ML-driven image reconstruction can speed up microscopy data processing by 500x with image quality improvement up to 70%.
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Dingreville, Remi Philippe Michel↗
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In situ diagnostics of the crystal-growth process through neutron imaging: application to scintillators
Neutrons are known to be unique probes in situations where other types of radiation fail to penetrate samples and their surrounding structures. In this paper it is demonstrated how thermal and cold neutron radiography can provide time-resolved imaging of materials while they are being processed ( e.g. while growing single crystals). The processing equipment, in this case furnaces, and the scintillator materials are opaque to conventional X-ray interrogation techniques. The distribution of the europium activator within a BaBrCl:Eu scintillator (0.1 and 0.5% nominal doping concentrations per mole) is studied in situ during the melting and solidification processes with a temporal resolution of 5–7 s. The strong tendency of the Eu dopant to segregate during the solidification process is observed in repeated cycles, with Eu forming clusters on multiple length scales (only for clusters larger than ~50 µm, as limited by the resolution of the present experiments). It is also demonstrated that the dopant concentration can be quantified even for very low concentration levels (~0.1%) in 10 mm thick samples. The interface between the solid and liquid phases can also be imaged, provided there is a sufficient change in concentration of one of the elements with a sufficient neutron attenuation cross section. Tomographic imaging of the BaBrCl:0.1%Eu sample reveals a strong correlation between crystal fractures and Eu-deficient clusters. The results of these experiments demonstrate the unique capabilities of neutron imaging for in situ diagnostics and the optimization of crystal-growth procedures.
47 OTHER INSTRUMENTATION↗
Process-Structure-Property Modeling for Severe Plastic Deformation Processes Using Orientation Imaging Microscopy and Data-Driven Techniques
Machining is a severe plastic deformation process, wherein the workpiece material is subjected to high deformation rates and temperatures. During metal machining, the dynamic recrystallization mechanism causes grain refinement into the sub-micron range. In this study, we investigate the microstructure evolution of oxygen-free high conductivity copper (OFHC Cu) subject to a machining process where the cutting speed and rake angle are controlled to manipulate the process strain, strain rate, and temperatures. Microstructures of the deformed chips are quantified using orientation imaging microscopy and novel statistical descriptors that capture the morphology and local lattice misorientations generated during the several mechanistic stages of the dynamic recrystallization process. Mechanical properties of the resulting chips are quantified using spherical nanoindentation protocols. A multiple output Gaussian process regression model is used to simultaneously model the structure-property evolution, which differs from more common approaches that establish such relationships sequentially. This modeling strategy is particularly attractive since it can flexibly provide both structure and property uncertainty estimates. In addition, the statistical modeling framework allows for the inclusion of multi-fidelity data. The statistical metrics utilized serve as efficient microstructure descriptors, which retain the physics of the observed structures without having to introduce ad hoc microstructure feature definitions.
36 MATERIALS SCIENCE↗
A Standardized Analysis Process Using Digital Image Correlation to Calculate In Situ Cladding Strain from Modified Burst Tests for Fuel Performance Code Validation
Historical data collection on nuclear fuel cladding materials has focused on generating a statistically significant amount of data to assess the material and its failure behavior. Furthermore, data generated to support material model and failure criteria development were previously posttest evaluations, so a large number of tests was required to gain new understanding. A way to expedite this process is to develop techniques capable of generating large, high-fidelity data sets from a single test with lower uncertainty or quantified uncertainty. One such example of this approach is Oak Ridge National Laboratory’s use of modified burst tests (MBTs) to analyze the mechanical behavior and failure conditions of cladding during a simulated reactivity-initiated accident (RIA). Each test incorporates digital image correlation (DIC) analysis techniques that are used to assess the accumulated strain in situ, as well as eventual cladding failure. This work has been fruitful in defining strain-to-failure conditions for materials like silicon carbide (SiC) fiber–reinforced/SiC matrix composite tubes (SiC/SiC), iron-chromium-aluminum (FeCrAl) alloy tubes, and chromium-coated Zircaloy-4 tubes. However, there are numerous DIC software available, including open-source and proprietary software. The different DIC software use various algorithms to process images and calculate displacement values. Using these different software and algorithms can lead to varying results, and perhaps larger-than-expected uncertainties. In the present study, previously published MBT data encompassing a variety of test conditions were reanalyzed with two different DIC software to assess the variance in the calculated strain results. The data consisted of SiC/SiC, FeCrAl, and chromium-coated Zircaloy-4 tubes. Plots of the calculated strains during the transient revealed good agreement between the two DIC software. The average root-mean-square errors between the two software was 0.20% strain, which is slightly larger than a previously reported error value for these tests. In conclusion, this variance in results is low enough that this analysis method can be used for code validation.
