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All sources NASA NTRS DOE OSTI

DOE OSTI2014

SU-F-BRF-09: A Non-Rigid Point Matching Method for Accurate Bladder Dose Summation in Cervical Cancer HDR Brachytherapy

Purpose: To propose and validate a deformable point matching scheme for surface deformation to facilitate accurate bladder dose summation for fractionated HDR cervical cancer treatment. Method: A deformable point matching scheme based on the thin plate spline robust point matching (TPSRPM) algorithm is proposed for bladder surface registration. The surface of bladders segmented from fractional CT images is extracted and discretized with triangular surface mesh. Deformation between the two bladder surfaces are obtained by matching the two meshes' vertices via the TPS-RPM algorithm, and the deformation vector fields (DVFs) characteristic of this deformation is estimated by B-spline approximation. Numerically, the algorithm is quantitatively compared with the Demons algorithm using five clinical cervical cancer cases by several metrics: vertex-to-vertex distance (VVD), Hausdorff distance (HD), percent error (PE), and conformity index (CI). Experimentally, the algorithm is validated on a balloon phantom with 12 surface fiducial markers. The balloon is inflated with different amount of water, and the displacement of fiducial markers is benchmarked as ground truth to study TPS-RPM calculated DVFs' accuracy. Results: In numerical evaluation, the mean VVD is 3.7(±2.0) mm after Demons, and 1.3(±0.9) mm after TPS-RPM. The mean HD is 14.4 mm after Demons, and 5.3mm after TPS-RPM. The mean PE is 101.7% after Demons and decreases to 18.7% after TPS-RPM. The mean CI is 0.63 after Demons, and increases to 0.90 after TPS-RPM. In the phantom study, the mean Euclidean distance of the fiducials is 7.4±3.0mm and 4.2±1.8mm after Demons and TPS-RPM, respectively. Conclusions: The bladder wall deformation is more accurate using the feature-based TPS-RPM algorithm than the intensity-based Demons algorithm, indicating that TPS-RPM has the potential for accurate bladder dose deformation and dose summation for multi-fractional cervical HDR brachytherapy. This work is supported in part by the National Natural ScienceFoundation of China (no 30970866 and no 81301940)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

TU-F-17A-06: Motion Stability and Dosimetric Impact of Spirometer-Based DIBH-RT of Left-Sided Breast Cancer

Purpose: Patients undergoing radiotherapy (RT) for left-sided breast cancer have increased risk of coronary artery disease. Deep Inhalation Breath Hold assisted RT (DIBH-RT) is shown to increase the geometric separation of the target area and heart, reducing cardiac radiation dose. The purposes of this study are to use Cine MV portal images to determine the stability of spirometer-guided DIBH-RT and examine the dosimetric cardiopulmonary impact of this technique. Methods: Twenty consecutive patients with left-sided breast cancer were recruited to the IRB-approved study. Free-breathing (FB) and DIBH-CT's were acquired at simulation. Rigid registration of the FB-CT and DIBH-CT was performed using primarily breast tissue. Treatment plans were created for each FB-CT and DIBH-CT using identical paired tangent fields with field-in-field or electronic compensation techniques. Dosimetric evaluation included mean and maximum (Dmax) doses for the left anterior descending artery (LAD), mean heart dose, and left lung V20. Cine MV portal images were acquired for medial and lateral fields during treatment. Analysis of Cine images involved chest wall segmentation using an algorithm developed in-house. Intra- and inter-fractional chest wall motion were determined through affine registration to the first frame of each Cine. Results: Dose to each cardiac structure evaluated was significantly (p<0.001) reduced with the DIBH plans. Mean heart dose decreased from 2.9(0.9–6.6) to 1.6(0.6–5.3) Gy; mean LAD dose from 16.6(3–43.6) to 7.4(1.7–32.7) Gy; and LAD Dmax from 35.4 (6.1–53) to 18.4(2.5–51.2) Gy. No statistically significant reduction was found for the left lung V20. Average AP and SI median chest wall motion (intrafractional) was 0.1 (SD=0.9) and 0.5 (SD=1.1) mm, respectively. Average AP inter-fractional chest wall motion was 2.0 (SD=1.4) mm. Conclusion: Spirometer-based DIBH treatments of the left breast are reproducible both inter- and intra-fractionally, and provide a statistically and potentially clinically useful dosimetric advantage to cardiac structures.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

