SEARCH · Search NASA
Results for “tomographic”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Method and apparatus to obtain limited angle tomographic images from stationary gamma cameras
A nuclear imaging system and method for performing three-dimensional imaging of anatomical structures. The system and method includes two or more gamma ray detectors each used in combination with a variable-slant hole collimator. The detectors are positioned in close proximity to, or in contact with, the structure being imaged. The detectors remain in a stationary position during the data collection process. An imaging or reconstruction method is then used to reconstruct a three-dimensional image from the data derived from the detectors.
Limited-View Cone Beam CT Reconstructions using 3D Subvolume-based Supervised and Adversarial Learning: Validation using Hydrodynamic simulations and Experimental Tomographic data
Explore the source record for details and available documents.
High Fidelity Tomographic Density Reconstructions
Explore the source record for details and available documents.
Box-Constrained L1/L2 Minimization in Single-View Tomographic Reconstruction
This manuscript will be submitted to one of two journals (SIAM Imaging, or Applied Mathematics and Computation)
Sparse-view tomographic reconstruction of MagLIF emission volumes using learned basis functions.
Abstract not provided.
Sparse-view tomographic reconstruction of MagLIF emission volumes using learned basis functions.
Abstract not provided.
SALSA3D: A Tomographic Model of the Compressional Wavespeed in the Earth?s Mantle for Improved Seismic Event Location .
Abstract not provided.
Single-View Tomographic Reconstruction Using Learned Primal Dual
Explore the source record for details and available documents.
Virtual Volumetric Additive Manufacturing (VirtualVAM)
Abstract Tomographic volumetric additive manufacturing (VAM) produces arbitrary 3D geometries by exposure of a rotating volume of photopolymer resin to tomographically‐patterned illumination. This enables high speed, layer‐less printing of parts from a wide range of photopolymers not amenable to layer‐by‐layer processes. Since the entire geometry is produced at once over the course of a few seconds to minutes, molecular diffusion length scales become significant to the printing process. Understanding these molecular reaction and diffusion processes is imperative for advancing VAM to a usable technology. These processes are experimentally very difficult to monitor and measure. Herein, VirtualVAM ‐ a simulation framework for modeling the tomographic VAM process, is developed and experimentally validated. VirtualVAM simulates reaction, diffusion, and heat generation processes over the course of a print with single‐voxel resolution. From a few experimentally‐determined input parameters and a set of images for projection, VirtualVAM is able to generate a large spatio‐temporal data set for any given tomographic VAM print. Using VirtualVAM, a number of experimentally‐unattainable aspects of the VAM process are investigated such as single‐voxel conversion profiles, effect of molecular oxygen, and stopping time determination. VirtualVAM also enables the optimization of exposure patterns to further improve contrast between in‐part and out‐of‐part delivered dose.
Analysis of Seismic Wave Amplification in Sedimentary Basins Using 3D Wavefield Simulations: Nenana Basin, Central Alaska
We investigate seismic wave amplification in Nenana basin, central Alaska, using 3D seismic wavefield simulations. We quantify frequency-dependent amplification by comparing synthetic seismograms among four different seismic velocity models: (a) a tomographic model, (b) a tomographic model with the uppermost 6.5 km replaced by a 1D basin profile, (c) a tomographic model with an embedded ellipsoidal basin, and (d) a tomographic model with an embedded realistic basin. For each model we perform wavefield simulations accurate up to 2 Hz for a set of 10 different earthquakes, which provide a range of frequencies and incident angles for waves interacting with the basin. Average amplification ratios are four on the horizontal components and seven on the vertical component. The dominant influence of the amplification is the 3D geometry of the basin, not the slow-velocity profile near the surface. Our synthetic amplification ratios are in general agreement with amplification estimates obtained from 14 stations that recorded the same set of earthquakes. Our approach offers a general strategy for documenting frequency-dependent basin amplification for a region with realistic basin structures and local earthquakes.
Quantitative phenotyping of crop roots with spectral electrical impedance tomography: a rhizotron study with optimized measurement design
Background: Root systems are key contributors to plant health, resilience, and, ultimately, yield of agricultural crops. To optimize plant performance, phenotyping trials are conducted to breed plants with diverse root traits. However, traditional analysis methods are often labour-intensive and invasive to the root system, therefore limiting high-throughput phenotyping. Spectral electrical impedance tomography (sEIT) could help as a non-invasive and cost-efficient alternative to optical root analysis, potentially providing 2D or 3D spatio-temporal information on root development and activity. Although impedance measurements have been shown to be sensitive to root biomass, nutrient status, and diurnal activity, only few attempts have been made to employ tomographic algorithms to recover spatially resolved information on root systems. In this study, we aim to establish relationships between tomographic electrical polarization signatures and root traits of different fine root systems (maize, pinto bean, black bean, and soy bean) under hydroponic conditions. Results: Our results show that, with the use of an optimized data acquisition scheme, sEIT is capable of providing spatially resolved information on root biomass and root surface area for all investigated root systems. We found strong correlations between the total polarization strength and the root biomass (R 2 = 0.82) and root surface area (R 2 = 0.8). Our findings suggest that the captured polarization signature is dominated by cell-scale polarization processes. Additionally, we demonstrate that the resolution characteristics of the measurement scheme can have a significant impact on the tomographic reconstruction of root traits. Conclusion: Our findings showcase that sEIT is a promising tool for the tomographic reconstruction of root traits in high-throughput root phenotyping trials and should be evaluated as a substitute for traditional, often time-consuming, root characterization methods.