Reactivity-initiated accident↗
SU-C-18C-06: Radiation Dose Reduction in Body Interventional Radiology: Clinical Results Utilizing a New Imaging Acquisition and Processing Platform
Purpose: To quantify the patient radiation dose reduction achieved during transarterial chemoembolization (TACE) procedures performed in a body interventional radiology suite equipped with the Philips Allura Clarity imaging acquisition and processing platform, compared to TACE procedures performed in the same suite equipped with the Philips Allura Xper platform. Methods: Total fluoroscopy time, cumulative dose area product, and cumulative air kerma were recorded for the first 25 TACE procedures performed to treat hepatocellular carcinoma (HCC) in a Philips body interventional radiology suite equipped with Philips Allura Clarity. The same data were collected for the prior 85 TACE procedures performed to treat HCC in the same suite equipped with Philips Allura Xper. Mean values from these cohorts were compared using two-tailed t tests. Results: Following installation of the Philips Allura Clarity platform, a 42.8% reduction in mean cumulative dose area product (3033.2 versus 1733.6 mGycm∧2, p < 0.0001) and a 31.2% reduction in mean cumulative air kerma (1445.4 versus 994.2 mGy, p < 0.001) was achieved compared to similar procedures performed in the same suite equipped with the Philips Allura Xper platform. Mean total fluoroscopy time was not significantly different between the two cohorts (1679.3 versus 1791.3 seconds, p = 0.41). Conclusion: This study demonstrates a significant patient radiation dose reduction during TACE procedures performed to treat HCC after a body interventional radiology suite was converted to the Philips Allura Clarity platform from the Philips Allura Xper platform. Future work will focus on evaluation of patient dose reduction in a larger cohort of patients across a broader range of procedures and in specific populations, including obese patients and pediatric patients, and comparison of image quality between the two platforms. Funding for this study was provided by Philips Healthcare, with 5% salary support provided to authors K. Pallav Kolli and Robert G. Gould for time devoted to the study. Data acquisition and analysis was performed by the authors independent of the funding source.
60 APPLIED LIFE SCIENCES↗
Camp Fire processed Landsat 8 images, pre-fire, during-fire, post-fire
The Camp Fire rapidly spread across a landscape in Butte County, California, toward the city of Paradise in the early morning hours of 8 November 2018. GEE acquires L8 data products from USGS that include Real-Time Tier 1 DN values, representing scaled, calibrated at-sensor radiance, and Level-1 Precision Terrain (L1TP) processing. We carried out additional processing on the pre- and post-fire images, including an illumination correction to account for effects of steep topography, and radiometric normalization to ensure homogeneity between images. We also identified and processed high-quality images for both the pre-fire condition(25 Oct 2013), and the post-fire burn scar (26 Dec 2018). Using the tools we provided in the paper, we estimate that, over the first hour, the Camp Fire was consuming ~200 ha/minute of vegetation with a linear spread rate of 14 km over the fire’s first 25 minutes, or ~33km/hr. We briefly discuss broader use of remote sensing and geospatial analysis for fire research and risk management. A visualization app (sliderApp) that includes pre-fire, active fire, and post-fire images: https://caralyngorman.users.earthengine.app/view/camp-fire-sliding-map-3-9-19
54 ENVIRONMENTAL SCIENCES↗
Imaging of a fluid injection process using geophysical data — A didactic example
In many subsurface industrial applications, fluids are injected into or withdrawn from a geologic formation. It is of practical interest to quantify precisely where, when, and by how much the injected fluid alters the state of the subsurface. Routine geophysical monitoring of such processes attempts to image the way that geophysical properties, such as seismic velocities or electrical conductivity, change through time and space and to then make qualitative inferences as to where the injected fluid has migrated. The more rigorous formulation of the time-lapse geophysical inverse problem forecasts how the subsurface evolves during the course of a fluid-injection application. Using time-lapse geophysical signals as the data to be matched, the model unknowns to be estimated are the multiphysics forward-modeling parameters controlling the fluid-injection process. Properly reproducing the geophysical signature of the flow process, subsequent simulations can predict the fluid migration and alteration in the subsurface. The dynamic nature of fluid-injection processes renders imaging problems more complex than conventional geophysical imaging for static targets. This work intents to clarify the related hydrogeophysical parameter estimation concepts.
