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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

CLAIRE—Parallelized Diffeomorphic Image Registration for Large-Scale Biomedical Imaging Applications

We study the performance of CLAIRE—a diffeomorphic multi-node, multi-GPU image-registration algorithm and software—in large-scale biomedical imaging applications with billions of voxels. At such resolutions, most existing software packages for diffeomorphic image registration are prohibitively expensive. As a result, practitioners first significantly downsample the original images and then register them using existing tools. Our main contribution is an extensive analysis of the impact of downsampling on registration performance. We study this impact by comparing full-resolution registrations obtained with CLAIRE to lower resolution registrations for synthetic and real-world imaging datasets. Our results suggest that registration at full resolution can yield a superior registration quality—but not always. For example, downsampling a synthetic image from 10243 to 2563 decreases the Dice coefficient from 92% to 79%. However, the differences are less pronounced for noisy or low contrast high resolution images. CLAIRE allows us not only to register images of clinically relevant size in a few seconds but also to register images at unprecedented resolution in reasonable time. The highest resolution considered are CLARITY images of size 2816×3016×1162. To the best of our knowledge, this is the first study on image registration quality at such resolutions.

Himthani, Naveen↗
DOE OSTI2026

Imaging from Macro to Nanoscale: Multimodal Advances in Chemical and Biomedical Imaging

Imaging increasingly serves as a multiscale framework for linking molecular mechanisms to cellular behavior, tissue architecture, and organ phenotypes in biology and unraveling fundamental processes in chemistry, physics and materials science. This Perspective highlights recent advances in chemical and biomedical imaging across macro-, micro-, and nanoscales, using representative examples published in Chemical and Biomedical Imaging (CBMI). At the macroscale, we discuss chemically selective MRI, including endogenous and exogenous CEST strategies, together with photoacoustic imaging as a hybrid modality with functional and chemical contrast. At the microscale, we consider fluorescence, label-free optical and vibrational imaging, and selected X-ray approaches that expand sensitivity, specificity, and temporal resolution in biological and materials systems. At the nanoscale, we highlight super-resolution fluorescence microscopy, single-molecule methods, tip-enhanced Raman spectroscopy, and correlative imaging strategies that resolve local heterogeneity and molecular organization. Across scales, a common theme emerges that advances in probes, contrast mechanisms, instrumentation, and sample handling are enabling chemically informed imaging that connects molecular specificity with biological context.

multiscale imaging↗
DOE OSTI2024

High-Density Polyethylene Custom Focusing Lenses for High-Resolution Transient Terahertz Biomedical Imaging Sensors

Transient terahertz time-domain spectroscopy (THz-TDS) imaging has emerged as a novel non-ionizing and noninvasive biomedical imaging modality, designed for the detection and characterization of a variety of tissue malignancies due to their high signal-to-noise ratio and submillimeter resolution. We report our design of a pair of aspheric focusing lenses using a commercially available lens-design software that resulted in about 200 × 200-μm2 focal spot size corresponding to the 1-THz frequency. The lenses are made of high-density polyethylene (HDPE) obtained using a lathe fabrication and are integrated into a THz-TDS system that includes low-temperature GaAs photoconductive antennae as both a THz emitter and detector. The system is used to generate high-resolution, two-dimensional (2D) images of formalin-fixed, paraffin-embedded murine pancreas tissue blocks. The performance of these focusing lenses is compared to the older system based on a pair of short-focal-length, hemispherical polytetrafluoroethylene (TeflonTM) lenses and is characterized using THz-domain measurements, resulting in 2D maps of the tissue refractive index and absorption coefficient as imaging markers. For a quantitative evaluation of the lens effect on the image resolution, we formulated a lateral resolution parameter, R2080, defined as the distance required for a 20–80% transition of the imaging marker from the bare paraffin region to the tissue region in the same image frame. The R2080 parameter clearly demonstrates the advantage of the HDPE lenses over TeflonTM lenses. The lens-design approach presented here can be successfully implemented in other THz-TDS setups with known THz emitter and detector specifications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Acoustics at the nanoscale (nanoacoustics): A comprehensive literature review. Part II: Nanoacoustics for biomedical imaging and therapy

