Omnidirectional 3D Gamma-ray Imaging with a Free-moving Spherical Active Coded Aperture

Hellfeld, Daniel; Barton, Paul; Gunter, Donald; Haefner, Andrew; Mihailescu, Lucian; Vetter, K.

doi:10.1109/nssmic.2017.8532690

Cited by 3 publications

(4 citation statements)

References 7 publications

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“…Finally, relevant for field operations, APSL will be developed for streaming operation, informing the user of potential source locations in real-time and directing the path to decrease the time to detect and localize sources. With the addition of contextual sensors, the image space can be constrained to static surfaces and volumes [14], [36], [37] or to moving objects in the 3D scene model, improving the computational speed of the reconstruction as well as providing additional context to the user in real-time.…”

Section: Future Workmentioning

confidence: 99%

Gamma-Ray Point-Source Localization and Sparse Image Reconstruction Using Poisson Likelihood

Hellfeld

Joshi

Bandstra

et al. 2019

IEEE Trans. Nucl. Sci.

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Gamma-ray imaging attempts to reconstruct the spatial and intensity distribution of gamma-emitting radionuclides from a set of measurements. Generally, this problem is solved by discretizing the spatial dimensions and employing the maximum likelihood expectation maximization (ML-EM) algorithm, with or without some form of regularization. While the generality of this formulation enables use in a wide variety of scenarios, it is susceptible to overfitting, limited by the discretization of spatial coordinates, and can be computationally expensive. We present a novel approach to 3D gamma-ray image reconstruction for scenarios where sparsity may be assumed, for example radiological source search. In this work we first formulate a point-source localization (PSL) approach as an optimization problem where both position and source intensity are continuous variables. We then extend and generalize this formulation to an iterative algorithm called additive point-source localization (APSL) for sparse parametric image reconstruction. A set of simulated source search scenarios using a single non-directional detector are considered, finding improved image accuracy and computational efficiency with APSL over traditional grid-based approaches.

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Section: Future Workmentioning

confidence: 99%

Gamma-Ray Point-Source Localization and Sparse Image Reconstruction Using Poisson Likelihood

Hellfeld

Joshi

Bandstra

et al. 2019

IEEE Trans. Nucl. Sci.

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“…Instead of trying to calculate the mutual rotation and translation of the gamma detector with respect to world coordinate system, we try to solve this misalignment problem mathematically by modifying (1). Taking b as the new intercepting point between the X device and the scnitillator, we can summarize this attempt as in (2).…”

Section: D Gamma-ray Imaging: Stereo Line Equation Modelmentioning

confidence: 99%

“…5 shows such an instance where the gamma detector is rotated by 180 • in both horizontal and vertical directions to move it from the right side to the left (only the movement of the gamma detector is shown as we mainly concentrate on rectifying its misalignment problem). If we take l r represents the view-line of the collimator when the detector is at right side, we can use (2) to summarize this view-line as:…”

Section: D Gamma-ray Imaging: Stereo Line Equation Modelmentioning

confidence: 99%

“…Detecting and imaging, localizing and volumetric-visualizing; gamma-ray sources are of the actively discussed topics in the field of radiology, since the discovery of X-rays in late 1895 [1]. Applications ranging from nuclear medicine to nuclear safeguards, nuclear contamination remediation, and even environmental monitoring are being widely researched [2]- [6], broadening the study of new designs and developments of much sophisticated gamma-ray imagers (detectors). Starting from medieval Geiger counters to modern static or portable imaging detectors with collimator, Compton, coded-aperture based approaches have been proposed in detecting and imaging [3].…”

Section: Introductionmentioning

confidence: 99%

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Stereo Vision-Based Gamma-Ray Imaging for 3D Scene Data Fusion

2019

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Modern developments of gamma-ray imagers by integrating multi-contextual sensors and advanced computer vision theories have enabled unprecedented capabilities in detection and imaging, reconstruction and mapping of radioactive sources. Notwithstanding these remarkable capabilities, the addition of multiple sensors such as light detection and ranging units (LiDAR), RGB-D sensors (Microsoft Kinect), and inertial measurement units (IMU) are mostly expensive. Instead of using such expensive sensors, we, in this paper, introduce a modest three-dimensional (3D) gamma-ray imaging method by exploiting the advancements in modern stereo vision technologies. A stereo line equation model is proposed to properly identify the distribution area of gamma-ray intensities that are used for two-dimensional (2D) visualizations. Scene data information of the surrounding environment captured at different locations are reconstructed by re-projecting disparity images created with the semi-global matching algorithm (SGM) and are merged together by employing the point-to-point iterative closest point algorithm (ICP). Instead of superimposing/overlaying 2D radioisotopes on the merged scene area, reconstructions of 2D gamma images are fused together with it to create a detailed 3D volume. Through experimental results, we try to emphasize the accuracy of our proposed fusion method.INDEX TERMS Stereo vision, gamma-ray imaging, stereo matching, scene data fusion, 3D imaging.

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