58 GEOSCIENCES↗
NanoPSD: A software for automatic detection of Nano-Particle Shape Distribution in electron microscopy images
Accurate quantification of the size and morphology of nanoparticles from electron microscopy (EM) images is essential to understand growth mechanisms, surface reactivity, and functional behavior in nanoscale materials. Manual analysis remains slow, subjective, and difficult to reproduce in large datasets. We introduce NanoPSD (Nano-Particle Shape Distribution), an open-source and fully automated framework for quantitative particle detection and morphology analysis from EM images. NanoPSD integrates adaptive contrast enhancement, polarity-agnostic scale-bar detection, Optical Character Recognition (OCR)-based calibration, and classical segmentation via Otsu thresholding with morphological refinement. Particle contours are used to extract geometric descriptors, including equivalent circular diameter, aspect ratio, circularity, and solidity, enabling automated classification into spherical, rod-like, and aggregate morphologies. The framework supports both single-image and batch processing, generating publication-quality visualizations, LaTeX-ready tables, and structured comma-separated values (CSV) datasets. As a demonstration, we applied NanoPSD to plasma-synthesized nanoparticle samples diagnosed via transmission electron microscopy (TEM). The code produced statistically robust size and morphology distributions spanning a few to tens of nanometers with minimal user supervision. The pipeline demonstrates high reproducibility and scalability, processing large image collections with consistent calibration and output formatting. Its modular design enables seamless integration of future deep-learning-based segmentation models, providing a pathway toward intelligent, data-driven electron microscopy analysis.
36 MATERIALS SCIENCE↗
Microfluidic electrochemical device and process for chemical imaging and electrochemical analysis at the electrode-liquid interface in-situ
A microfluidic electrochemical device and process are detailed that provide chemical imaging and electrochemical analysis under vacuum at the surface of the electrode-sample or electrode-liquid interface in-situ. The electrochemical device allows investigation of various surface layers including diffuse layers at selected depths populated with, e.g., adsorbed molecules in which chemical transformation in electrolyte solutions occurs.
36 MATERIALS SCIENCE↗
Microfluidic electrochemical device and process for chemical imaging and electrochemical analysis at the electrode-liquid interface in-situ
A microfluidic electrochemical device and process are detailed that provide chemical imaging and electrochemical analysis under vacuum at the surface of the electrode-sample or electrode-liquid interface in-situ. The electrochemical device allows investigation of various surface layers including diffuse layers at selected depths populated with, e.g., adsorbed molecules in which chemical transformation in electrolyte solutions occurs.
42 ENGINEERING↗
THE CANADA-FRANCE-HAWAII TELESCOPE LEGACY SURVEY: STACKED IMAGES AND CATALOGS
This paper describes the image stacks and catalogs of the Canada-France-Hawaii Telescope Legacy Survey produced using the MegaPipe data pipeline at the Canadian Astronomy Data Centre. The Legacy Survey is divided into two parts. The Deep Survey consists of four fields each of 1 deg{sup 2}, with magnitude limits (50% completeness for point sources) of u = 27.5, g = 27.9, r = 27.7, i = 27.4, and z = 26.2. It contains 1.6 Multiplication-Sign 10{sup 6} sources. The Wide Survey consists of 150 deg{sup 2} split over four fields, with magnitude limits of u = 26.0, g = 26.5, r = 25.9, i = 25.7, and z = 24.6. It contains 3 Multiplication-Sign 10{sup 7} sources. This paper describes the calibration, image stacking, and catalog generation process. The images and catalogs are available on the web through several interfaces: normal image and text file catalog downloads, a 'Google Sky' interface, an image cutout service, and a catalog database query service.