MATBOX (Microstructure Analysis Toolbox) [SWR-20-76]

MATBOX is a MATLAB application for performing various microstructure-related tasks including microstructure numerical generation, image filtering and microstructure segmentation, microstructure characterization, result three-dimensional visualization and result correlation, and microstructure meshing. MATBOX was originally developed to analyze electrode microstructures for lithium ion batteries; however, the algorithms provided by the toolbox are widely applicable to other heterogeneous materials. The toolbox provides a user-friendly experience thanks to a Graphic-User Interface.

Usseglio Viretta, Francois↗

DOE OSTI2012

On an Enhanced PERSIANN-CCS Algorithm for Precipitation Estimation

By employing wavelet and selected features (WSF), median merging (MM), and selected curve-fitting (SCF) techniques, the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) has been improved. The PERSIANN-CCS methodology includes the following four main steps: 1) segmentation of satellite cloud images into cloud patches, 2) feature extraction, 3) classification of cloud patches, and 4) derivation of the temperature rain-rate (T R) relationship for every cluster. The enhancements help improve step 2 by employing WSF, and step 4 by employing MM and SCF. For the study area herein, the results show that the enhanced methodology improves the equitable threat score (ETS) of the daily and hourly rainfall estimates mostly in the winter and fall. The ETS percentage improvement is about 20% for the daily (10% for hourly) estimates in the winter, 10% for the daily (8% for hourly) estimates in the fall, and at most 5% for the daily estimates in the summer at some rainfall thresholds. In the winter and fall, the area bias is improved almost at all rainfall thresholds for daily and hourly estimates. However, no significant improvement is obtained in the spring, and the area bias in the summer is also greater than that of the implemented PERSIANN-CCS algorithm.

Mahrooghy, Majid↗

DOE OSTI2026

Data for "High-resolution microCT reveals relationships between stomata and interior leaf anatomy in Sorghum"

Includes three different types of stacks, in two folders: "REC_RAW_STACKS.zip" contains: (1) Raw gray-scale reconstructed microCT x-ray scans, in the form of individual stacks per sample. "ML_STACKS.zip" contains: (2) Stacks that have been labeled using a machine-learning mask-RCNN pipeline identifying epidermis, mesophyll, airspace, vascular bundle, and background. (3) Stacks that have stomata locations labeled using a small point. The three types of stacks were used to calculate a variety of anatomical and physiological traits, using ImageJ Macros provided on Github: https://github.com/leakey-lab/microCT-Macros-Fischer. Young leaves from WT and transgenic Sorghum plants. CSV "microCT_REC2_META.csv" contains metadata, including sample/sub-sample labels.

3D↗

DOE OSTI2015

Snake River Plain Geothermal Play Fairway Analysis Heat, Permeability, and Seal CRS Map Raster Files

Snake River Plain Play Fairway Analysis - Phase 1 CRS Raster Files. This dataset contains raster files created in ArcGIS. These raster images depict Common Risk Segment (CRS) maps for HEAT, PERMEABILITY, AND SEAL, as well as selected maps of Evidence Layers. These evidence layers consist of either Bayesian krige functions or kernel density functions, and include: (1) HEAT: Heat flow (Bayesian krige map), Heat flow standard error on the krige function (data confidence), volcanic vent distribution as function of age and size, groundwater temperature (equivalue interval and natural breaks bins), and groundwater T standard error. (2) PERMEABILTY: Fault and lineament maps, both as mapped and as kernel density functions, processed for both dilational tendency (TD) and slip tendency (ST), along with data confidence maps for each data type. Data types include mapped surface faults from USGS and Idaho Geological Survey data bases, as well as unpublished mapping; lineations derived from maximum gradients in magnetic, deep gravity, and intermediate depth gravity anomalies. (3) SEAL: Seal maps based on presence and thickness of lacustrine sediments and base of SRP aquifer. Raster size is 2 km. All files generated in ArcGIS.