We report in the past decade, acoustics at the nanoscale (i.e., nanoacoustics) has evolved rapidly with continuous and substantial expansion of capabilities and refinement of techniques. Motivated by research innovations in the last decade, for the first time, recent advancements of acoustics-associated nanomaterials/nanostructures and nanodevices for different applications are outlined in this comprehensive review, which is written in two parts. As part II of this two-part review, this paper concentrates on nanoacoustics in biomedical imaging and therapy applications, including molecular ultrasound imaging, photoacoustic imaging, ultrasound-mediated drug delivery and therapy, and photoacoustic drug delivery and therapy. Firstly, the recent developments of nanosized ultrasound and photoacoustic contrast agents as well as their various imaging applications are examined. Secondly, different types of nanomaterials/nanostructures as nanocarriers for ultrasound and photoacoustic therapies are discussed. Finally, a discussion of challenges and future research directions are provided for nanoacoustics in medical imaging and therapy.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

High-Resolution Scanning Coded-Mask-Based X-ray Multi-Contrast Imaging and Tomography

Near-field X-ray speckle tracking has been used in phase-contrast imaging and tomography as an emerging technique, providing higher contrast images than traditional absorption radiography. Most reported methods use sandpaper or membrane filters as speckle generators and digital image cross-correlation for phase reconstruction, which has either limited resolution or requires a large number of position scanning steps. Recently, we have proposed a novel coded-mask-based multi-contrast imaging (CMMI) technique for single-shot measurement with superior performance in efficiency and resolution compared with other single-shot methods. We present here a scanning CMMI method for the ultimate imaging resolution and phase sensitivity by using a coded mask as a high-contrast speckle generator, the flexible scanning mode, the adaption of advanced maximum-likelihood optimization to scanning data, and the multi-resolution analysis. Scanning CMMI can outperform other speckle-based imaging methods, such as X-ray speckle vector tracking, providing higher quality absorption, phase, and dark-field images with fewer scanning steps. Scanning CMMI is also successfully demonstrated in multi-contrast tomography, showing great potentials in high-resolution full-field imaging applications, such as in vivo biomedical imaging.

Qiao, Zhi (ORCID:0000000233928732)↗
DOE OSTI2021

Passive Inter-Photon Imaging

Digital camera pixels measure image intensities by converting incident light energy into an analog electrical current, and then digitizing it into a fixed-width binary representation. This direct measurement method, while conceptually simple, suffers from limited dynamic range and poor performance under extreme illumination --- electronic noise dominates under low illumination, and pixel full-well capacity results in saturation under bright illumination. We propose a novel intensity cue based on measuring inter-photon timing, defined as the time delay between detection of successive photons. Based on the statistics of inter-photon times measured by a time-resolved single-photon sensor, we develop theory and algorithms for a scene brightness estimator which works over extreme dynamic range; we experimentally demonstrate imaging scenes with a dynamic range of over ten million to one. The proposed techniques, aided by the emergence of single-photon sensors such as single-photon avalanche diodes (SPADs) with picosecond timing resolution, will have implications for a wide range of imaging applications: robotics, consumer photography, astronomy, microscopy and biomedical imaging.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Deep learning classification of lipid droplets in quantitative phase images

We report the application of supervised machine learning to the automated classification of lipid droplets in label-free, quantitative-phase images. By comparing various machine learning methods commonly used in biomedical imaging and remote sensing, we found convolutional neural networks to outperform others, both quantitatively and qualitatively. We describe our imaging approach, all implemented machine learning methods, and their performance with respect to computational efficiency, required training resources, and relative method performance measured across multiple metrics. Overall, our results indicate that quantitative-phase imaging coupled to machine learning enables accurate lipid droplet classification in single living cells. As such, the present paradigm presents an excellent alternative of the more common fluorescent and Raman imaging modalities by enabling label-free, ultra-low phototoxicity, and deeper insight into the thermodynamics of metabolism of single cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Paw-Net: Stacking ensemble deep learning for segmenting scanning electron microscopy images of fine-grained shale samples

