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At least 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

TU-H-CAMPUS-JeP2-05: Can Automatic Delineation of Cardiac Substructures On Noncontrast CT Be Used for Cardiac Toxicity Analysis?

Purpose: To evaluate the feasibility of using an automatic segmentation tool to delineate cardiac substructures from computed tomography (CT) images for cardiac toxicity analysis for non-small cell lung cancer (NSCLC) patients after radiotherapy. Methods: A multi-atlas segmentation tool developed in-house was used to delineate eleven cardiac substructures including the whole heart, four heart chambers, and six greater vessels automatically from the averaged 4DCT planning images for 49 NSCLC patients. The automatic segmented contours were edited appropriately by two experienced radiation oncologists. The modified contours were compared with the auto-segmented contours using Dice similarity coefficient (DSC) and mean surface distance (MSD) to evaluate how much modification was needed. In addition, the dose volume histogram (DVH) of the modified contours were compared with that of the auto-segmented contours to evaluate the dosimetric difference between modified and auto-segmented contours. Results: Of the eleven structures, the averaged DSC values ranged from 0.73 ± 0.08 to 0.95 ± 0.04 and the averaged MSD values ranged from 1.3 ± 0.6 mm to 2.9 ± 5.1mm for the 49 patients. Overall, the modification is small. The pulmonary vein (PV) and the inferior vena cava required the most modifications. The V30 (volume receiving 30 Gy or above) for the whole heart and the mean dose to the whole heart and four heart chambers did not show statistically significant difference between modified and auto-segmented contours. The maximum dose to the greater vessels did not show statistically significant difference except for the PV. Conclusion: The automatic segmentation of the cardiac substructures did not require substantial modification. The dosimetric evaluation showed no statistically significant difference between auto-segmented and modified contours except for the PV, which suggests that auto-segmented contours for the cardiac dose response study are feasible in the clinical practice with a minor modification to the PV vessel.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2022

Microstructure Segmentation with Deep Learning Encoders Pre-Trained on a Large Microscopy Dataset

This study examined the improvement of microscopy segmentation accuracy by transfer learning from a large dataset of microscopy images called MicroNet. Many neural network encoder architectures, including VGG, Inception, and ResNet, were trained on over 100,000 labelled microscopy images from 54 classes. These pre-trained encoders were then embedded into multiple segmentation architectures including U-Net and DeepLabV3+ to evaluate segmentation performance on newly created benchmark microscopy datasets. Compared to ImageNet pre-training, models pre-trained on MicroNet generalized better to out-of-distribution micrographs taken under different imaging and sample conditions and were more accurate with less training data. When training with only a single Ni-superalloy image, pre-training on MicroNet produced a 72.2 percent reduction in relative segmentation error. These results suggest that transfer learning from large in-domain datasets generate models with learned feature representations that are more useful for downstream tasks and will likely improve any microscopy image analysis technique that can leverage pre-trained encoders.

machine learning↗
NASA NTRS1989

Image understanding and the man-machine interface II; Proceedings of the Meeting, Los Angeles, CA, Jan. 17, 18, 1989

Image understanding concepts and models, image understanding systems and applications, advanced digital processors and software tools, and advanced man-machine interfaces are among the topics discussed. Particular papers are presented on such topics as neural networks for computer vision, object-based segmentation and color recognition in multispectral images, the application of image algebra to image measurement and feature extraction, and the integration of modeling and graphics to create an infrared signal processing test bed.

Barrett, Eamon B.↗
DOE OSTI2026

Gaia: segmented germanium detector for high-energy X-ray fluorescence and spectroscopic imaging

We present Gaia, a monolithic array of 96 high-purity germanium pixel detectors integrated with a custom low-noise application-specific integrated circuit (ASIC) and a field-programmable gate array (FPGA)-based data acquisition system. The sensor operates at ∼100 K using a commercial closed-cycle cryocooler, with the in-vacuum electronics thermally isolated from the cold finger to ensure thermal stability. The system demonstrates an average energy resolution of 711 eV at 122 keV, measured using a 57 Co source, and 253 eV at 5.89 keV, measured with 55 Fe across all channels. The readout architecture incorporates a high-performance FPGA paired with a dual-core ARM processor, forming a complete embedded Linux-based computing platform. Communication between the processor and FPGA is handled via memory-mapped I/O, and data are streamed over high-speed gigabit Ethernet. A full-scale 384-pixel Gaia detector, based on this 96-element module, is currently under fabrication.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Test of the CLAS12 RICH large-scale prototype in the direct proximity focusing configuration

