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All sources NASA NTRS DOE OSTI

DOE OSTI2026

Locating the atoms at the hard-soft interface of gold nanoparticles

Surface structure affects the growth, shape and properties of nanoparticles. In wet chemical syntheses, metal additives and surfactants are used to modify surfaces and guide nanocrystal growth. To understand this process, it is critical to understand how the surface structure, and hence its energy, is modified. However, measuring the type and arrangement of atoms at hard-soft interfaces on nanoscale surfaces, especially in the presence of surfactants, is extremely challenging. Here, we determine the atomic structure of the hard-soft interface in a metallic nanoparticle by developing low-dose imaging conditions in four-dimensional scanning transmission electron microscopy that are preferentially sensitive to surface adatoms. By revealing experimentally the copper additives and bromide surfactant counterion at the surface of a gold nanocuboid and quantifying their interatomic distances, our direct, low-dose imaging method provides atomic-level understanding of chemically sophisticated nanomaterial surface structures. These measurements of the atomic structure of the hard-soft interface provide the information necessary to understand and quantify surface chemistries and energies and their pivotal role in nanocrystal growth.

Li, Weilun [Monash University, Melbourne, VIC (Aus↗

DOE OSTI2025

Advancing X-ray quantum imaging through Monte-Carlo simulations

Imaging with X-rays poses fundamental limits due to radiation damage of the highly energetic photons. This becomes problematic for sensitive biological systems such as subcellular structures. Lowering the radiation dose, without sacrificing the signal-to-noise ratio, would be desirable for any kind of imaging modalities involving X-rays. To achieve this goal, quantum imaging with entangled X-ray photons constitutes a promising route. Production of biphotons have been demonstrated in the X-ray regime by the process of Spontaneous Parametric Down-Conversion (SPDC). However, compared to SPDC in the regime of visible light, the production rate for X-ray biphotons is extremely low. With the introduction of new high average brightness X-ray sources, such as 4th generation synchrotrons and high repetition rate Free-Electron X-ray Lasers (XFEL), quantum imaging may become practical. We introduce a ray tracing approach using Monte-Carlo sampling, specifically designed for quantum imaging with entangled X-ray photons generated by SPDC. By simulation, the superior image quality of quantum over classical imaging methods is demonstrated using realistic experimental conditions available at high repetition rate XFELs. With these simulations, we can efficiently assist the design of future experiments at beam lines, which can substantially accelerate the advancement of X-ray quantum imaging and reduce costs.

Entangled Photons↗

DOE OSTI2025

Enhancing synchrotron radiation micro-CT images using deep learning: an application of Noise2Inverse on bone imaging

In bone-imaging research, in situ synchrotron radiation micro-computed tomography (SRµCT) mechanical tests are used to investigate the mechanical properties of bone in relation to its microstructure. Low-dose computed tomography (CT) is used to preserve bone's mechanical properties from radiation damage, though it increases noise. To reduce this noise, the self-supervised deep learning method Noise2Inverse was used on low-dose SRµCT images where segmentation using traditional thresholding techniques was not possible. Simulated-dose datasets were created by sampling projection data at full, one-half, one-third, one-fourth and one-sixth frequencies of an in situ SRµCT mechanical test. After convolutional neural networks were trained, Noise2Inverse performance on all dose simulations was assessed visually and by analyzing bone microstructural features. Visually, high image quality was recovered for each simulated dose. Lacunae volume, lacunae aspect ratio and mineralization distributions shifted slightly in full, one-half and one-third dose network results, but were distorted in one-fourth and one-sixth dose network results. Following this, new models were trained using a larger dataset to determine differences between full dose and one-third dose simulations. Significant changes were found for all parameters of bone microstructure, indicating that a separate validation scan may be necessary to apply this technique for microstructure quantification. Noise present during data acquisition from the testing setup was determined to be the primary source of concern for Noise2Inverse viability. While these limitations exist, incorporating dose calculations and optimal imaging parameters enables self-supervised deep learning methods such as Noise2Inverse to be integrated into existing experiments to decrease radiation dose.

