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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Towards Realistic and High Fidelity Models for Nuclear Reactor Power Synthesis Simulation with Self-Powered Neutron Detectors

As presented in this report, a weighting function–based inferencing method is being applied to synthesize the power distribution in next-generation and university research reactors based on simulated self power neutron detector (SPND) responses. The overall goal is to assess the impacts of sensor uncertainty and true power distribution perturbations on the error in the synthesized power distribution. Regarding sensor uncertainty, the NuScale Small Modular Reactor (SMR) and the Westinghouse AP1000 serve as testbeds for analyzing the impact of varying the sensor uncertainty, as well as varying the number of sensors per sensor string in the reactor core. The reactor models are informed by Monte Carlo N-Particle (MCNP) neutron flux tallies. For the NuScale SMR and Westinghouse AP1000, the SPND response functions (i.e., the response of the SPNDs to individual segments of fuel) were determined homogeneously. Regarding an analysis of power distribution perturbation detection, the Texas A&M Testing, Research, Isotopes, General Atomics Reactor (TAMU TRIGA) reactor was used as a demonstration case with one particular arrangement of SPNDs; the response functions for this reactor model were determined heterogeneously, making this a uniquely high-fidelity demonstration of perturbation detection. Finally, SPND models generated in the Geometry and Tracking 4 (Geant4) code have been generated and tested for comparison with traditionally implemented analytical SPND models, with the intent for Geant4 integration with the full methodological framework. SPND current outputs as a function of distance from some fuel assembly segment in the NuScale SMR are compared with the analytically determined currents. Results from the sensor uncertainty simulations for the NuScale SMR and AP1000 indicate that the average error in the inferred power distribution on the fuel assembly segment level is reasonably low, being slightly less than the random uncertainty applied to all respective SPNDs in both cores. For example, if all SPNDs in the core have a random uncertainty of 5%, then the corresponding fuel assembly segment level error (i.e. difference between the true and inferred local power) is ~2–3%. However, the maximum error in the inferred power distribution on the fuel assembly segment level can be considerably high (>15%) when SPND random uncertainties start to exceed ~3%. In general, the average and maximum errors in the inferred power distribution were slightly higher in the AP1000 as opposed to the NuScale SMR for the sensor string configurations considered herein. Another result determined from analysis of the sensor uncertainty simulations was that increasing the number of SPNDs per string does not clearly reduce inferred power distribution error and can in fact make the error large in some cases; however, this assessment may skewed due to imposed iteration limits. Results from the perturbation detection demonstration using the high-fidelity TAMU TRIGA model indicate that, given the arrangement of 17 SPND strings and 4 SPNDs per string considered herein, there is a clear, provable ability to infer a localized Gaussian-type peak perturbation in the 3D power distribution. Such a perturbation was detected with an average fuel assembly segment level error of 0.19%, and the general visualization of the detected perturbation clearly indicates that the magnitude and shape were appropriately resolved. Finally, the electrical current output generated by the Geant4 modeled SPND indicates significant magnitude differences than the analytically modeled SPND, demonstrating the need for accurate SPND models which account for finite sensor geometry effects to inform the power synthesis work described herein.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Nonlocal Means Denoising of Self-Gated and k-Space Sorted 4-Dimensional Magnetic Resonance Imaging Using Block-Matching and 3-Dimensional Filtering: Implications for Pancreatic Tumor Registration and Segmentation