A large-area ring-imaging Cherenkov detector has been designed to provide clean hadron identi- fication capability in the momentum range from 3 GeV/c up to 8 GeV/c for the CLAS12 experiment at the upgraded 12 GeV continuous electron beam accelerator facility of Jefferson Laboratory. The adopted solution foresees a novel hybrid optics design based on aerogel radiator, composite mirrors and highly packed and highly segmented photon detectors. Cherenkov light will either be imaged directly (forward tracks) or after two mirror reflections (large-angle tracks). We report here the results of the tests of a large-scale prototype of the RICH detector performed with the hadron beam of the CERN T9 experimental hall for the direct detection configuration. The tests demonstrated that the proposed design provides the required pion-to-kaon rejection factor of 1 : 500 in the whole momentum range.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Comparative Approach of MRI-Based Brain Tumor Segmentation and Classification Using Genetic Algorithm

The detection of a brain tumor and its classification from modern imaging modalities is a primary concern, but a time-consuming and tedious work was performed by radiologists or clinical supervisors. The accuracy of detection and classification of tumor stages performed by radiologists is depended on their experience only, so the computer-aided technology is very important to aid with the diagnosis accuracy. In this study, to improve the performance of tumor detection, we investigated comparative approach of different segmentation techniques and selected the best one by comparing their segmentation score. Further, to improve the classification accuracy, the genetic algorithm is employed for the automatic classification of tumor stage. The decision of classification stage is supported by extracting relevant features and area calculation. The experimental results of proposed technique are evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on segmentation score, accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 92.03% accuracy, 91.42% specificity, 92.36% sensitivity, and an average segmentation score between 0.82 and 0.93 demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 93.79% dice similarity index coefficient, which indicates better overlap between the automated extracted tumor regions with manually extracted tumor region by radiologists.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Micropuncture of Bowman's Space in Mice Facilitated by 2 Photon Microscopy

Renal micropuncture and renal 2-photon imaging are seminal techniques in renal physiology. However, micropuncture is limited by dependence on conventional microscopy to surface nephron features, and 2-photon studies are limited in that interventions can only be assessed at the organ, rather than the nephron level. In particular, micropuncture studies of the glomeruli of mice have been challenged by the paucity of surface glomeruli in mice. To address this limitation in order to pursue studies of aspirate from Bowman's space in mouse physiologic models, we developed 2-photon glomerular micropuncture. We present a novel surgical preparation that allows lateral access to the kidney while preserving the required vertical imaging column for 2-photon microscopy. Administration of high molecular weight fluorescein isothiocyanate (FITC)- dextran is used to render the renal vasculature and therefore glomeruli visible for 2-photon imaging. A quantum dot-coated pipette is then introduced under stereotactic guidance to a glomerulus selected from the several to many which may be visualized within the imaging window. In this protocol, we provide details of the preparation, materials, and methods necessary to carry out the procedure. This technique facilitates previously-impossible physiologic study of the kidney, including recovery of filtrate from Bowman's space and all segments of the nephron within the imaging depth limit, about 100 µm below the renal capsule. Pressure, charge and flow may all be measured using the introduced pipette. Here, we provide representative data from liquid chromatography/mass spectrometry performed on aspirate from Bowman's space. We expect this technique to have wide applicability in renal physiologic investigation.

Matsushita, Katsuyuki↗
DOE OSTI2016

SU-C-207B-03: A Geometrical Constrained Chan-Vese Based Tumor Segmentation Scheme for PET