Sparse Matrix-Based HPC Tomography
Tomographic imaging has benefited from advances in X-ray sources, detectors and optics to enable novel observations in science, engineering and medicine. These advances have come with a dramatic increase of input data in the form of faster frame rates, larger fields of view or higher resolution, so high performance solutions are currently widely used for analysis. Tomographic instruments can vary significantly from one to another, including the hardware employed for reconstruction: from single CPU workstations to large scale hybrid CPU/GPU supercomputers. Furthermore, flexibility on the software interfaces and reconstruction engines are also highly valued to allow for easy development and prototyping. This paper presents a novel software framework for tomographic analysis that tackles all aforementioned requirements. The proposed solution capitalizes on the increased performance of sparse matrix-vector multiplication and exploits multi-CPU and GPU reconstruction over MPI. Furthermore, the solution is implemented in Python and relies on CuPy for fast GPU operators and CUDA kernel integration, and on SciPy for CPU sparse matrix computation. As opposed to previous tomography solutions that are tailor-made for specific use cases or hardware, the proposed software is designed to provide flexible, portable and high-performance operators that can be used for continuous integration at different production environments, but also for prototyping new experimental settings or for algorithmic development. The experimental results demonstrate how our implementation can even outperform state-of-the-art software packages used at advanced X-ray sources worldwide.
Elucidating the Onset of Plasticity in Sliding Contacts Using Differential Computational Orientation Tomography
Abstract Depending on the mechanical and thermal energy introduced to a dry sliding interface, the near-surface regions of the mated bodies may undergo plastic deformation. In this work, we use large-scale molecular dynamics simulations to generate “differential computational orientation tomographs” (dCOT) and thus highlight changes to the microstructure near tribological FCC alloy surfaces, allowing us to detect subtle differences in lattice orientation and small distances in grain boundary migration. The analysis approach compares computationally generated orientation tomographs with their undeformed counterparts via a simple image analysis filter. We use our visualization method to discuss the acting microstructural mechanisms in a load- and time-resolved fashion, focusing on sliding conditions that lead to twinning, partial lattice rotation, and grain boundary-dominated processes. Extracting and laterally averaging the color saturation value of the generated tomographs allows us to produce quantitative time- and depth-resolved maps that give a good overview of the progress and severity of near-surface deformation. Corresponding maps of the lateral standard deviation in the color saturation show evidence of homogenization processes occurring in the tribologically loaded microstructure, frequently leading to the formation of a well-defined separation between deformed and undeformed regions. When integrated into a computational materials engineering framework, our approach could help optimize material design for tribological and other deformation problems. Graphic Abstract .
Seeing the whole picture: Methods for getting the most from micro X-ray computed tomography of TRISO nuclear fuel particles
Tristructural isotropic (TRISO) coated fuel particles are a nuclear fuel form under extensive study for use in advanced nuclear reactor concepts. TRISO fuels are subjected to high temperature neutron irradiations and then examined to assess their performance by determining fission product retention and studying morphological changes. Micro X-ray computed tomography is one method of nondestructively studying the effects of TRISO performance. This work addresses the need for image processing to remove X-ray tomographic reconstruction artifacts that prevent the study of TRISO features, as the TRISO particles’ high Z kernel can introduce metal artifacts that degrade the image quality in the surrounding low Z coating layers. These metal artifacts were reduced by imaging the TRISO particles with both high- and low-energy X-rays and applying a mask to the radiographs obtained with low-energy X-rays to digitally remove the dense fuel kernel region. These masked radiographs were then used to produce a tomographic reconstruction which was combined with the tomographic reconstruction of the high-energy data. This enabled the relatively-low-density TRISO buffer layer to be examined in more detail, providing information on irradiation induced dimensional changes of the coatings. This methodology, which helps see the full picture of a TRISO particle, is not limited to nuclear fuels but can be applied to systems that contain highly attenuating material surrounded by less dense materials.