Purpose: To denoise self-gated k-space sorted 4-dimensional magnetic resonance imaging (SG-KS-4D-MRI) by applying a nonlocal means denoising filter, block-matching and 3-dimensional filtering (BM3D), to test its impact on the accuracy of 4D image deformable registration and automated tumor segmentation for pancreatic cancer patients. Methods and Materials: Nine patients with pancreatic cancer and abdominal SG-KS-4D-MRI were included in the study. Block-matching and 3D filtering was adapted to search in the axial slices/frames adjacent to the reference image patch in the spatial and temporal domains. The patches with high similarity to the reference patch were used to collectively denoise the 4D-MRI image. The pancreas tumor was manually contoured on the first end-of-exhalation phase for both the raw and the denoised 4D-MRI. B-spline deformable registration was applied to the subsequent phases for contour propagation. The consistency of tumor volume defined by the standard deviation of gross tumor volumes from 10 breathing phases (σ-GTV), tumor motion trajectories in 3 cardinal motion planes, 4D-MRI imaging noise, and image contrast-to-noise ratio were compared between the raw and denoised groups. Results: Block-matching and 3D filtering visually and quantitatively reduced image noise by 52% and improved image contrast-to-noise ratio by 56%, without compromising soft tissue edge definitions. Automatic tumor segmentation is statistically more consistent on the denoised 4D-MRI (σ-GTV = 0.6 cm{sup 3}) than on the raw 4D-MRI (σ-GTV = 0.8 cm{sup 3}). Tumor end-of-exhalation location is also more reproducible on the denoised 4D-MRI than on the raw 4D-MRI in all 3 cardinal motion planes. Conclusions: Block-matching and 3D filtering can significantly reduce random image noise while maintaining structural features in the SG-KS-4D-MRI datasets. In this study of pancreatic tumor segmentation, automatic segmentation of GTV in the registered image sets is shown to be more consistent on the denoised 4D-MRI than on the raw 4D-MRI.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2010

Polyimide Aerogels with Three-Dimensional Cross-Linked Structure

Polyimide aerogels with three-dimensional cross-linked structure are made using linear oligomeric segments of polyimide, and linked with one of the following into a 3D structure: trifunctional aliphatic or aromatic amines, latent reactive end caps such as nadic anhydride or phenylethynylphenyl amine, and silica or silsesquioxane cage structures decorated with amine. Drying the gels supercritically maintains the solid structure of the gel, creating a polyimide aerogel with improved mechanical properties over linear polyimide aerogels. Lightweight, low-density structures are desired for acoustic and thermal insulation for aerospace structures, habitats, astronaut equipment, and aeronautic applications. Aerogels are a unique material for providing such properties because of their extremely low density and small pore sizes. However, plain silica aerogels are brittle. Reinforcing the aerogel structure with a polymer (X-Aerogel) provides vast improvements in strength while maintaining low density and pore structure. However, degradation of polymers used in cross-linking tends to limit use temperatures to below 150 C. Organic aerogels made from linear polyimide have been demonstrated, but gels shrink substantially during supercritical fluid extraction and may have lower use temperature due to lower glass transition temperatures. The purpose of this innovation is to raise the glass transition temperature of all organic polyimide aerogel by use of tri-, tetra-, or poly-functional units in the structure to create a 3D covalently bonded network. Such cross-linked polyimides typically have higher glass transition temperatures in excess of 300 400 C. In addition, the reinforcement provided by a 3D network should improve mechanical stability, and prevent shrinkage on supercritical fluid extraction. The use of tri-functional aromatic or aliphatic amine groups in the polyimide backbone will provide such a 3D structure.

Panek, John↗
DOE OSTI2021

Comprehension of Spatial Constraints by Neural Logic Learning from a Single RGB-D Scan

Autonomous industrial assembly relies on the precise measurement of spatial constraints as designed by computer-aided design (CAD) software such as SolidWorks. This paper proposes a framework for an intelligent industrial robot to understand the spatial constraints for model assembly. An extended generative adversary network (GAN) with a 3D long short-term memory (LSTM) network was designed to composite 3D point clouds from a single RGB-D scan. The spatial constraints of the segmented point clouds are identified by a neural-logic network that incorporates general knowledge of spatial constraints in terms of first-order logic. The model was designed to comprehend a complete set of spatial constraints that are consistent with industrial CAD software, including left, right, above, below, front, behind, parallel, perpendicular, concentric, and coincident relations. The accuracy of 3D model composition and spatial constraint identification was evaluated by the RGB-D scans and 3D models in the ABC dataset. The proposed model achieved 57.23% intersection over union (IoU) in 3D model composition, and over 99% in comprehending all spatial constraints.