Segmentation of scanning electron microscopy (SEM) images is critical yet time-consuming for geological analyses, as it needs to differentiate the boundaries for different mineral objects to facilitate subsequent analyses, such as porosity calculation. Recently, various machine learning methods, especially convolutional neural networks (CNNs), have been explored to segment SEM images of fine-grained shale samples. However, we found that general CNNs do not yield optimal performance due to insufficient training data and imbalanced objects in SEM images. This work has revised the U-Net architecture, a popular approach for biomedical image analyses, by incorporating a loss function that addresses the imbalance issue. Furthermore, we used the ensemble learning method to train multiple models and combined the results to improve the overall performance of segmentation. We prepared 2162 sub-images from raw SEM images in our experiments and divided them into training, validation, and testing datasets. The overall results show that our method improves the average Intersection over Union (IOU) of mineral objects from 0.49 to 0.58, compared to the original U-Net model. Our method can clearly distinguish each object from others with boundaries, even in highly imbalanced images. Training our models takes less than three minutes using a single GPU, while manual labeling can take up to three hours for each image. Furthermore, the method helps geoscientists gain insights quickly and effectively by building neural network models from a small dataset of SEM images.

58 GEOSCIENCES↗
DOE OSTI2021

Artificial intelligence velocimetry and microaneurysm-on-a-chip for three-dimensional analysis of blood flow in physiology and disease

Significance Microfluidics is an important in vitro platform to gain insights into mechanics of blood flow and mechanisms of pathophysiology of human diseases. Extraction of 3D fields in microfluidics with dense cell suspensions remains a formidable challenge. We present artificial-intelligence velocimetry (AIV) as a general platform to determine 3D flow fields and a microaneurysm-on-a-chip to simulate blood flow in microaneurysms in patients with diabetic retinopathy. AIV is built on physics-informed neural networks that integrate seamlessly 2D images from microfluidic experiments or in vivo observations with physical laws to estimate full 3D velocity and stress fields. AIV could be integrated into imaging technologies to automatically infer key hemodynamic metrics from in vivo and in vitro biomedical images.

42 ENGINEERING↗
DOE OSTI2022

Physics-assisted generative adversarial network for X-ray tomography

X-ray tomography is capable of imaging the interior of objects in three dimensions non-invasively, with applications in biomedical imaging, materials science, electronic inspection, and other fields. The reconstruction process can be an ill-conditioned inverse problem, requiring regularization to obtain satisfactory results. Recently, deep learning has been adopted for tomographic reconstruction. Unlike iterative algorithms which require a distribution that is known a priori , deep reconstruction networks can learn a prior distribution through sampling the training distributions. In this work, we develop a Physics-assisted Generative Adversarial Network (PGAN), a two-step algorithm for tomographic reconstruction. In contrast to previous efforts, our PGAN utilizes maximum-likelihood estimates derived from the measurements to regularize the reconstruction with both known physics and the learned prior. Compared with methods with less physics assisting in training, PGAN can reduce the photon requirement with limited projection angles to achieve a given error rate. The advantages of using a physics-assisted learned prior in X-ray tomography may further enable low-photon nanoscale imaging.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

A Field-Deployable Magnetic Resonance Imaging Rhizotron for Modeling and Enhancing Root Growth and Biogeochemical Function

A collaborative team from Texas A&M AgriLife Research, ABQMR Inc., the Soil Health Institute, the Athinoula A. Martinos Center for Biomedical Imaging, and NIST developed low-field magnetic resonance imaging (LF-MRI) instrumentation capable of imaging intact soil-root systems. The system measured root biomass, architecture, 3D mass distribution, and growth rates, providing a non-destructive means to evaluate ideal plant characteristics based on root metrics. It also successfully generated three-dimensional images of soil water content, a key property influencing root growth and exploration. Operating much like an MRI used in a medical setting, the system functioned in field conditions without damaging plants, overcoming the limitations of traditional methods such as trenching, soil coring, and root excavation. Over the course of the project, the team designed and built three functional prototype systems. These prototypes provided new insights into root–water–soil interactions that drive processes such as nutrient uptake, water use, and carbon management. This information contributed to efforts to optimize plants for carbon sequestration without sacrificing economic yield. The project also supported the identification of desirable traits for energy sorghum, including high root growth rates, more vertical root angles, and enhanced drought resilience under water-limiting conditions.