Purpose: Accurate segmentation of tumor in PET is challenging when part of tumor is connected with normal organs/tissues with no difference in intensity. Conventional segmentation methods, such as thresholding or region growing, cannot generate satisfactory results in this case. We proposed a geometrical constrained Chan-Vese based scheme to segment tumor in PET for this special case by considering the similarity between two adjacent slices. Methods: The proposed scheme performs segmentation in a slice-by-slice fashion where an accurate segmentation of one slice is used as the guidance for segmentation of rest slices. For a slice that the tumor is not directly connected to organs/tissues with similar intensity values, a conventional clustering-based segmentation method under user’s guidance is used to obtain an exact tumor contour. This is set as the initial contour and the Chan-Vese algorithm is applied for segmenting the tumor in the next adjacent slice by adding constraints of tumor size, position and shape information. This procedure is repeated until the last slice of PET containing tumor. The proposed geometrical constrained Chan-Vese based algorithm was implemented in Matlab and its performance was tested on several cervical cancer patients where cervix and bladder are connected with similar activity values. The positive predictive values (PPV) are calculated to characterize the segmentation accuracy of the proposed scheme. Results: Tumors were accurately segmented by the proposed method even when they are connected with bladder in the image with no difference in intensity. The average PPVs were 0.9571±0.0355 and 0.9894±0.0271 for 17 slices and 11 slices of PET from two patients, respectively. Conclusion: We have developed a new scheme to segment tumor in PET images for the special case that the tumor is quite similar to or connected to normal organs/tissues in the image. The proposed scheme can provide a reliable way for segmenting tumors.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

Exoplanet Yield Sensitivity for the Hybrid Lyot Coronagraph from end-to-end modeling for LUVOIR-A

NASA's future direct imaging flagship mission will require telescope mirror segmentation to survey a statistically significant population of nearby stellar systems by increasing aperture size beyond that achieved by traditional space-borne monolithic apertures thus far. Segmentation is a path for the size expansion, but it entails a challenge due to the introduction of temporal pupil discontinuities in terms of both phase and amplitude. Modeling of the integrated performance of the coronagraph with the expected dynamical profile of the telescope is an important step toward realistic estimates of the mission's science yields. We present results of a study using the hybrid Lyot coronagraph with a 15-m on-axis, segmented optical telescope assembly (OTA), akin to that of the Large UV / Optical /Infrared (LUVOIR) mission. We have developed a high-fidelity, end-to-end model as part of the Segmented Coronagraph Design and Analysis (SCDA) study that includes diffractive optical propagations at the 1e-10 contrast level across the optical train including the OTA and intermediate re-imaging optics inside the coronagraph instrument. We perform a sensitivity analysis to assess the contrast sensitivity to wavefront perturbations for a set of key telescope terms including segment-level low-order and mid-spatial frequency aberrations and evaluate expected exoplanet yields obtained in the presence of these sets of aberrations. Industry-provided segment-level wavefront aberration datasets involving the following mechanisms are input to the end-to-end model: (1) thermal gradients and accelerations, and (2) telescope dynamics including line-of-sight and segment jitter. Dynamically averaged PSFs in the presence of aberrations are fed into AYO (Altruistic Yield Optimization), a mission design reference simulator, that then assesses the scientific yield sensitivities.

high-contrast imaging↗
NASA NTRS2010

Wavefront Control and Image Restoration with Less Computing

PseudoDiversity is a method of recovering the wavefront in a sparse- or segmented- aperture optical system typified by an interferometer or a telescope equipped with an adaptive primary mirror consisting of controllably slightly moveable segments. (PseudoDiversity should not be confused with a radio-antenna-arraying method called pseudodiversity.) As in the cases of other wavefront- recovery methods, the streams of wavefront data generated by means of PseudoDiversity are used as feedback signals for controlling electromechanical actuators of the various segments so as to correct wavefront errors and thereby, for example, obtain a clearer, steadier image of a distant object in the presence of atmospheric turbulence. There are numerous potential applications in astronomy, remote sensing from aircraft and spacecraft, targeting missiles, sighting military targets, and medical imaging (including microscopy) through such intervening media as cells or water. In comparison with prior wavefront-recovery methods used in adaptive optics, PseudoDiversity involves considerably simpler equipment and procedures and less computation. For PseudoDiversity, there is no need to install separate metrological equipment or to use any optomechanical components beyond those that are already parts of the optical system to which the method is applied. In Pseudo- Diversity, the actuators of a subset of the segments or subapertures are driven to make the segments dither in the piston, tilt, and tip degrees of freedom. Each aperture is dithered at a unique frequency at an amplitude of a half wavelength of light. During the dithering, images on the focal plane are detected and digitized at a rate of at least four samples per dither period. In the processing of the image samples, the use of different dither frequencies makes it possible to determine the separate effects of the various dithered segments or apertures. The digitized image-detector outputs are processed in the spatial-frequency (Fourier-transform) domain to obtain measures of the piston, tip, and tilt errors over each segment or subaperture. Once these measures are known, they are fed back to the actuators to correct the errors. In addition, measures of errors that remain after correction by use of the actuators are further utilized in an algorithm in which the image is phase-corrected in the spatial-frequency domain and then transformed back to the spatial domain at each time step and summed with the images from all previous time steps to obtain a final image having a greater signal-to-noise ratio (and, hence, a visual quality) higher than would otherwise be attainable.