Wang, Dali↗
DOE OSTI2016

TU-H-CAMPUS-IeP2-03: Development of 3D Printed Coronary Phantoms for In-Vitro CT-FFR Validation Using Data from 320- Detector Row Coronary CT Angiography

Purpose: To validate Computed Tomography Fractional Flow Reserve (CT-FFR) measurements with accurate 3D printed coronary phantoms. Methods: DICOM data from four phases in two patients imaged with a standard 320 × 0.5mm coronary CT acquisition (70–80% cardiac cycle) underwent semi-automated segmentation using a research workstation. Both patients had a >50% stenosis from the clinical image interpretation. Each volume was saved as a Stereo Lithographic (STL) file with 250 micron resolution. The 3D geometries were qualitatively assessed; the best of the four phases was 3D printed using a Stratasys Eden260V printer in Tango+, a rubber-like material that roughly emulates mechanical properties of human vasculature. We connected the model to a programmable pump and measured the pressure drop using pressure sensors embedded proximal and distal to the arterial stenosis. Next, the STL files used for the 3D printed models were uploaded in the ANSYS meshing tool (ICEM CFD 16.1). A standard meshing process was applied and the meshed geometry was directly imported in the ANSYS Fluent for Computational Flow Dynamics simulations. The CFD simulations were used to calculate the CT-FFR and compared to the bench top FFR measured in the 3D printed phantoms. Results: FFR-CT measurements and phantoms were completed in within an hour after the segmentation. Patient 1 had a 60% stenosis that resulted in a CT-FFR of 0.68. The second case had a 50% stenosis and a CT-FFR of 0.75. The average bench top FFR measurements were 0.72 and 0.80, respectively. Conclusion: This pilot investigation demonstrated the use of a bench-top coronary model for CT-FFR validation. The measurements and the CFD simulations agreed within 6%. Project supported by Support: Toshiba America Medical Systems Corp.and NIH grant R01-EB002873. Project supported by Toshiba America Medical Systems Corp.and partial support from NIH grant R01-EB002873.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Image Fusion Guidance with Pre-procedural CT with Real-Time Fluoroscopy for Adrenal Venous Sampling

PurposeTo assess the effectiveness of image fusion guidance (IF) with pre-procedural computed tomography (CT) with intraprocedural fluoroscopy for adrenal venous sampling (AVS).Materials and MethodsAVS before and after ACTH stimulation including bilateral segmental sampling of effluent tributaries was performed in 62 patients with IF. A 3D volume rendering image, including adrenal glands and veins extracted from previously obtained contrast-enhanced CT images, was manually registered to the real-time X-ray fluoroscopy. The technical success rates, procedure time, radiation exposure, and volume of contrast medium used were compared with 49 patients who underwent AVS without IF.ResultsNo significant differences in the technical success rates with and without IF were observed (98.4 vs. 91.8% for the right adrenal veins, p = 0.168, and 98.4 vs. 100% for the left adrenal veins, p = 1.000). The procedure time with IF was significantly shorter than that without IF (95.6 ± 18.8 vs. 108.4 ± 20.0 min, p = 0.001). The total dose-area product with IF was significantly lower than that without IF (43.1 ± 30.7 vs. 72.2 ± 45.3 Gy cm{sup 2}, p < 0.001). The contrast medium volume used with IF was significantly lower than that without IF (54.6 ± 21.9 vs. 65.7 ± 27.6 mL, p = 0.020).ConclusionsAlthough the contribution to improving the technical success rates was small in our study, IF can effectively reduce procedure time, radiation exposure, and volume of contrast medium during AVS.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Adaptive beamlet-based finite-size pencil beam dose calculation for independent verification of IMRT and VMAT