09 BIOMASS FUELS↗
DOE OSTI2025

Localization and coherent imaging of hidden moving objects using laser speckle

Imaging and sensing of moving objects through opaque scattering media is a challenging but important problem in a variety of applications, including environmental sensing, biomedical imaging, and material inspection. We have previously demonstrated a technique to coherently image a moving object through thick, heavily scattering random media using correlations of speckle images as a function of the object’s spatial translation. Here, we demonstrate that this technique can be combined with localization to achieve imaging without prior knowledge of the object’s motion, greatly extending the application domain. This method is effective beyond the thin or weakly scattering regime and, rather than motion being deleterious, exploits the information available when the hidden object is moving, as could be the case in a cluttered terrestrial environment or through substantial levels of biological tissue scatter.

Hastings, Ryan L. (ORCID:0009000095977807)↗
DOE OSTI2025

Ultra‐Flexible Pixelated Perovskite Photodetectors Enabled by Honeycomb Polymer Grids for High‐Resolution Imaging

Abstract A nature‐inspired fabrication method based on a photolithography‐free flexible polymer grid is reported for high‐resolution pixelation of perovskite photodiode arrays with exceptional mechanical ductility and a morphology resembling that of natural compound eyes. The resulting pixelated perovskite photosensitive layer has a ≈1 µm pixel size with 2000 Pixels per inch (PPI) resolution when fully assembled as a photodetector array, delivering a detectivity of >10 13 Jones while providing cross‐talk free imaging. Using a polymer grid effectively releases stress on the perovskite platform, greatly increasing the mechanical agility of the otherwise brittle perovskite film. This novel fabrication methodology and device design offer new possibilities for applications in robotics, biomedical imaging, and virtual and augmented reality.

Zheng, Ding [Department of Chemistry and the Mater↗
DOE OSTI2025

Near-zero photon bioimaging by fusing deep learning and ultralow-light microscopy

Enhancing the reliability and reproducibility of optical microscopy by reducing specimen irradiance continues to be an important biotechnology target. As irradiance levels are reduced, however, the particle nature of light is heightened, giving rise to Poisson noise, or photon sparsity that restricts only a few (0.5%) image pixels to comprise a photon. Photon sparsity can be addressed by collecting approximately 200 photons per pixel; this, however, requires long acquisitions and, as such, suboptimal imaging rates. Here, we introduce near-zero photon bioimaging, a method that operates at kHz rates and 10,000-fold lower irradiance than standard microscopy. To achieve this level of performance, we uniquely combined a judiciously designed epifluorescence microscope enabling ultralow background levels and AI that learns to reconstruct biological images from as low as 0.01 photons per pixel. We demonstrate that near-zero photon bioimaging captures the structure of multicellular and subcellular features with high fidelity, including features represented by nearly zero photons. Beyond optical microscopy, the near-zero photon bioimaging paradigm can be applied in remote sensing, covert applications, and biomedical imaging that utilize damaging or quantum light.

AI↗
DOE OSTI2023

Bioimaging Science Program Principal Investigator Meetings Proceedings (2021)

As part of the 2021 Biological Systems Science Division (BSSD) Principal Investigator (PI) Meeting, the Bioimaging Science program (BSP), within BSSD's Biomolecular Characterization and Imaging Science portfolio, held its annual PI meeting virtually February 22–23. BSP's mission is to understand the translation of genomic information into the mechanisms that power living cells, communities of cells, and whole organisms. The goal of BSP is to develop new imaging and measurement technologies to visualize the spatial and temporal relationships of key metabolic processes governing phenotypic expression in plants and microbes. BSP convenes annual PI meetings to bring together its contributing investigators to review progress and current state-of-the-art bioimaging research. Holding the BSP meeting as part of the broader BSSD PI meeting allowed researchers to interact with the extended Genomic Science program community. This convergence provided a platform for networking and exchange of ideas, helping to forge new multidisciplinary collaborations among investigators from the two sister programs. An important highlight of the BSP meeting was the keynote presentation “Enhancing Fluorescence Microscopy with Computation” by Dr. Hari Shroff of the NIH National Institute of Biomedical Imaging and Bioengineering. All the BSP PIs made presentations describing their research focus and progress, and these were followed by roundtable discussions of each project. The meeting’s proceedings provide an outline of the program’s current state and potential future directions and opportunities.

59 BASIC BIOLOGICAL SCIENCES↗