Standoff 3D Gamma-Ray Imaging

Mihailescu, Lucian; Vetter, K.; Chivers, Daniel H.

doi:10.1109/tns.2009.2015304

Cited by 37 publications

(17 citation statements)

References 10 publications

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“…1. These sensors in combination with algorithms enable the reconstruction of the scene, the tracking of the position and orientation of the imager in the scene, and the reconstruction of the gamma-ray image in this scene in 3D and in real time [1][2][3].…”

Section: Methodsmentioning

confidence: 99%

Advanced Concepts in Multi-Dimensional Radiation Detection and Imaging

Vetter

Haefner

Barnowski

et al. 2016

Proceedings of International Symposium on Radiation Detectors and Their Uses (ISRD2016)

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Recent developments in the detector fabrication, signal readout, and data processing enable new concepts in radiation detection that are relevant for applications ranging from fundamental physics to medicine as well as nuclear security and safety. We present recent progress in multi-dimensional radiation detection and imaging in the Berkeley Applied Nuclear Physics program. It is based on the ability to reconstruct scenes in three dimensions and fuse it with gamma-ray image information. We are using the High-Efficiency Multimode Imager HEMI in its Compton imaging mode and combining it with contextual sensors such as the Microsoft Kinect or visual cameras. This new concept of volumetric imaging or scene data fusion provides unprecedented capabilities in radiation detection and imaging relevant for the detection and mapping of radiological and nuclear materials. This concept brings us one step closer to the seeing the world with gamma-ray eyes.

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Section: Methodsmentioning

confidence: 99%

Advanced Concepts in Multi-Dimensional Radiation Detection and Imaging

Vetter

Haefner

Barnowski

et al. 2016

Proceedings of International Symposium on Radiation Detectors and Their Uses (ISRD2016)

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“…This was demonstrated in [28] using a Compton camera and a 3D Design Information Verification system consisting mainly of a LiDAR system. First, the 3D data from the LiDAR were used to build a sparse 3D image space model divided into voxels.…”

Section: Integration Of Radiological and Contextual Sensorsmentioning

confidence: 99%

Ground Penetrating Radar as a Contextual Sensor for Multi-Sensor Radiological Characterisation

Ukaegbu

Gamage

2017

Sensors

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Radioactive sources exist in environments or contexts that influence how they are detected and localised. For instance, the context of a moving source is different from a stationary source because of the effects of motion. The need to incorporate this contextual information in the radiation detection and localisation process has necessitated the integration of radiological and contextual sensors. The benefits of the successful integration of both types of sensors is well known and widely reported in fields such as medical imaging. However, the integration of both types of sensors has also led to innovative solutions to challenges in characterising radioactive sources in non-medical applications. This paper presents a review of such recent applications. It also identifies that these applications mostly use visual sensors as contextual sensors for characterising radiation sources. However, visual sensors cannot retrieve contextual information about radioactive wastes located in opaque environments encountered at nuclear sites, e.g., underground contamination. Consequently, this paper also examines ground-penetrating radar (GPR) as a contextual sensor for characterising this category of wastes and proposes several ways of integrating data from GPR and radiological sensors. Finally, it demonstrates combined GPR and radiation imaging for three-dimensional localisation of contamination in underground pipes using radiation transport and GPR simulations.

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“…The use of real time LiDAR and video to augment the analysis of radiation data holds the potential to improve detection and localization performance while also providing contextually complete data products to aid in the interpretation of the radiation measurement. Potential concepts include the use of contextual cues present in visual imagery to inform detection algorithms of the local environment, the use of ranging information from LiDAR to improve localization performance, as well as complete fusion of a gamma-ray image with a three-dimensional scene reconstruction [46,47].…”

Section: Fusion Of Gamma-ray Images Video and 3d Point Cloudsmentioning

confidence: 99%