Lyon, Richard G.↗
DOE OSTI2012

Using Segmentation in CT Metal Artifact Reduction

Metal artifact reduction methods in computed tomography replace the projection data passing through metals with an estimate of the true data. Inaccurate estimation leads to the generation of secondary artifacts. Data estimates can be improved by the use of prior knowledge of the projection data. In this paper, a method has been created to generate a prior image. The method uses computer vision techniques to segment regions of the initially reconstructed image and then discriminates between regions that are likely to be artifacts and anatomical structures. Results on test images show that metal artifacts are reduced and that few secondary artifacts are present, even in the case of multiple metal objects.

42 ENGINEERING↗
DOE OSTI2015

Multi-contrast 3D X-ray imaging of porous and composite materials

Grating-based X-ray computed tomography allows for simultaneous and nondestructive determination of the full X-ray complex index of refraction and the scattering coefficient distribution inside an object in three dimensions. Its multi-contrast capabilities combined with a high resolution of a few micrometers make it a suitable tool for assessing multiple phases inside porous and composite materials such as concrete. Here, we present quantitative results of a proof-of-principle experiment performed on a concrete sample. Thanks to the complementarity of the contrast channels, more concrete phases could be distinguished than in conventional attenuation-based imaging. The phase-contrast reconstruction shows high contrast between the hardened cement paste and the aggregates and thus allows easy 3D segmentation. Thanks to the dark-field image, micro-cracks inside the coarse aggregates are visible. We believe that these results are extremely interesting in the field of porous and composite materials studies because of unique information provided by grating interferometry in a non-destructive way.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Design and performance analysis of a PIAACMC coronagraph on a segmented aperture

The Phase-Induced Amplitude Apodization Complex Mask Coronagraph (PIAACMC) offers high throughput and small inner working angle (IWA) for direct imaging of exoplanets. PIAACMC is also compatible with segmented apertures, which is important for the success of future large telescopes, such as the Large UV / Optical/ Infrared (LUVOIR) concept currently under way to inform NASA's2020 decadal survey. We present a design of PIAACMC for one of the segmented apertures considered for LUVOIR and simulate its performance with wavefront control, in monochromatic as well as broadband light. We characterize its performance in terms of throughput, inner working angle, contrast, area of the dark zone, and sensitivity to low order aberrations, in particular tip/tilt. In addition, we study the resilience to large stellar angular sizes and polarization. Finally, we compute the "performance map", which is a recently introduced figure of merit for coronagraphs

Exoplanets↗
NASA NTRSDate not supplied

Recent Developments to the Porous Microstructure Analysis (PuMA) Software

Introduction The Porous Microstructure Analysis (PuMA) software is an open source framework for image-based simulation, primarily used to determine effective properties based on material microstructure. PuMA was originally developed for the study of NASA thermal protection materials; however, many of the solvers in PuMA have applicability to a broad range of materials science applications. PuMA version 3.2 computes material surface area, pore diameters, effective thermal conductivity, continuum and rarefied tortuosity, and permeability. For anisotropic materials, PuMA can estimate material orientation and compute anisotropic thermal conductivity and elasticity. In this talk, a brief overview of the PuMA software and underlying methods will be presented, as well as some recent and ongoing developments, including the use of immersed boundary methods for image-based simulation and the development of a new weave segmentation tool, called TomoSAM. Cut-Cell method for heat and mass transfer For simulations on complex microstructures, traditional unstructured meshing techniques often prove to be difficult and time-intensive. Voxel-based solvers, which represent the surface as a staircase structure, are relatively simple to implement but can lose accuracy when feature resolution is poor. In this work, we present a novel 3D cut-cell method for solving the variable coefficient Poisson equation on complex microstructures, suitable for the determination of effective thermal conductivity or tortuosity of a material. The method uses a Marching Cubes/Marching Squares surface reconstruction to create cut-cells and determine geometric quantities. A flux-correction method is extended to 3D, with least squares gradient reconstruction, to solve for the boundary fluxes in the cut-cells. Verification cases show the solver achieves globally 2nd order accuracy on complex microstructures. TomoSAM TomoSAM, a module of the PuMA software, has been developed as a plugin for 3D Slicer, a software platform used for 3D image processing and visualization. It utilizes the Segment Anything Model (SAM), a deep learning model capable of identifying objects and generating image masks based on minimal user input. This feature enables efficient segmentation of complex 3D datasets, particularly of woven materials, from tomography or similar imaging methods, reducing the need for manual segmentation.