Accurate real space iterative reconstruction (RESIRE) algorithm for tomography
Tomography has made a revolutionary impact on the physical, biological and medical sciences. The mathematical foundation of tomography is to reconstruct a three-dimensional (3D) object from a set of two-dimensional (2D) projections. As the number of projections that can be measured from a sample is usually limited by the tolerable radiation dose and/or the geometric constraint on the tilt range, a main challenge in tomography is to achieve the best possible 3D reconstruction from a limited number of projections with noise. Over the years, a number of tomographic reconstruction methods have been developed including direct inversion, real-space, and Fourier-based iterative algorithms. Here, we report the development of a real-space iterative reconstruction (RESIRE) algorithm for accurate tomographic reconstruction. RESIRE iterates between the update of a reconstructed 3D object and the measured projections using a forward and back projection step. The forward projection step is implemented by the Fourier slice theorem or the Radon transform, and the back projection step by a linear transformation. Our numerical and experimental results demonstrate that RESIRE performs more accurate 3D reconstructions than other existing tomographic algorithms, when there are a limited number of projections with noise. Furthermore, RESIRE can be used to reconstruct the 3D structure of extended objects as demonstrated by the determination of the 3D atomic structure of an amorphous Ta thin film. We expect that RESIRE can be widely employed in the tomography applications in different fields. Finally, to make the method accessible to the general user community, the MATLAB source code of RESIRE and all the simulated and experimental data are available at https://zenodo.org/record/7273314.
Dark energy survey year 3 results: Cosmology with peaks using an emulator approach
ABSTRACT We constrain the matter density Ωm and the amplitude of density fluctuations σ8 within the ΛCDM cosmological model with shear peak statistics and angular convergence power spectra using mass maps constructed from the first three years of data of the Dark Energy Survey (DES Y3). We use tomographic shear peak statistics, including cross-peaks: peak counts calculated on maps created by taking a harmonic space product of the convergence of two tomographic redshift bins. Our analysis follows a forward-modelling scheme to create a likelihood of these statistics using N-body simulations, using a Gaussian process emulator. We take into account the uncertainty from the remaining, largely unconstrained ΛCDM parameters (Ωb, ns, and h). We include the following lensing systematics: multiplicative shear bias, photometric redshift uncertainty, and galaxy intrinsic alignment. Stringent scale cuts are applied to avoid biases from unmodelled baryonic physics. We find that the additional non-Gaussian information leads to a tightening of the constraints on the structure growth parameter yielding $S_8~\equiv ~\sigma _8\sqrt{\Omega _{\mathrm{m}}/0.3}~=~0.797_{-0.013}^{+0.015}$ (68 per cent confidence limits), with a precision of 1.8 per cent, an improvement of 38 per cent compared to the angular power spectra only case. The results obtained with the angular power spectra and peak counts are found to be in agreement with each other and no significant difference in S8 is recorded. We find a mild tension of $1.5 \, \sigma$ between our study and the results from Planck 2018, with our analysis yielding a lower S8. Furthermore, we observe that the combination of angular power spectra and tomographic peak counts breaks the degeneracy between galaxy intrinsic alignment AIA and S8, improving cosmological constraints. We run a suite of tests concluding that our results are robust and consistent with the results from other studies using DES Y3 data.
Hyper Suprime-Cam Year 3 results: Cosmology from cosmic shear two-point correlation functions
Here, we perform a blinded cosmology analysis with cosmic shear two-point correlation functions measured from more than 25 million galaxies in the Hyper Suprime-Cam three-year shear catalog in four tomographic redshift bins ranging from 0.3 to 1.5. After conservative masking and galaxy selection, the survey covers 416 deg 2 of the northern sky with an effective galaxy number density of 15 arcmin –2 over the four redshift bins. The 2PCFs adopted for cosmology analysis are measured in the angular range; 7.1 <θ/arcmin < 56.6 for ξ + and 31.2 < θ/arcmin < 248 for ξ – , with a total signal-to-noise ratio of 26.6. We apply a conservative, wide, flat prior on the photometric redshift errors on the last two tomographic bins, and the relative magnitudes of the cosmic shear amplitude across four redshift bins allow us to calibrate the photometric redshift errors. With this flat prior on redshift errors, we find Ω m = 0.256$^{+0.056}_{–0.044}$ and S 8 ≡ σ 8 $\sqrt{Ω_{m}/0.3}$ = 0.769$^{+0.031}_{–0.034}$ (both 68% C.I.) for a flat Λ cold dark matter cosmology. We find, after unblinding, that our constraint on S 8 is consistent with the Fourier space cosmic shear and the 3 × 2 pt analyses on the same HSC dataset. We carefully study the potential systematics from astrophysical and systematic model uncertainties in our fiducial analysis using synthetic data, and report no biases (including projection bias in the posterior space) greater than 0.5σ in the estimation of S 8 . Our analysis hints that the mean redshifts of the two highest tomographic bins are higher than initially estimated. In addition, a number of consistency tests are conducted to assess the robustness of our analysis. Comparing our result with Planck-2018 cosmic microwave background observations, we find a ~2σ tension for the Λ CDM model.