Purpose: The use of sophisticated dose calculation procedure in modern radiation therapy treatment planning is inevitable in order to account for complex treatment fields created by multileaf collimators (MLCs). As a consequence, independent volumetric dose verification is time consuming, which affects the efficiency of clinical workflow. In this study, the authors present an efficient adaptive beamlet-based finite-size pencil beam (AB-FSPB) dose calculation algorithm that minimizes the computational procedure while preserving the accuracy. Methods: The computational time of finite-size pencil beam (FSPB) algorithm is proportional to the number of infinitesimal and identical beamlets that constitute an arbitrary field shape. In AB-FSPB, dose distribution from each beamlet is mathematically modeled such that the sizes of beamlets to represent an arbitrary field shape no longer need to be infinitesimal nor identical. As a result, it is possible to represent an arbitrary field shape with combinations of different sized and minimal number of beamlets. In addition, the authors included the model parameters to consider MLC for its rounded edge and transmission. Results: Root mean square error (RMSE) between treatment planning system and conventional FSPB on a 10 × 10 cm{sup 2} square field using 10 × 10, 2.5 × 2.5, and 0.5 × 0.5 cm{sup 2} beamlet sizes were 4.90%, 3.19%, and 2.87%, respectively, compared with RMSE of 1.10%, 1.11%, and 1.14% for AB-FSPB. This finding holds true for a larger square field size of 25 × 25 cm{sup 2}, where RMSE for 25 × 25, 2.5 × 2.5, and 0.5 × 0.5 cm{sup 2} beamlet sizes were 5.41%, 4.76%, and 3.54% in FSPB, respectively, compared with RMSE of 0.86%, 0.83%, and 0.88% for AB-FSPB. It was found that AB-FSPB could successfully account for the MLC transmissions without major discrepancy. The algorithm was also graphical processing unit (GPU) compatible to maximize its computational speed. For an intensity modulated radiation therapy (∼12 segments) and a volumetric modulated arc therapy fields (∼90 control points) with a 3D grid size of 2.0 × 2.0 × 2.0 mm{sup 3}, dose was computed within 3–5 and 10–15 s timeframe, respectively. Conclusions: The authors have developed an efficient adaptive beamlet-based pencil beam dose calculation algorithm. The fast computation nature along with GPU compatibility has shown better performance than conventional FSPB. This enables the implementation of AB-FSPB in the clinical environment for independent volumetric dose verification.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Machine Learning Atom Probe Tomography Tool For Automatic And Fast Clustering

The software uses a YOLO11 segmentation model trained on synthetic data to analyze APT datasets. The workflow operates as follows: 1. Data Slicing: The APT dataset is divided into multiple 2D cross-sections of a specified thickness. 2. Segmentation: The model identifies point-dense regions within each 2D slice. 3. 3D Reconstruction: Detected regions (masks) from all slices are combined and reconstructed back into the original 3D space, forming clusters. The integration with HPC resources enables the software to process large-scale APT datasets efficiently. This combination of automation and scalability reduces manual intervention, improves reproducibility, and accelerates the clustering workflow.

Tang, Yalei [Idaho National Laboratory (INL), Idah↗
DOE OSTI2020

Neutrino Tridents in the NOvA Near Detector

NOvA is a long-baseline neutrino oscillation experiment based at Fermilab that uses theNuMI beam line and two detectors to measure neutrino rates at a near location, and 810 km away at a far location.The detectors are made from PVC extrusions which segment the volume in to 4x6 cm cells which alternate orientations to provide 3D tracking of particles in the detector.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Technical Note: Development of a 3D printed subresolution sandwich phantom for validation of brain SPECT analysis

Purpose: To make an adaptable, head shaped radionuclide phantom to simulate molecular imaging of the brain using clinical acquisition and reconstruction protocols. This will allow the characterization and correction of scanner characteristics, and improve the accuracy of clinical image analysis, including the application of databases of normal subjects. Methods: A fused deposition modeling 3D printer was used to create a head shaped phantom made up of transaxial slabs, derived from a simulated MRI dataset. The attenuation of the printed polylactide (PLA), measured by means of the Hounsfield unit on CT scanning, was set to match that of the brain by adjusting the proportion of plastic filament and air (fill ratio). Transmission measurements were made to verify the attenuation of the printed slabs. The radionuclide distribution within the phantom was created by adding {sup 99m}Tc pertechnetate to the ink cartridge of a paper printer and printing images of gray and white matter anatomy, segmented from the same MRI data. The complete subresolution sandwich phantom was assembled from alternate 3D printed slabs and radioactive paper sheets, and then imaged on a dual headed gamma camera to simulate an HMPAO SPECT scan. Results: Reconstructions of phantom scans successfully used automated ellipse fitting to apply attenuation correction. This removed the variability inherent in manual application of attenuation correction and registration inherent in existing cylindrical phantom designs. The resulting images were assessed visually and by count profiles and found to be similar to those from an existing elliptical PMMA phantom. Conclusions: The authors have demonstrated the ability to create physically realistic HMPAO SPECT simulations using a novel head-shaped 3D printed subresolution sandwich method phantom. The phantom can be used to validate all neurological SPECT imaging applications. A simple modification of the phantom design to use thinner slabs would make it suitable for use in PET.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Oak Ridge National Laboratory Progress Update on Segmentation of Neutron Tomographic Images, 2017

