Evaluation of image segmentation techniques for image-based rock property estimation
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Purswani, Prakash↗
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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.
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Three-dimensional (3D) velocity map imaging: from technique to application
The velocity map imaging (VMI) technique was first introduced by Eppink and Parker in 1997, as an improvement to the original ion imaging method by Houston and Chandler in 1987. The method has gained huge popularity over the past two decades and has become a standard tool for measuring high-resolution translational energy and angular distributions of ions and electrons. VMI has evolved gradually from 2D momentum measurements to 3D measurements with various implementations and configurations. The most recent advancement has brought unprecedented 3D performance to the technique in terms of resolutions (both spatial and temporal), multi-hit capability as well as acquisition speed while maintaining many attractive attributes afforded by conventional VMI such as being simple, cost-effective, visually appealing and versatile. Here, in this tutorial, we will discuss many technical aspects of the recent advancement and its application in probing correlated chemical dynamics.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Development of techniques for gantryless associated-particle imaging
To move fast-neutron radiography using the associated-particle imaging technique from the laboratory to the field, the development of new analysis techniques is required. In particular, the relative positions of the source and detectors need to be determined when they have been placed by hand, the normalization for a particular source–detector geometry needs to be determined without a measurement in the same geometry with no object present, and accurate image stitching is required when multiple detector positions are necessary to image an object. The present work describes methods that employ transmission neutron data to localize a fast-neutron imaging panel with respect to the neutron source, calculate a normalization for a given source–detector geometry, stitch images together, and describes the required system calibrations. The reported techniques enable in-field neutron radiography for cases in which the source–detector geometry is not well known a priori and where operational constraints preclude a normalization measurement.
Heath, Matthew↗
Image Processing Techniques for Structural Dynamics Testing using Radiographic Images.
Abstract not provided.
Rohe, Daniel Peter↗
Image Processing Techniques for Structural Dynamics Testing using Radiographic Images.
Abstract not provided.
Rohe, Daniel Peter↗
Image Processing Techniques for Structural Dynamics Testing using Radiographic Images.
Abstract not provided.
Rohe, Daniel↗
An Internal Digital Image Correlation Technique for High-Strain Rate Dynamic Experiments
Full-field, quantitative visualization techniques, such as digital image correlation (DIC), have unlocked vast opportunities for experimental mechanics. However, DIC has traditionally been a surface measurement technique, and has not been extended to perform measurements on the interior of specimens for dynamic, full-scale laboratory experiments. This limitation restricts the scope of physics which can be investigated through DIC measurements, especially in the context of heterogeneous materials. The focus of this study is to develop a method for performing internal DIC measurements in dynamic experiments. The aim is to demonstrate its feasibility and accuracy across a range of stresses (up to 650 MPa), strain rates (10 3 - 10 6 s -1 ), and high-strain rate loading conditions (e.g., ramped and shock wave loading). Internal DIC is developed based on the concept of applying a speckle pattern at an inner-plane of a transparent specimen. The high-speed imaging configuration is coupled to the traditional dynamic experimental setups, and is focused on the internal speckle pattern. During the experiment, while the sample deforms dynamically, in-plane, two-dimensional deformations are measured via correlation of the internal speckle pattern. In this study, the viability and accuracy of the internal DIC technique is demonstrated for split-Hopkinson (Kolsky) pressure bar (SHPB) and plate impact experiments. The internal DIC experimental technique is successfully demonstrated in both the SHPB and plate impact experiments. In the SHPB setting, the accuracy of the technique is excellent throughout the deformation regime, with measurement noise of approximately 0.2% strain. In the case of plate impact experiments, the technique performs well, with error and measurement noise of 1% strain. The internal DIC technique has been developed and demonstrated to work well for full-scale dynamic high-strain rate and shock laboratory experiments, and the accuracy is quantified. Here, the technique can aid in investigating the physics and mechanics of the dynamic behavior of materials, including local deformation fields around dynamically loaded material heterogeneities.