Obata, Yoshihiro (ORCID:0000000303659129)↗

DOE OSTI2026

Quantum correlation imaging via X-ray parametric down-conversion

Quantum imaging leverages correlations between pairs of photons and has the potential to obtain image information beyond what classical sources provide. Extending this approach to the X-ray regime has been limited by low photon-pair generation rates and the lack of suitable detectors. Here, we demonstrate X-ray coincidence imaging using spontaneous parametric down-conversion (SPDC) and a pixelated area detector with time- and energy-resolved capabilities. This configuration enables simultaneous detection of correlated X-ray photon pairs and coincidence-based imaging of test objects, including a biological specimen. The increased coincidence rate and spatially resolved detection establish a basis for future quantum-enhanced and low-dose X-ray imaging.

Goodrich, Justin C. [Brookhaven National Laborator↗

DOE OSTI2025

Advancing the Frontiers of Deep Learning for Low-Dose 3D Cone-Beam CT Reconstruction

X-ray computed tomography (CT) is an important noninvasive medical imaging modality for studying the structural details of internal organs. Image reconstruction in CT is an inverse problem of recovering an object's internal structure from the absorption profile of X-ray beams (sinogram) measured using a detector. The classical variational approach for CT reconstruction minimizes an energy functional using an appropriate iterative algorithm. Motivated by the success of deep learning (DL), researchers have begun to leverage training data and enhanced computing capabilities in recent years to produce high-fidelity reconstructed images. Nonetheless, much of the academic research in DL algorithms for CT has focused primarily on the two-dimensional setting (with simplified forward operators and noise model) for proofs-of-concept, and a comprehensive benchmarking of various classical and data-driven CT reconstruction approaches has not beenundertaken. The key objective of our CT reconstruction grand challenge was to promote methodological advancements for both classical and DL-based approaches for clinical CT with a reasonably accurately simulated 3D CT forward operator and noise model. We have utilized the publicly available LIDC-IDRI dataset and simulated sinograms and FDK images corresponding to two dose levels (clinical- and low-dose, constituting two tracks of the challenge) starting from the normal-dose images as the ground truth. In this paper, we summarize the motivation, context, and results of our challenge, and highlight the future research directions in DL for clinical CT.

X-ray tomography↗

NASA NTRS2014

Monitoring for Renal Stone Recurrence in Astronauts With History of Stone

After an initial stone episode persons are at increased risk for future stone formation. A systematic approach is required to monitor the efficacy of treatment and preventive measures, and to assess the risk of developing new stones. This is important for persons working in critical jobs or austere environments, such as astronauts. A literature review of the current standards of care for renal stone monitoring and imaging was done. Military and civil aviation standards were also reviewed, as well as the medical precedents from the space program. Additionally, a new, more effective, renal stone ultrasound protocol has been developed. Using this work, a monitoring algorithm was proposed that takes into consideration the unique mission and operational environment of spaceflight. The approach to imaging persons with history of renal stones varies widely in the literature. Imaging is often done yearly or biannually, which may be too long for mission critical personnel. In the proposed algorithm astronauts with a history of renal stone, who may be under consideration for assignment, are imaged by a detailed, physiciandriven, ultrasound protocol. Unassigned personnel are monitored by yearly ultrasound and urine studies. Any positive ultrasound study is then followed by low-dose renal computed tomography scan. Other criteria are also established. The proposed algorithm provides a balanced approach between efficacy and reduced radiation exposure for the monitoring of astronauts with a renal stone history. This may eventually allow a transition from a risk-averse, to a risk-modifying approach that can enable continued service of individuals with history of renal stone that have adequately controlled risk factors.