The present report documents progress regarding segmentation of fast-neutron tomographic images of objects consisting of assemblies of machined parts with distinct boundaries. This report satisfies the fiscal year 2017 technical deliverable for project OR16-3DTomography-PD3Jb, “3D Tomography and Image Processing Using Fast Neutrons,” to report on the extension of geometric shape-finding algorithms to three dimensions. The project has two overall goals. The first of these goals is to extend associated particle fast neutron transmission and, particularly, induced-reaction tomographic imaging algorithms to three dimensions. The second of these goals is to automatically segment the resultant tomographic images into constituent parts, and then extract information about the parts, such as the class of shape and potentially the shape parameters. This report addresses continued progress in the component of the project concerned with image segmentation.

3D tomography↗
DOE OSTI2014

3D characterization of trans- and inter-lamellar fatigue crack in (α + β) Ti alloy

This paper presents a three dimensional image processing strategy that has been developed to quantitatively analyze and correlate the path of a fatigue crack with the lamellar microstructure found in Ti-6246. The analysis is carried out on X-ray microtomography images acquired in situ during uniaxial fatigue testing. The crack, the primary β-grain boundaries and the α lamellae have been segmented separately and merged for the first time to allow a better characterization and understanding of their mutual interaction. This has particularly emphasized the role of translamellar crack growth at a very high propagation angle with regard to the lamellar orientation, supporting the central role of colonies favorably oriented for basal 〈a〉 slip to guide the crack in the fully lamellar microstructure of Ti alloy. - Highlights: • 3D tomography images reveal strong short fatigue crack interaction with α lamellae. • Proposed 3D image processing methodology makes their segmentation possible. • Crack-lamellae orientation maps show prevalence of translamellar cracking. • Angle study comforts the influence of basal/prismatic slip on crack path.

36 MATERIALS SCIENCE↗
DOE OSTI2019

R and DE Robotic Sensor Intern Team: Sensor Suite Software and User Interface

The sensor tree developed for the H-Canyon tunnel inspection project produces a large quantity of camera and Lidar data. A postprocessing pipeline was created to facilitate the analysis of these data. The pipeline follows the following routine: 1) Registration - Salient local features in subsequent scans are identified and used to align the scans with one another. This allows production of a single cohesive map of the tunnel. 2) Sensor Fusion - RGB camera data is 'painted' onto the depth data created by the Lidar. 3) Segmentation - Individual surfaces from within the scene are segmented out separately for individual analysis. 4) Degradation Analysis - Automatic mapping of local deviation from the plane in position or surface normal is performed, as is detection of exposed rebar using Lidar return intensity. 5) Obstacle Detection - Lidar data is also used in real time to detect obstacles in the environment. This could give feedback to the operator to successfully drive in the tunnel. 6) Graphic User Interface - a custom GUI was created to facilitate customer use of the system. The system provides real-time feedback from both the cameras and Lidar. During alignment, local features are used to recognize points within the walls that are especially unique and noticeable (salient) based on their deviation from the expected surface of the wall. These are used as landmarks to register the clouds, which also informs precise localization of the base at each scan point. After registration, clouds are 'painted' with RGB data from the spherical cameras, as shown below. The painted clouds provide a 3D, colorized model of the environment which improves the realism of the experience for the user. Painting also labels high-resolution color imagery from the 3D cameras with depth values. Following sensor fusion, individual walls and cylindrical duct surfaces are segmented from the scene for individual analysis using a Random Sample Consensus algorithm. This algorithm allows segmentation of planar walls and cylindrical ducts. Once individual walls are identified, each point within the wall can be compared to its neighbors and the wall overall in order to detect local areas with damaged concrete. Automatic rebar detection is also performed using the contrast in Lidar return intensity between iron and concrete. The Lidar point-cloud data is also used for obstacle detection. As the Lidar spins, various algorithms are run to analyze the current 'slice' and obstacles in the environment are determined and registered on a cost-map. A Graphical User Interface (GUI) was developed to allow the user to easily command the crawler to set the rotational speed of the Lidar, perform a scan, and register and save the 3D generated cloud after a scan is done. For the good of the crawler, a critical aspect of the GUI design is to allow the user to cancel a scan any time after it is commanded and before it is saved.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