79 ASTRONOMY AND ASTROPHYSICS↗
Cleaning Images with Gaussian Process Regression
Many approaches to astronomical data reduction and analysis cannot tolerate missing data: corrupted pixels must first have their values imputed. This paper presents astrofix, a robust and flexible image imputation algorithm based on Gaussian process regression. Through an optimization process, astrofix chooses and applies a different interpolation kernel to each image, using a training set extracted automatically from that image. It naturally handles clusters of bad pixels and image edges and adapts to various instruments and image types. For bright pixels, the mean absolute error of astrofix is several times smaller than that of median replacement and interpolation by a Gaussian kernel. We demonstrate good performance on both imaging and spectroscopic data, including the SBIG 6303 0.4 m telescope and the FLOYDS spectrograph of Las Cumbres Observatory and the CHARIS integral-field spectrograph on the Subaru Telescope.
42 ENGINEERING↗
Unsupervised learning-enabled pulsed infrared thermographic microscopy of subsurface defects in stainless steel
Metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing method (AM) frequently contain microscopic porosity defects, with typical approximate size distribution from one to 100 microns. Presence of such defects could lead to premature failure of the structure. In principle, structural integrity assessment of LPBF metals can be accomplished with nondestructive evaluation (NDE). Pulsed infrared thermography (PIT) is a non-contact, one-sided NDE method that allows for imaging of internal defects in arbitrary size and shape metallic structures using heat transfer. PIT imaging is performed using compact instrumentation consisting of a flash lamp for deposition of a heat pulse, and a fast frame infrared (IR) camera for measuring surface temperature transients. However, limitations of imaging resolution with PIT include blurring due to heat diffusion, sensitivity limit of the IR camera. We demonstrate enhancement of PIT imaging capability with unsupervised learning (UL), which enables PIT microscopy of subsurface defects in high strength corrosion resistant stainless steel 316 alloy. PIT images were processed with UL spatial–temporal separation-based clustering segmentation (STSCS) algorithm, refined by morphology image processing methods to enhance visibility of defects. The STSCS algorithm starts with wavelet decomposition to spatially de-noise thermograms, followed by UL principal component analysis (PCA), fine-tuning optimization, and neural learning-based independent component analysis (ICA) algorithms to temporally compress de-noised thermograms. The compressed thermograms were further processed with UL-based graph thresholding K-means clustering algorithm for defects segmentation. The STSCS algorithm also includes online learning feature for efficient re-training of the model with new data. For this study, metallic specimens with calibrated microscopic flat bottom hole defects, with diameters in the range from 203 to 76 µm, were produced using electro discharge machining (EDM) drilling. While the raw thermograms do not show any material defects, using STSCS algorithm to process PIT images reveals defects as small as 101 µm in diameter. To the best of our knowledge, this is the smallest reported size of a sub-surface defect in a metal imaged with PIT, which demonstrates the PIT capability of detecting defects in the size range relevant to quality control requirements of LPBF-printed high-strength metals.