36 MATERIALS SCIENCE↗
Use of a non-collinear wave mixing technique to image internal microscale damage in concrete
This research demonstrates the feasibility of using a non-collinear wave mixing technique to image internal microscale damage throughout the interior volume of a relatively large (28 cm thick) concrete component. By exploiting the underlying mechanics of nonlinear wave mixing, it is possible to mix two incident waves with frequencies low enough to propagate without being scattered by the inherently heterogenous, concrete microstructure, while still being sensitive to damage features with length scales well below these incident wavelengths. For this study, scanning and imaging is accomplished by manually adjusting the locations of the two incident waves, while a knowledge of the wave speeds in concrete plus synchronization identifies the location of the mixing zone—the specific volume of concrete being imaged. The viability of the proposed technique is demonstrated by examining a concrete prism specimen with known, embedded internal microscale damage.
Kim, Jin-Yeon (ORCID:0000000255180032)↗
Benchmarking image processing techniques for porosity measurement in polymer additive manufacturing: Review and experimental analysis
An image processing workflow is proposed for porosity measurement in polymer additive manufacturing. Various techniques, including global and local thresholding, region growing, and K-means clustering, were applied to microscopic images of carbon fiber reinforced acrylonitrile butadiene styrene (CF-ABS) and benchmarked for their ability to accurately measure porosity. Global methods included Otsu, minimum error, iterative, and entropy-based thresholding, while local methods included Niblack, Bernsen, Sauvola, and Bradley-Roth algorithms. Artificial uneven illumination was introduced to test local adaptive thresholds. Results showed significant differences in porosity values across methods. Otsu, region growing, and K-means clustering excelled under uniform illumination, while Sauvola and Bradley-Roth performed better with uneven illumination. Comparison with X-ray computed tomography (XCT) revealed slightly lower porosity values (2.55 %) than optimized methods (2.73–2.79 %) due to XCT's lower resolution excluding smaller pores. While XCT offers finer pore detection, it limits sample volume and underestimates porosity due to spatial variation. Validation using artificial grayscale images with 5 % porosity confirmed that Otsu, Bradley-Roth, region growing, and Sauvola algorithms produced accurate results. Although tested on a single material system, these methods can be adapted to others with optimization. In conclusion, given XCT's high computational and time costs, this study highlights suitable image processing techniques as cost-effective alternatives for porosity analysis in polymer composites.
Additive manufacturing↗
Multiscale in-situ characterization of static recrystallization using dark-field X-ray microscopy and high-resolution X-ray diffraction
Abstract Dark-field X-ray microscopy (DFXM) is a high-resolution, X-ray-based diffraction microstructure imaging technique that uses an objective lens aligned with the diffracted beam to magnify a single Bragg reflection. DFXM can be used to spatially resolve local variations in elastic strain and orientation inside embedded crystals with high spatial (~ 60 nm) and angular (~ 0.001°) resolution. However, as with many high-resolution imaging techniques, there is a trade-off between resolution and field of view, and it is often desirable to enrich DFXM observations by combining it with a larger field-of-view technique. Here, we combine DFXM with high-resolution X-ray diffraction (HR-XRD) applied to an in-situ investigation of static recrystallization in an 80% hot-compressed Mg–3.2Zn–0.1Ca wt.% (ZX30) alloy. Using HR-XRD, we track the relative grain volume of > 8000 sub-surface grains during annealing in situ. Then, at several points during the annealing process, we “zoom in” to individual grains using DFXM. This combination of HR-XRD and DFXM enables multiscale characterization, used here to study why particular grains grow to consume a large volume fraction of the annealed microstructure. This technique pairing is particularly useful for small and/or highly deformed grains that are often difficult to resolve using more standard diffraction microstructure imaging techniques.