Reyes, David P.↗

DOE OSTI2025

Dataset for Leveraging CryoEM and AI-Driven Morphological Feature Analysis for Insights on Bacterial Structures

This repository hosts an AI-assisted image segmentation and analysis pipeline for Pantoea sp. YR343 cryo-electron microscopy (cryoEM) datasets. The workflow automates membrane thickness measurements, flagella detection, and field-of-view (FOV) screening from low-dose, high-resolution cryoEM micrographs eliminating the need for slow manual annotation. By integrating deep-learning based segmentation (YOLOv11) with quantitative post-processing, this toolkit provides a scalable and reproducible way to study bacterial morphology under hydrated, near-native conditions. The GitHub repository for AI-based tools for cryoEM bacteria ultrastructures can be found here: https://github.com/Sireesiru/Cryo-EM-Ultrastructures/tree/main

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Beyond Contrast Transfer: Spectral SNR as a Finite-Dose Metric for STEM Phase Retrieval

The contrast transfer function (CTF) is widely used to evaluate phase retrieval methods in scanning transmission electron microscopy (STEM), including center-of-mass imaging, parallax imaging, direct ptychography, and iterative ptychography. However, the CTF reflects only the maximum usable signal, neglecting the effects of finite electron fluence and the Poisson-limited nature of detection. As a result, it can significantly overestimate practical performance, especially in low-dose regimes. Here, we employ the spectral signal-to-noise ratio (SSNR), as a finite-dose statistical framework to evaluate the recoverable signal as a function of spatial frequency. Using numerical reconstructions of white-noise objects, we show that center-of-mass, parallax, and direct ptychography exhibit dose-independent SSNRs, with close-form analytic expressions. In contrast, iterative ptychography exhibits a surprising dose dependence: at low fluence, its SSNR converges to that of direct ptychography; at high fluence, it saturates at a value consistent with the maximum detective quantum efficiency predicted by recent quantum Fisher information bounds. The results highlight the limitations of CTF-based evaluation and motivate SSNR as a more accurate, finite-dose metric for assessing STEM phase retrieval methods.

STEM phase retrieval↗

NASA NTRS1985

Multiwire Gamma Camera for Radionuclide and Radiographic Imaging in the Space environment

Unique multiwire proportional counter technology has been developed at the Johnson Space Center over the past several years. The technology will be described and how it may apply both in near- and long-term NASA efforts. In the near-term, I feel that the technology will provide a significant tool for the cardiovascular research area. In particular, low-dose nuclear medicine and tissue densitometry techniques of expanded scope will be supplied. In the longer term, the multiwire technique can provide a general purpose radiology and nuclear medicine facility for use in the space station which would be difficult and costly to provide by other means.

Lacy, Jeffrey L.↗

NASA NTRS1997

Mutagenic effects of a single and an exact number of alpha particles in mammalian cells

One of the main uncertainties in risk estimation for environmental radon exposure using lung cancer data from underground miners is the extrapolation from high- to low-dose exposure where multiple traversal is extremely rare. The biological effects of a single alpha particle are currently unknown. Using the recently available microbeam source at the Radiological Research Accelerator Facility at Columbia University, we examined the frequencies and molecular spectrum of S1- mutants induced in human-hamster hybrid (A(L)) cells by either a single or an exact number of alpha particles. Exponentially growing cells were stained briefly with a nontoxic concentration of Hoechst dye for image analysis, and the location of individual cells was computer-monitored. The nucleus of each cell was irradiated with either 1,2,4, or 8 alpha particles at a linear energy transfer of 90 keV/microm consistent with the energy spectrum of domestic radon exposure. Although single-particle traversal was only slightly cytotoxic to A(L) cells (survival fraction approximately 0.82), it was highly mutagenic, and the induced mutant fraction averaged 110 mutants per 10(5) survivors. In addition, both toxicity and mutant induction were dose-dependent. Multiplex PCR analysis of mutant DNA showed that the proportion of mutants with multilocus deletions increased with the number of particle traversals. These data provide direct evidence that a single a particle traversing a nucleus will have a high probability of resulting in a mutation and highlight the need for radiation protection at low doses.

