Benefits of Position-Sensitive Detectors for Radioactive Source Detection

There are many systems for counting photons such as gamma-rays emitted from radioactive sources. Many of these systems are also position-sensitive, which means that the system provides directional information about recorded events. This paper investigates whether or not the additional information provided by position-sensitive capability improves the performance of detecting a point-source in background. We analyze the asymptotic performance of the generalized likelihood ratio test (GLRT) and a test based on the maximum-likelihood (ML) estimate of the source intensity for systems with and without position-sensitive capability. When the background intensity is known and detector sensitivity is spatially uniform, we prove that position-sensitive capability increases the area under the receiver operating characteristic curve (AUC). For cases when detector sensitivity is nonuniform or background intensity is unknown, we provide numerical results to illustrate the effect of the parameters on detection performance.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85967/1/Fessler6.pd

Benefits of Position–Sensitive Detectors for Source Detection with Known Background

We address the question of whether or not the directional or imaging information offered by a position-sensitive gamma-ray detector improves the detection accuracy when searching for a source of known shape amid a background of known intensity. We formulate the detection problem as a composite hypothesis testing problem and examine the behavior of the generalized likelihood ratio test (GLRT) in terms of the area under the receiver operating characteristic (AUC). Due to the analytical complexity of the GLRT in this case, we examine its asymptotic properties when the number of detected photons is large. We find that a detector of uniform sensitivity can more accurately detect a source when imaging information is used.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85968/1/Fessler245.pd

Source Detection and Image Reconstruction with Position-Sensitive Gamma-Ray Detectors.

Gamma-ray detectors have important applications in security, medicine, and nuclear non-proliferation. This thesis investigates the use of regularization to improve image reconstruction and efficient methods for predicting source detection performance with position-sensitive gamma-ray detectors. Position-sensitive detectors have the ability to measure the spatial gamma emission density around the detector. An image of the spatial gamma emission density where a spatially small source is present will be sparse in the canonical basis, meaning that the emission density is zero for most directions but large for a small number of directions. This work uses regularization to enforce sparsity in the reconstructed image, and proposes a regularizer that effectively enforces sparsity in the reconstructed images. This work also proposes a method for predicting detection performance. Position-sensitive gamma-ray imaging systems are complex and difficult to model both accurately and efficiently. This work investigates the asymptotic properties of tests based on maximum likelihood (ML) estimates under model mismatch, meaning that the statistical model used for detection differs from the true distribution. We propose general expressions for the asymptotic distribution of likelihood-based test statistics when the number of measurements is Poisson. We use the general expressions to derive expressions specific to gamma-ray source detection that one can evaluate using a modest amount of data from a real system or Monte-Carlo simulation. We show empirically with simulated data that the proposed expressions yield more accurate detection performance predictions than expressions that ignore model mismatch. We also use data recorded with a 3D position-sensitive CdZnTe system with a Cs-137 source in a natural background to show that the proposed method is reasonably accurate with real data. These expressions require less data and computation than conventional empirical methods. To quantify the benefit of position-sensitivity, we state and prove a theorem affirming that, asymptotically as scan time becomes large, position-sensitivity increases the area under the receiver operating characteristic curve (AUC) when the background intensity is known, detector sensitivity is spatially uniform, and the system model is correctly specified.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91441/1/danling_1.pd

1 Benefits of Position–Sensitive Detectors for Radioactive Source Detection

Abstract—There are many systems for counting photons such as gamma–rays emitted from radioactive sources. Many of these systems are also position–sensitive, which means that the system provides directional information about recorded events. This paper investigates whether or not the additional information provided by position–sensitive capability improves the performance of detecting a point–source in background. We analyze the asymptotic performance of the generalized likelihood ratio test (GLRT) and a test based on the maximum–likelihood (ML) estimate of the source intensity for systems with and without position–sensitive capability. When the background intensity is known and detector sensitivity is spatially uniform, we prove that position–sensitive capability increases the area under the receiver operating characteristic curve (AUC). For cases when detector sensitivity is nonuniform or background intensity is unknown, we provide numerical results to illustrate the effect of the parameters on detection performance. Index Terms—Detection, generalized likelihood ratio test (GLRT), asymptotic I

Imaging, Detection, and Identification Algorithms for Position-Sensitive Gamma-Ray Detectors.

Three-dimensional-position-sensitive semiconductors record both the locations and energies of gamma-ray interactions with high resolution, enabling spectroscopy and imaging of gamma-ray-emitting materials. Imaging enables the detection of point sources of gamma rays in an otherwise extended-source background, even when the background spectrum is unknown and may share the point source's spectrum. The generalized likelihood ratio test (GLRT) and source-intensity test (SIT) are applied to this situation to detect one-or-more unshielded point sources from a library of isotopes in a spectrally unknown or known background when the background intensity varies spatially by a factor of two or less. In addition to estimating the number of sources present, their activities, isotopes, and directions from the detector are estimated. Experimental and some simulated results are presented for a single detector and an 18-detector array of 2 cm by 2 cm by 1.5 cm CdZnTe crystals and compared with the performance of spectral-only detection when the background and source are assumed to be spectrally different. Furthermore, the expected detection performance of the 18-detector array system is investigated statistically using experimental data in the case where the background is distinct spectrally from the point source and the possible source location and isotopic identity are known. Including imaging gave at least 7% higher SNR compared to ignoring the image dimension. Also, imaging methods based on the maximum-likelihood expectation-maximization method are introduced to determine the spatial distribution of isotopes and to find the activity distributions within targets moving with known motion through a radioactive background. Software has also been developed to support the analysis of the data from 3D-position-sensitive spectroscopic systems, for a range of detector designs and applications. The software design and unique features that allow fast multidimensional data analysis are presented, along with parallel computing performance.Ph.D.Nuclear Engineering & Radiological SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/89797/1/cgwahl_1.pd