmentioning

confidence: 96%

“…Binary information is available in most modern detectors but they are typically orientation-sensitive [10]. Designs with layered bar architectures have also been shown to be effective methods for the detection and track reconstruction of charged particles [11]. In this particular case, due to the few secondary particles that also remain highly localized within the detector traditional track, reconstruction is relatively difficult.…”

Section: Introductionmentioning

confidence: 99%

“…The following sections give detailed descriptions of the glass making conditions, testing of the optical properties, and testing of the neutron capturing abilities of these glasses. The report also summarizes the Geant4 [16,17] simulations performed on the layered bar architecture [11] and the machine learning studies based on this unique detector design.…”

Section: Introductionmentioning

confidence: 99%

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A glass neutron detector with machine learning capabilities

et al. 2019

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The detection of neutrons above background levels is an indication of nuclear materials, creating significant applications for handheld neutron detectors in homeland security. For such applications, a 10B and 6Li enriched, scintillating glass neutron detector was designed. The model is compact enough to be used as a handheld detector and is equipped with machine learning capabilities to determine the location of the source and discriminate a neutron from a gamma. Lithium Borosilicate glass samples, with up to 70% 10B and 6Li content, and doped with Tb and Eu, were engineered to optimize the performance of the detector. The scintillation properties and neutron/gamma detection capabilities of the glass samples were tested. The model detector's performance was simulated in Geant4 and the simulation data was utilized for machine learning to predict the location of the source with an Artificial Neural Network (ANN). The reported handheld neutron detector design with the implemented artificial intelligence capability can achieve 99.9% accuracy in neutron/gamma discrimination, 8.9% error in radial angle estimates, and 2.9% error in azimuthal angle estimates.

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Section: Developing the Boron-10 Enriched Glasssupporting

confidence: 86%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A glass neutron detector with machine learning capabilities

et al. 2019

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“…Whether new detector technology, e.g. high density scintillating glass (Wilkinson et al, 2017), might be suitable for this purpose and what accuracy is to be expected would require a dedicated study. Regardless of the contrast mechanism, integrated-mode scanners provide lower spatial resolution than list-mode scanners because reconstruction can only be performed along the average MLP of all particles in one beam and not each proton's MLP individually (Krah et al, 2018a).…”

Section: Discussionmentioning

confidence: 99%

Scattering proton CT

et al. 2020

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Proton computed tomography (CT) is an imaging modality investigated mainly in the context of proton therapy as a complement to x-ray CT. It uses protons with high enough energy to fully traverse the imaged object. Common prototype systems measure each proton's position and direction upstream and downstream of the object as well as the energy loss which can be converted into the water equivalent thickness. A reconstruction algorithm then produces a map of the relative stopping power in the object. As an alternative to energy-loss proton CT, it has been proposed to reconstruct a map of the object's scattering power based on the protons' angular dispersion which can be estimated from the measured directions. As in energy-loss proton CT, reconstruction should best be performed considering the non-linear shape of proton trajectories due to multiple Coulomb scattering, but no algorithm to achieve this is so far available in the literature. In this work, we propose a filtered backprojection algorithm with distance-driven binning to account for the protons' most likely path. Furthermore, we present a systematic study of scattering proton CT in terms of inherent noise and spatial resolution and study the artefacts which arise from the physics of multiple Coulomb scattering. Our analysis is partly based on analytical models and partly on Monte Carlo simulations. Our results show that the proposed algorithm performs well in reconstructing relative scattering power maps, i.e. scattering power relative to that of water. Spatial resolution is improved by almost a factor of three compared to straight line projection and is comparable to energy-loss proton CT. Image noise, on the other hand, is inherently much higher. For example, in a water cylinder of 20 cm diameter, representative of a human head, noise in the central image pixel is about 40 times higher in scattering proton CT than in energy-loss proton CT. Relative scattering power in dense regions such as bone inserts is systematically underestimated by a few percent, depending on beam energy and phantom geometry.

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High density scintillating glass proton imaging detector

Cited by 2 publications

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A glass neutron detector with machine learning capabilities

A glass neutron detector with machine learning capabilities

Scattering proton CT

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