36 MATERIALS SCIENCE↗
Manifold projection image segmentation for nano-XANES imaging
As spectral imaging techniques are becoming more prominent in science, advanced image segmentation algorithms are required to identify appropriate domains in these images. We present a version of image segmentation called manifold projection image segmentation (MPIS) that is generally applicable to a broad range of systems without the need for training because MPIS uses unsupervised machine learning with a few physically motivated hyperparameters. We apply MPIS to nanoscale x-ray absorption near edge structure (XANES) imaging, where XANES spectra are collected with nanometer spatial resolution. We show the superiority of manifold projection over linear transformations, such as the commonly used principal component analysis (PCA). Moreover, MPIS maintains accuracy while reducing computation time and sensitivity to noise compared to the standard nano-XANES imaging analysis procedure. Finally, we demonstrate how multimodal information, such as x-ray fluorescence data and spatial location of pixels, can be incorporated into the MPIS framework. We propose that MPIS is adaptable for any spectral imaging technique, including scanning transmission x-ray microscopy, where the length scale of domains is larger than the resolution of the experiment.
36 MATERIALS SCIENCE↗
Noninvasive Imaging of a Liquid Jet
Liquid jets are found in many applications, from printing to manufacturing to entertainment. Herein this study uses three different noninvasive imaging modalities to compare resulting images of a liquid jet operating at three Reynolds numbers that cover laminar, transitional, and turbulent flow. Selected measurement quantities from each image type are also compared. High-speed backlit (BL) imaging is a simple imaging technique found in many laboratories, and this is compared to two high-speed X-ray imaging techniques, white beam (WB) imaging and focused beam (FB) radiography. BL imaging can provide a wide field of view and is easy to implement, but it only shows the presence or absence of liquid. WB imaging can show detailed contours on the surface of the liquid jet, but the imaging region is much smaller. FB radiography produces a point-source measurement and can provide the quantitative, instantaneous local liquid path length, termed the equivalent path length (EPL). All three techniques provide similar measures of jet thickness, with the FB measurements having less variation. FB measurements can also provide detailed cross sections of the average liquid jet thickness at high spatial resolutions.
42 ENGINEERING↗
Machine learning and deep learning for brain tumor MRI image segmentation
Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic resonance imaging (MRI) is a commonly used imaging technique for capturing brain images. Both machine learning and deep learning techniques are popular in analyzing MRI images. This article reviews some commonly used machine learning and deep learning techniques for brain tumor MRI image segmentation. The limitations and advantages of the reviewed machine learning and deep learning methods are discussed. Even though each of these methods has a well-established status in their individual domains, the combination of two or more techniques is currently an emerging trend.
Research & Experimental Medicine↗
Image-Guided Intraoperative Assessment of Surgical Margins in Oral Cavity Squamous Cell Cancer: A Diagnostic Test Accuracy Review
The assessment of resection margins during surgery of oral cavity squamous cell cancer (OCSCC) dramatically impacts the prognosis of the patient as well as the need for adjuvant treatment in the future. Currently there is an unmet need to improve OCSCC surgical margins which appear to be involved in around 45% cases. Intraoperative imaging techniques, magnetic resonance imaging (MRI) and intraoral ultrasound (ioUS), have emerged as promising tools in guiding surgical resection, although the number of studies available on this subject is still low. The aim of this diagnostic test accuracy (DTA) review is to investigate the accuracy of intraoperative imaging in the assessment of OCSCC margins. By using the Cochrane-supported platform Review Manager version 5.4, a systematic search was performed on the online databases MEDLINE-EMBASE-CENTRAL using the keywords “oral cavity cancer, squamous cell carcinoma, tongue cancer, surgical margins, magnetic resonance imaging, intraoperative, intra-oral ultrasound”. Ten papers were identified for full-text analysis. The negative predictive value (cutoff < 5 mm) for ioUS ranged from 0.55 to 0.91, that of MRI ranged from 0.5 to 0.91; accuracy analysis performed on four selected studies showed a sensitivity ranging from 0.07 to 0.75 and specificity ranging from 0.81 to 1. Image guidance allowed for a mean improvement in free margin resection of 35%. IoUS shows comparable accuracy to that of ex vivo MRI for the assessment of close and involved surgical margins, and should be preferred as the more affordable and reproducible technique. Both techniques showed higher diagnostic yield if applied to early OCSCC (T1–T2 stages), and when histology is favorable.