3D tomodosimetry using long scintillating fibers: A feasibility study

Purpose: 3D dosimetry is recognized as an ideal for patient-specific quality assurance (QA) of highly conformal radiotherapy treatments. However, existing 3D dosimeters are not straightforward to implement in the clinic, as their read-out procedure is often tedious and their accuracy, precision, and/or sample size exhibit limitations. The purpose of this work is to develop a 3D dosimeter based on the concept of tomodosimetry inside concentric cylindrical planes using long scintillating fibers for the QA of modern radiotherapy techniques such as intensity-modulated radiation therapy (IMRT) or intensity-modulated arc therapy (IMAT).Methods: Using a model-based simulation, scintillating fibers were modeled on three concentric cylindrical planes of radii 2.5, 5.0, and 7.5 cm, inside a 10 cm radius water-equivalent cylinder phantom. The phantom was set to rotate around its central axis, made parallel to the linac gantry axis of rotation. Light acquisitions were simulated using the calculated dose from the treatment planning software and reconstructed in each cylindrical plane at a resolution of 1 mm{sup 2} using a total-variation minimization iterative reconstruction algorithm. The 3D dose was then interpolated from the reconstructed cylindrical plane doses at a resolution of 1 mm{sup 3}. Different scintillating fiber patterns were compared by varying the angle of each fiber in its cylindrical plane and introducing a light-tight cut in each fiber. The precision of the reconstructed cylindrical dose distribution was evaluated using a Poisson modeling of the acquired light signals and the accuracy of the interpolated 3D dose was evaluated using an IMRT clinical plan for a prostate case.Results: Straight scintillating fiber patterns with light-tight cuts were the most accurate in cylindrical dose reconstruction, showing less than 0.5 mm distance-to-agreement in dose gradients and a mean local dose difference of less than 0.2% in the high dose region for a 10 × 10 cm{sup 2} field. The precision attained with this fiber configuration was less than 0.9% in the high dose, low gradient region of an IMRT segment for light acquisitions of 0.1 MU over a 360 degree rotation of the cylinder phantom. 3D dose interpolation for the IMRT clinical plan yielded an overall dose difference with the reference input of less than 1%, except in high dose gradients.Conclusions: Using long scintillating fibers inside rotating, concentric cylindrical planes, the authors demonstrate that their tomodosimetry method has the potential for high resolution, precise, and accurate 3D dosimetry. Moreover, because of its water-equivalence and rotational symmetry, this design should find interesting application for both treatment QA and machine commissioning.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

A hybrid machine-learning approach for analysis of methane hydrate formation dynamics in porous media with synchrotron CT imaging

Fast multi-phase processes in methane hydrate bearing samples pose a challenge for quantitative micro-computed tomography study and experiment steering due to complex tomographic data analysis involving time-consuming segmentation procedures. This is because of the sample's multi-scale structure, which changes over time, low contrast between solid and fluid materials, and the large amount of data acquired during dynamic processes. Here, a hybrid approach is proposed for the automatic segmentation of tomographic data from time-resolved imaging of methane gas-hydrate formation in sandy granular media, which includes a deep-learning 3D U-Net model. To prepare a training dataset for the 3D U-Net, a technique to automate data labeling based on sample-specific information about the mineral matrix immobility and occasional fluid movement in pores is proposed. Automatic segmentation allowed for studying properties of the hydrate growth in pores, as well as dynamic processes such as incremental flow and redistribution of pore brine. Results of the quantitative analysis showed that for typical gas-hydrate stability parameters (100 bar methane pressure, 7°C temperature) the rate of formation is slow (less than 1% per hour), after which the surface area of contact between brine and gas increases, resulting in faster formation (2.5% per hour). Hydrate growth reaches the saturation point after 11 h of the experiment. Finally, the efficacy of the proposed segmentation scheme in on-the-fly automatic data analysis and experiment steering with zooming to regions of interest is demonstrated.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Multiscale porosity characterization in additively manufactured polymer nanocomposites using micro-computed tomography