36 MATERIALS SCIENCE↗
First indirect x-ray imaging tests with an 88-mm diameter single crystal
Using the 1-BM-C beamline at the Advanced Photon Source (APS), we have performed the initial indirect x - ray imaging point-spread-function (PSF) test of a unique 88-mm diameter YAG:Ce single crystal of only 100 - micron thickness. The crystal was bonded to a fiber optic plat e (FOP) for mechanical support and to allow the option for FO coupling to a large format camera. This configuration resolution was compared to that of self - supported 25-mm diameter crystals, with and without an Al reflective coating. An upstream monochromator was used to select 17-keV x-rays from the broadband APS bending magnet source of synchrotron radiation. The upstream , adjustable Mo collimators were then used to provide a series of x-ray source transverse sizes from 200 microns down to about 15-20 microns (FWHM) at the crystal surface. The emitted scintillator radiation was in this case lens coupled to the ANDOR Neo sCMOS camera, and the indirect x-ray images were processed offline by a MATLAB - based image processing program. Based on single Gaussian peak fits to the x-ray image projected profiles, we observed a 10.5 micron PSF. This sample thus exhibited superior spatial resolution to standard P43 polycrystalline phosphors of the same thickness which would have about a 100-micron PSF. Lastly, this single crystal resolution combined with the 88-mm diameter makes it a candidate to support future x-ray diffraction or wafer topography experiments.
43 PARTICLE ACCELERATORS↗
MACHINE-LEARNING IMAGE RECONSTRUCTION SPEEDS UP NANOMETROLOGY DATA PROCESSING BY 500X WITHOUT SACRIFICING IMAGE QUALITY
Generic poster for MSRF ERB and ML workshop hosted by LANL in Santa Fe
Dingreville, Remi Philippe Michel↗
Image segmentation for uranium isotopic analysis by SIMS: Combined adaptive thresholding and marker controlled watershed approach
A novel approach to particle identification and particle isotope ratio determination has been developed for nuclear safeguard applications. This particle search approach combines an adaptive thresholding algorithm and marker-controlled watershed segmentation (MCWS) transform, which improves the secondary ion mass spectrometry (SIMS) isotopic analysis of uranium containing particle populations for nuclear safeguards applications. The Niblack assisted MCWS approach (a.k.a. SEEKER) developed for this work has improved the identification of isotopically unique uranium particles under conditions that have historically presented significant challenges for SIMS image data processing techniques. Particles obtained from five NIST uranium certified reference materials (CRM U129A, U015, U150, U500 and U850) were successfully identified in regions of SIMS image data 1) where a high variability in image intensity existed, 2) where particles were touching or were in close proximity to one another and/or 3) where the magnitude of ion signal for a given region was count limited. Analysis of the isotopic distributions of uranium containing particles identified by SEEKER showed four distinct, accurately identified 235U enrichment distributions, corresponding to the NIST certified 235U/238U isotope ratios for CRM U129A/U015 (not statistically differentiated), U150, U500 and U850. Additionally, comparison of the minor uranium isotope (234U, 235U and 236U) atom percent values verified that, even in the absence of high precision isotope ratio measurements, SEEKER could be used to segment isotopically unique uranium particles from SIMS image data. Although demonstrated specifically for SIMS analysis of uranium containing particles for nuclear safeguards, SEEKER has application in addressing a broad set of image processing challenges.
adaptive thresholding↗
Coarse-to-fine Task-driven Inpainting for Geoscience Images
The processing and recognition of geoscience images have wide applications. Most of existing researches focus on understanding the high-quality geoscience images by assuming that all the images are clear. However, in many real-world cases, the geoscience images might contain occlusions during the image acquisition. This problem actually implies the image inpainting problem in computer vision and multimedia. As far as we know, all the existing image inpainting algorithms learn to repair the occluded regions for a better visualization quality, they are excellent for natural images but not good enough for geoscience images, and they never consider the following geoscience task when developing inpainting methods. Here, this paper aims to repair the occluded regions for a better geoscience task performance and advanced visualization quality simultaneously, without changing the current deployed deep learning based geoscience models. Because of the complex context of geoscience images, we propose a coarse-to-fine encoder-decoder network with the help of designed coarse-to-fine adversarial context discriminators to reconstruct the occluded image regions. Due to the limited data of geoscience images, we propose a MaskMix based data augmentation method, which augments inpainting masks instead of augmenting original images, to exploit the limited geoscience image data. The experimental results on three public geoscience datasets for remote sensing scene recognition, cross-view geolocation and semantic segmentation tasks respectively show the effectiveness and accuracy of the proposed method. The code is available at: https://github.com/HMS97/Task-driven-Inpainting.
97 MATHEMATICS AND COMPUTING↗