60 APPLIED LIFE SCIENCES↗
STEM Ptychographic Holography of Electric and Magnetic Potentials
The development of fast an efficient direct electron imaging detectors have enabled the advancement of phase-imaging techniques in STEM such as iterative ptychography. As beneficial as these techniques are to imaging phase objects, the information recorded in the raw data is due to phase gradients across the probe, so it can be challenging to reconstruct slowly varying phase at lower spatial frequencies such as those induced by electric or magnetic potentials within the specimen. STEM holography [1,2] is an interferometric 4D-STEM technique where electrons in the beam are coherently divided into a superposition of two or more spatially separated probes which are then scanned over the specimen. For example, in a two-beam superposition, the two probes form overlapping bright field discs at the detector which then interfere (left of Fig. 1). Furthermore, if one probe passes through vacuum while the other transmits through the specimen, the resulting relative phase shift can be measured by recording shifts in the interference pattern. STEM holography is thus directly sensitive to the phase of the probe relative to the reference beam, and this phase can be measured regardless of the convergence angle of the probe, unlike single beam ptychography.
Biological Sciences↗
Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network
As a label‐free imaging technique, quantitative phase imaging (QPI) provides optical path length information of transparent specimens for various applications in biology, materials science, and engineering. Multispectral QPI measures quantitative phase information across multiple spectral bands, permitting the examination of wavelength‐specific phase and dispersion characteristics of samples. Herein, the design of a diffractive processor is presented that can all‐optically perform multispectral quantitative phase imaging of transparent phase‐only objects within a snapshot. The design utilizes spatially engineered diffractive layers, optimized through deep learning, to encode the phase profile of the input object at a predetermined set of wavelengths into spatial intensity variations at the output plane, allowing multispectral QPI using a monochrome focal plane array. Through numerical simulations, diffractive multispectral processors are demonstrated to simultaneously perform quantitative phase imaging at 9 and 16 target spectral bands in the visible spectrum. The generalization of these diffractive processor designs is validated through numerical tests on unseen objects, including thin Pap smear images. Due to its all‐optical processing capability using passive dielectric diffractive materials, this diffractive multispectral QPI processor offers a compact and power‐efficient solution for high‐throughput quantitative phase microscopy and spectroscopy.
36 MATERIALS SCIENCE↗
Surface hopping molecular dynamics simulation of ultrafast methyl iodide photodissociation mapped by Coulomb explosion imaging
Our simulation demonstrates the robustness of the Coulomb explosion imaging technique in studying methyl iodide photodissociation, and shows that it can be effectively used for imaging non-adiabatic transitions in coordinate space.
Ding, Yijue↗
Extraction of ground-state nuclear deformations from ultrarelativistic heavy-ion collisions: Nuclear structure physics context
The collective-flow-assisted nuclear shape-imaging method in ultrarelativistic heavy-ion collisions (UHICs) has recently been used to characterize nuclear collective states. In this paper, we assess the foundations of the shape-imaging technique employed in these studies. We argue that some current UHIC nuclear imaging techniques neglect fundamental aspects of spontaneous symmetry breaking and symmetry restoration in colliding ions and incorrectly infer one-body multipole moments from studies of nucleonic correlations. Therefore, the impact of this approach on nuclear structure research has been overstated. Conversely, efforts to incorporate existing knowledge on nuclear shapes into analysis pipelines can be beneficial for benchmarking tools and calibrating models used to extract information from ultrarelativistic heavy-ion experiments.
Nuclear data analysis & compilation↗