NASA Discipline Radiation Health↗

DOE OSTI2026

Fingerprinting Uranium Oxides with Electron Energy Loss Spectroscopy Supported by Theoretical Computations

Uranium oxides occur in a variety of phases that differ in crystal structure and uranium oxidation states. Electron energy loss spectroscopy (EELS) is one of the few techniques that has sufficient spatial resolution and sensitivity to electronic structure to distinguish amongst phases at the nanoscale. However, beam-sensitive materials such as uranium oxides are subject to spectral modification due to interactions with the electron beam. Therefore, theory support is essential to reliably exclude the impact of beam damage and generate true reference datasets. Here we use a comparison of theoretical and experimental spectra to probe the impact of beam damage on O K-edge and U N-edge (N6,7 and N4,5) EELS spectra of various single-valent and mixed-valence uranium oxide bulk phases. Using a low-dose experimental set-up, we show that the O K-edge theoretical spectra are in excellent agreement with experiment for both peak positions and relative intensities of respective peaks. In contrast, U N-edge features are less distinguishing due to the partially localized nature of the U 5f orbitals and overlapping multiplet and spin–orbit coupling effects. This work demonstrates that O K-edge EELS is sufficiently diagnostic to distinguish a wide range of uranium oxides and that the experimental approach used here minimizes beam damage and allows valence state discrimination across the U(IV), U(V) and U(VI) series. When combined with imaging modes available in electron mi-croscopy, the work enables detailed investigation and characterization of uranium redox transformations at the nanoscale.

Carbone, Jacopo↗

DOE OSTI2025

Psychosomatic Bias in Low-dose Radiation Epidemiology: Assessing the Role of Radiophobia and Stress in Cancer Incidence

Abstract Historical assessment of radiation effects at low doses (below 0.2 Sv) are generally the result of back extrapolation from higher doses, which are known to have a linear relation between risk and dose. There are multiple counter-examples, and some literature argues that a threshold, nonlinear, or even a beneficial effect (hormeisis) can occur from radiation below these doses. The common theme found in all of these studies stems from the traditional approach of correlating disease rates to stimulus and then effectively curve-fitting the result toward zero dose. What has not been considered in general are the personal stress levels of the exposed individuals due to fear of cancer from low doses. The increased levels of cortisol due to the psychological stress from fear or depression has been shown in the literature to increase cancer probability. The extent to which low-dose exposed individuals were highly fearful or stressed from the radiation exposure would then give rise to elevated cancer based on stress rather than a fundamental radiogenic mechanism. If the population under epidemiological study is aware of a potential historical exposure (no matter how small) and has then lived under stress from fear or depression due to that exposure, the psychosomatic effects will bias the epidemiology accordingly and so should be quantified and accounted for as done with the effects of smoking. Health Phys. 129(0):000-000; 2025

Environmental Sciences & Ecology↗

DOE OSTI2026

Automated Bacterial Identification and Morphological Feature Analysis in Low‐Dose Cryo‐EM Using YOLOv11

Bacteria rapidly adapt to environmental cues through morphological and ultrastructural changes that correlate with physiology and behavior. Cryogenic transmission electron microscopy (cryo‐TEM) can capture these phenotypic changes in near‐native, vitrified states, but manual analysis of low‐dose micrographs is labor intensive and limits throughput. Here, we present an end‐to‐end workflow that combines low‐dose cryo‐TEM imaging with a YOLOv11‐based instance‐segmentation model to automatically identify bacteria and quantify key structural features directly from the micrographs. This workflow enables (i) robust bacterial localization and counting from low‐magnification atlas/montage images, (ii) automated measurements of cell‐envelope (outer–inner membrane) thickness and anisotropy from higher‐magnification views, and (iii) detection and quantification of bacteria–flagella interactions, including overlap length and curvature metrics for interacting versus noninteracting flagella. Using Pantoea sp. YR343 grown under distinct media conditions, we show that the automated measurements agree with manual annotations while substantially reducing analysis time. Together, these tools provide a practical framework for scalable bacterial identification and quantitative phenotyping in low‐dose cryo‐TEM datasets and establish a foundation for extending cryo‐TEM image analysis toward higher‐throughput studies of microbial heterogeneity and biointerfaces.