Tomography↗
NASA NTRSDate not supplied

Recent Developments to the Porous Microstructure Analysis (PuMA) Software

The Porous Microstructure Analysis (PuMA) software is an open source framework for image-based simulation, primarily used to determine effective properties based on material microstructure. PuMA was originally developed for the study of NASA thermal protection materials; however, many of the solvers in PuMA have applicability to a broad range of materials science applications. PuMA version 3.2 computes material surface area, pore diameters, effective thermal conductivity, continuum and rarefied tortuosity, and permeability. For anisotropic materials, PuMA can estimate material orientation and compute anisotropic thermal conductivity and elasticity. In this talk, a brief overview of the PuMA software and underlying methods will be presented, as well as some recent and ongoing developments, including the use of immersed boundary methods for image-based simulation and the development of a new weave segmentation tool, called TomoSAM. Cut-Cell method for heat and mass transfer For simulations on complex microstructures, traditional unstructured meshing techniques often prove to be difficult and time-intensive. Voxel-based solvers, which represent the surface as a staircase structure, are relatively simple to implement but can lose accuracy when feature resolution is poor. In this work, we present a novel 3D cut-cell method for solving the variable coefficient Poisson equation on complex microstructures, suitable for the determination of effective thermal conductivity or tortuosity of a material. The method uses a Marching Cubes/Marching Squares surface reconstruction to create cut-cells and determine geometric quantities. A flux-correction method is extended to 3D, with least squares gradient reconstruction, to solve for the boundary fluxes in the cut-cells. Verification cases show the solver achieves globally 2nd order accuracy on complex microstructures. TomoSAM TomoSAM, a module of the PuMA software, has been developed as a plugin for 3D Slicer, a software platform used for 3D image processing and visualization. It utilizes the Segment Anything Model (SAM), a deep learning model capable of identifying objects and generating image masks based on minimal user input. This feature enables efficient segmentation of complex 3D datasets, particularly of woven materials, from tomography or similar imaging methods, reducing the need for manual segmentation.

Tomography↗
DOE OSTI2016

SU-F-J-97: A Joint Registration and Segmentation Approach for Large Bladder Deformations in Adaptive Radiotherapy

Purpose: To improve results of deformable image registration (DIR) in adaptive radiotherapy for large bladder deformations in CT/CBCT pelvis imaging. Methods: A variational multi-modal DIR algorithm is incorporated in a joint iterative scheme, alternating between segmentation based bladder matching and registration. Using an initial DIR to propagate the bladder contour to the CBCT, in a segmentation step the contour is improved by discrete image gradient sampling along all surface normals and adapting the delineation to match the location of each maximum (with a search range of +−5/2mm at the superior/inferior bladder side and step size of 0.5mm). An additional graph-cut based constraint limits the maximum difference between neighboring points. This improved contour is utilized in a subsequent DIR with a surface matching constraint. By calculating an euclidean distance map of the improved contour surface, the new constraint enforces the DIR to map each point of the original contour onto the improved contour. The resulting deformation is then used as a starting guess to compute a deformation update, which can again be used for the next segmentation step. The result is a dense deformation, able to capture much larger bladder deformations. The new method is evaluated on ten CT/CBCT male pelvis datasets, calculating Dice similarity coefficients (DSC) between the final propagated bladder contour and a manually delineated gold standard on the CBCT image. Results: Over all ten cases, an average DSC of 0.93±0.03 is achieved on the bladder. Compared with the initial DIR (0.88±0.05), the DSC is equal (2 cases) or improved (8 cases). Additionally, DSC accuracy of femoral bones (0.94±0.02) was not affected. Conclusion: The new approach shows that using the presented alternating segmentation/registration approach, the results of bladder DIR in the pelvis region can be greatly improved, especially for cases with large variations in bladder volume. Fraunhofer MEVIS received funding from a research grant by Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Automated Quantitative Analysis of Extended Irradiation Defects - Dislocations, Voids and Precipitates in Neutron Irradiated HT-9 Steel

