Investigation of Time and Position Resolved Alpha Transducers for Multi-Modal Imaging with a D-T Neutron Generator

Deuterium-Tritium (D-T) neutron generators have been used as an active interrogation source for associated particle imaging (API) techniques. The D-T reaction yields a 14.1 MeV neutron and a 3.5 MeV alpha (or assoicated) particle, projected nearly back-to-back. The kinetics of the reaction allow the direction and initial time of the neutron to be determined utilizing position sensitive detectors for both the alpha and neutron. This information facilitates multi-modal fast neutron imaging of inspection objects and closed containers to infer the geometry within them and the presence of special nuclear material (SNM). Since position and time of interaction of the alpha and neutron within their respective detection media are required to form these images, improved certainty in the direction and timestamp of the both provides improved imaging performance. This dissertation presents work performed to understand performance limits of a first-generation design associated particle detector (APD) for a specific prototype imaging system developed by the Nuclear Materials Detection and Characterization Group at Oak Ridge National Laboratory. The performance of the first-generation design was first studied through measurements, analytical timing models, and detailed Monte-Carlo timing simulations. Implications on the influence of certain factors on next generations designs were taken from this study. Clear pathways for improved detector performance were identified through the engineering and implementation of new light interfaces and new detector technology. With these improvements in place, three next-generation prototype APD designs were developed with excellent timing performance and optimum trade-offs in position resolution

GIST: an interactive, GPU-based level set segmentation tool for 3D medical images

technical reportWhile level sets have demonstrated a great potential for 3D medical image segmentation, their usefulness has been limited by two problems. First, 3D level sets are relatively slow to compute. Second, their formulation usually entails several free parameters which can be very difficult to correctly tune for specific applications. The second problem is compounded by the first. This paper describes a new tool for 3D segmentation that addresses these problems by computing level-set surface models at interactive rates. This tool employs two important, novel technologies. First is the mapping of a 3D level-set solver onto a commodity graphics card (GPU). This mapping relies on a novel mechanism for GPU memory management. The interactive rates level-set PDE solver give the user immediate feedback on the parameter settings, and thus users can tune free parameters and control the shape of the model in real time. The second technology is the use of region-based speed functions, which allow a user to quickly and intuitively specify the behavior of the deformable model. We have found that the combination of these interactive tools enables users to produce good, reliable segmentations. To support this observation, this paper presents qualitative results from several different datasets as well as a quantitative evaluation from a study of brain tumor segmentations

Volumetric reconstruction in the microCAT tomography system

A new system for x-ray cone-beam micro-tomography has been developed to screen mice for internal phenotypic abnormalities at the Oak Ridge National Laboratory Mammalian Genetics Facility. Currently this system uses an image reconstruction algorithm that is based on two-dimensional (fan-beam) reconstruction techniques. The disparity between the actual scanner geometry and that assumed for reconstruction purposes introduces artifacts into the reconstruction volume that become increasingly worse the further their axial distance from the midplane. In order to reconcile this disparity and reduce axial distortion artifacts, a volumetric reconstruction algorithm based on cone beam geometry was implemented. The volumetric algorithm is derived and its heuristic implementation is explained within the constraints of the system, which limit the arclength of the scanning trajectory. Reconstructions using the volumetric algorithm are analyzed and compared to reconstructions from the current method. We show that our implementation produces images of equivalent quality in the midplane, and a marked decrease in axial distortion elsewhere Volume reconstruction times are shown to be comparable to those currently achieved. The theoretical foundations are given for future work to optimize the implementation through parallelization and by overcoming the data sufficiency problem

Case study: an evaluation of user-assisted hierarchical watershed segmentation

technical reportWhile level sets have demonstrated a great potential for 3D medical image segmentation, their usefulness has been limited by two problems. First, 3D level sets are relatively slow to compute. Second, their formulation usually entails several free parameters which can be very difficult to correctly tune for specific applications. The second problem is compounded by the first. This paper describes a new tool for 3D segmentation that addresses these problems by computing level-set surface models at interactive rates. This tool employs two important, novel technologies. First is the mapping of a 3D level-set solver onto a commodity graphics card (GPU). This mapping relies on a novel mechanism for GPU memory management. The interactive rates level-set PDE solver give the user immediate feedback on the parameter settings, and thus users can tune free parameters and control the shape of the model in real time. The second technology is the use of region-based speed functions, which allow a user to quickly and intuitively specify the behavior of the deformable model. We have found that the combination of these interactive tools enables users to produce good, reliable segmentations. To support this observation, this paper presents qualitative results from several different datasets as well as a quantitative evaluation from a study of brain tumor segmentations

Deformable Multisurface Segmentation of the Spine for Orthopedic Surgery Planning and Simulation

Purpose: We describe a shape-aware multisurface simplex deformable model for the segmentation of healthy as well as pathological lumbar spine in medical image data. Approach: This model provides an accurate and robust segmentation scheme for the identification of intervertebral disc pathologies to enable the minimally supervised planning and patient-specific simulation of spine surgery, in a manner that combines multisurface and shape statistics-based variants of the deformable simplex model. Statistical shape variation within the dataset has been captured by application of principal component analysis and incorporated during the segmentation process to refine results. In the case where shape statistics hinder detection of the pathological region, user assistance is allowed to disable the prior shape influence during deformation. Results: Results demonstrate validation against user-assisted expert segmentation, showing excellent boundary agreement and prevention of spatial overlap between neighboring surfaces. This section also plots the characteristics of the statistical shape model, such as compactness, generalizability and specificity, as a function of the number of modes used to represent the family of shapes. Final results demonstrate a proof-of-concept deformation application based on the open-source surgery simulation Simulation Open Framework Architecture toolkit. Conclusions: To summarize, we present a deformable multisurface model that embeds a shape statistics force, with applications to surgery planning and simulation

Free-moving Quantitative Gamma-ray Imaging

The ability to map and estimate the activity of radiological source distributions in unknown three-dimensional environments has applications in the prevention and response to radiological accidents or threats as well as the enforcement and verification of international nuclear non-proliferation agreements. Such a capability requires well-characterized detector response functions, accurate time-dependent detector position and orientation data, an algorithmic understanding of the surrounding 3D environment, and appropriate image reconstruction and uncertainty quantification methods. We have previously demonstrated 3D mapping of gamma-ray emitters with free-moving detector systems on a relative intensity scale using a technique called Scene Data Fusion (SDF). Here we characterize the detector response of a multi-element gamma-ray imaging system using experimentally benchmarked Monte Carlo simulations and perform 3D mapping on an absolute intensity scale. We present experimental reconstruction results from hand-carried and airborne measurements with point-like and distributed sources in known configurations, demonstrating quantitative SDF in complex 3D environments.Comment: 19 pages, 5 figures, 4 supplementary figures, submitted to Scientific Reports - Natur