YOLOv11↗

DOE OSTI2026

Learning neural representations for X-ray ptychography reconstruction with unknown probes

X-ray ptychography provides exceptional nanoscale resolution and is widely applied in materials science, biology, and nanotechnology. However, its full potential is constrained by the critical challenge of accurately reconstructing images when the illuminating probe is unknown. Conventional iterative methods and deep learning approaches are often suboptimal, particularly under the low-signal conditions inherent to low-dose and high-speed experiments. These limitations compromise reconstruction fidelity and restrict the broader adoption of the technique. In this work, we introduce the Ptychographic Implicit Neural Representation (PtyINR), a self-supervised framework that simultaneously addresses the object- and probe-recovery problem. By parameterizing both as continuous neural representations, PtyINR performs end-to-end reconstruction directly from raw diffraction patterns without requiring any pre-characterization of the probe. Extensive evaluations demonstrate that PtyINR achieves superior reconstruction quality on both simulated and experimental data, with remarkable robustness under challenging low-signal conditions. Furthermore, PtyINR offers a generalizable, physics-informed framework for addressing probe-dependent inverse problems, making it applicable to a wide range of computational microscopy problems.

36 MATERIALS SCIENCE↗

NASA NTRS1997

Latent transforming growth factor beta1 activation in situ: quantitative and functional evidence after low-dose gamma-irradiation

The biological activity of transforming growth factor beta1 (TGF-beta) is controlled by its secretion as a latent complex in which it is noncovalently associated with latency-associated peptide (LAP). Activation is the extracellular process in which TGF-beta is released from LAP, and is considered to be a primary regulatory control. We recently reported rapid and persistent changes in TGF-beta immunoreactivity in conjunction with extracellular matrix remodeling in gamma-irradiated mouse mammary gland. Our hypothesis is that these specific changes in immunoreactivity are indicative of latent TGF-beta activation. In the present study, we determined the radiation dose response and tested whether a functional relationship exists between radiation-induced TGF-beta and collagen type III remodeling. After radiation exposures as low as 0.1 Gy, we detected increased TGF-beta immunoreactivity in the mammary epithelium concomitant with decreased LAP immunostaining, which are events consistent with activation. Quantitative image analysis demonstrated a significant (P=0.0005) response at 0.1 Gy without an apparent threshold and a linear dose response to 5 Gy. However, in the adipose stroma, loss of LAP demonstrated a qualitative threshold at 0.5 Gy. Loss of LAP paralleled induction of collagen III immunoreactivity in this tissue compartment. We tested whether TGF-beta mediates collagen III expression by treating animals with TGF-beta panspecific monoclonal antibody, 1D11.16, administered i.p. shortly before irradiation. Radiation-induced collagen III staining in the adipose stroma was blocked in an antibody dose-dependent manner, which persisted through 7 days postirradiation. RNase protection assay revealed that radiation-induced elevation of total gland collagen III mRNA was also blocked by neutralizing antibody treatment. These data provide functional confirmation of the hypothesis that radiation exposure leads to latent TGF-beta activation, support our interpretation of the reciprocal shift in immunoreactivity as evidence of activation, and implicate TGF-beta as a mediator of tissue response to ionizing radiation. The sensitivity of activation to low radiation doses points to a potential role for TGF-beta in orchestrating tissue response to oxidative stress. As such, radiation may be useful as a probe to delineate the consequences of latent TGF-beta activation in situ.

NASA Discipline Radiation Health↗