Structural alloys that make up the core components of nuclear reactors are exposed to radiation that can substantially alter the microstructure and mechanical properties of the material. Irradiation can cause the formation of extended defects including dislocation line and loops, voids and various types of precipitates. To understand the irradiation effects, it is critical to characterize these defects individually and collectively to quantify how they evolve throughout irradiation stages. Transmission electron microscopy (TEM) is a standard tool for providing extensive size, number density and spatial distribution of extended irradiation defects, however, manual measurement of defect size and number density requires time-intensive effort to analyze enough defects that provide a statistically meaningful result and are prone to errors of subjectivity. In this work, we developed a set of MATLAB algorithms offering robust and automated identification and quantification of extended irradiation defects like dislocation lines, voids and precipitates commonly observed in neutron irradiated cladding alloys [1]. Using a neutron-irradiated HT-9 martensitic alloy irradiated at 412°C to 111.8 dpa, we demonstrate the capability of these algorithms for quantitative defect analysis. Figure 1 presents defect images of the HT-9 using an advanced diffraction contrast imaging scanning transmission electron microscopy (DCI STEM) that suppresses bend contours and provides better defect contrast compared to conventional TEM [2]. The first step of defect analysis is defect identification, a process that can be digitally achieved by contrast thresholding (for simple defect contrast) and by machine learning computer vision for defect semantic segmentation [3]. Here, labeled binary defect images (Figures 2a and 2c) were used. For dislocation density quantification, we employed an optimized circular-grid intersection method first introduced by Dr. John Steeds [4]. This method is shown in Figures 2a and 2b, where the number of intersections of dislocation lines with circular traces was measured. This produces an unbiased sampling of dislocation lines, regardless of their orientation. This process was optimized with automated digital intersection number and grid length registration. Interestingly, it was found that a relatively large number of circles in a set circular gird is was needed to produce an invariant result (Figure 2b), with a low systematic measurement error. This demonstrates the main advantage of computer-based defect analysis, in performing repetitive operations quickly with a reduced systematic error. For the quantitative analysis of voids and precipitates a challenge encountered with automated defect analysis is feature overlap, particularly when the density of voids/precipitates is high. To mitigate this problem, as shown in Figure 2d, we modified the popular circular Hough transformation and treated ‘circular shape’ as the particle descriptor for voids/precipitates, efficiently resolving the overlapping particles. In summary, this automated digital quantification together with machine learning enabled defect identification [3] opens new opportunities for standardizing the characterization workflow for quantitative analysis of extended irradiation defects.

Sainju, Rajat↗
DOE OSTI2017

The Impacts of Source Location in Tomographic Gamma Scanning - 17324

Canberra Industries has recently designed and built a Tomographic Gamma Scanning (TGS) system for the deployment in a nuclear facility. The TGS technique combines high-resolution gamma spectroscopy with low spatial resolution 3-dimensional image reconstruction to provide increased accuracy over traditional approaches for the assay of non-uniform source distributions in low-to medium-density containers. The TGS system has been optimized to measure 120 liter drums and has been equipped to use various collimators and attenuators to assay containers under a wide range of operational conditions. In this paper, we report on experimental work using TGS systems aimed at evaluating the effects of source locations at the limits of volume elements, or voxels, defined in the image reconstruction. These include sources placed at voxel boundaries within a single vertical layer and sources placed in the lowest and highest possible vertical positions within the containers. Building upon previous studies, the accuracy of the TGS system was also assessed by varying the radial position of a point source within a vertical segment. The robustness of the TGS image reconstruction algorithm to location of the sources location was also analyzed as a function of matrix density and gamma-ray energy. Methods to reduce the biases from source location at extreme positions within the container were implemented and tested. The results from this work have been used to better understand the total measurement uncertainty (TMU) and analysis methodology used in TGS systems currently deployed and under development. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