Extrusion-based additive manufacturing (AM) of polymer composites exhibits complex thermally driven phenomena that introduce severe discontinuities in the internal structure across length scales, especially voids or porosity. This study utilizes a high-throughput porosity characterization technique to analyze large datasets from numerous micro-computed tomography (mu CT) scans to capture the influence of AM print parameters on the size, shape, and location of porosity across multiple print layers (up to a few cm) on fused granular fabrication (FGF) printers. The materials investigated include nanocomposite formulations based on commercially relevant nylon-12 and polyether ketone ketone (PEKK) materials comprising nano- or micro- sized fillers. The estimated global porosity follows an inverse linear correlation against the bulk density of the printed samples. Increasing the extrusion multiplier (EM) and the nozzle temperature while decreasing the print speeds reduces the global porosity. Outlier analyses (local porosity morphology) show that faster print speeds and higher extrusion rates result in long, slender inter-layer voids, while lower nozzle temperatures lead to large, symmetrical, inter-bead voids (at the bead junction). Lack of active chamber temperature increases inter-layer and intra-bead voids with a two-fold increase in global porosity. Overall, the micro filler-reinforced composites exhibit higher global porosity than nanofiller-reinforced composites, which is attributed to the increased mismatch in the thermal expansion coefficient between the filler and the polymers used in the study.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Deep learning for Alzheimer's disease: Mapping large-scale histological tau protein for neuroimaging biomarker validation

Abnormal tau inclusions are hallmarks of Alzheimer's disease and predictors of clinical decline. Several tau PET tracers are available for neurodegenerative disease research, opening avenues for molecular diagnosis in vivo. However, few have been approved for clinical use. Understanding the neurobiological basis of PET signal validation remains problematic because it requires a large-scale, voxel-to-voxel correlation between PET and (immuno) histological signals. Large dimensionality of whole human brains, tissue deformation impacting co-registration, and computing requirements to process terabytes of information preclude proper validation. We developed a computational pipeline to identify and segment particles of interest in billion-pixel digital pathology images to generate quantitative, 3D density maps. The proposed convolutional neural network for immunohistochemistry samples, IHCNet, is at the pipeline's core. We have successfully processed and immunostained over 500 slides from two whole human brains with three phospho-tau antibodies (AT100, AT8, and MC1), spanning several terabytes of images. Our artificial neural network estimated tau inclusion from brain images, which performs with ROC AUC of 0.87, 0.85, and 0.91 for AT100, AT8, and MC1, respectively. Introspection studies further assessed the ability of our trained model to learn tau-related features. We present an end-to-end pipeline to create terabytes-large 3D tau inclusion density maps co-registered to MRI as a means to facilitate validation of PET tracers.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-J-230: Evaluation of ViewRay 0.35 T MRI Normal Structure Segmentation

Purpose: In order to take advantage of the high resolution soft tissue imaging available in MR images, we investigated 3D images obtained with the low field 0.35 T MR in ViewRay to serve as an alternative to CT scans for radiotherapy treatment planning. In these images, normal and target structure delineation can be visualized. Assessment is based upon comparison with the CT images and the ability to produce comparable contours. Methods: Routine radiation oncology CT scans were acquired on five patients. Contours of brain, brainstem, esophagus, heart, lungs, spinal cord, and the external body were drawn. The same five patients were then scanned on the ViewRay TrueFISP-based imaging pulse sequence. The same organs were selected on the MR images and compared to those from the CT scan. Physical volume and the Dice Similarity Coefficient (DSC) were used to assess the contours from the two systems. Image quality stability was quantitatively ensured throughout the study following the recommendations of the ACR MR accreditation procedure. Results: The highest DSC of 0.985, 0.863, and 0.843 were observed for brain, lungs, and heart respectively. On the other hand, the brainstem, spinal cord, and esophagus had the lowest DSC. Volume agreement was most satisfied for the heart (within 5%) and the brain (within 2%). Contour volume for the brainstem and lung (a widely dynamic organ) varied the most (27% and 19%). Conclusion: The DSC and volume measurements suggest that the results obtained from ViewRay images are quantitatively consistent and comparable to those obtained from CT scans for the brain, heart, and lungs. MR images from ViewRay are well-suited for treatment planning and for adaptive MRI-guided radiotherapy. The physical data from 0.35 T MR imaging is consistent with our geometrical understanding of normal structures.

60 APPLIED LIFE SCIENCES↗