15 GEOTHERMAL ENERGY↗

DOE OSTI2019

Deep Direct-Use Feasibility Study Estimation of Reservoir Properties for the Tuscarora Sandstone

This dataset contains black-and-white photographic images of individual core segments from well Preston 119 and photomicrographs taken of petrographic thin sections from well Clay 513. Minipermeameter measurement results of 2,260 samples for fracture and matrix permeability on core segments for well Preston 119. These results are summarized in figures, spreadsheets, and PowerPoints. Microsoft Excel will be necessary to examine the permeability data and software capable of reading a .PDF file will be needed to view the file of annotated core CT scans. Microsoft Powerpoint will be necessary to view the photomicrograph presentation.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

Describing Point Defect Topology in 2D Energy Materials Through Computer Vision

2D materials↗

DOE OSTI2026

Describing Point Defect Topology in 2D Energy Materials Through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. Here we employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science. ML is often not just a matter of straightforward application, and pretrained models proved ineffective in this case. Instead, we trained our own neural network (NN) and applied data augmentation techniques and fine-tuning to the training dataset. Since labeled microscopy data is often scarce, we developed training data from a previously published wide-frame MXene image, using customized Gaussian fitting to locate atomic positions. Our trained model was then applied to a large dataset of experimental images, enabling a statistical study of defect configurations across three samples prepared with different HF etchant concentrations (5%, 9.1%, and 12.5%), as shown in Fig. 1. This also allowed us to investigate local strain around vacancies, though we find that we are limited by the precision of measurements using high-angle annular dark field (HAADF) images, as shown in Fig. 2. This study demonstrates how ML enables large-scale, quantitative analysis of atomic defects - an otherwise infeasible task with traditional methods. While our NN was specialized for Ti3C2 MXenes, the pipeline we developed provides a foundation for future ML models tailored to other materials. Ultimately, we envision embedding the NN onto the microscope to give real-time feedback to the user. To make this a reality, continued work is necessary to fully understand the NN's capabilities and limitations. This study gets one step closer to our goals of automated experimentation moving away from traditional methods of manual labeling. As ML capabilities advance, we hope to continue adapting and applying these techniques in microscopy.

2D materials↗

DOE OSTI2026

Assembly and Testing of Scintillator Tile Modules for the CMS High-Granularity Calorimeter Upgrade

The High-Granularity Calorimeter (HGCAL), part of the upgrade to the Compact Muon Solenoid (CMS) experiment, employs silicon and scintillator tile modules in the endcaps to maintain detector performance during the High-Luminosity Large Hadron Collider (HL-LHC) era, scheduled to operate from 2030 to 2040. The upgraded calorimeter will provide highly segmented three-dimensional imaging, energy measurements, and precise timing for particle shower reconstruction. Approximately half of the HGCal plastic scintillator tile modules (1,834 total) will be assembled at Fermilab using an automated pick-and-place (PnP) machine. Following assembly, each tile module undergoes quality control (QC) procedures such as electrical validation, thermal testing, and physics validation using cosmic rays. This poster/talk presents the QC procedures and results from the tile module production in 2026, demonstrating the performance and quality of the assembled tile modules.

Bazimya, Jean Luc [Grambling State U.]↗

DOE OSTI2023

A novel statistical methodology for quantifying the spatial arrangements of axons in peripheral nerves

A thorough understanding of the neuroanatomy of peripheral nerves is required for a better insight into their function and the development of neuromodulation tools and strategies. In biophysical modeling, it is commonly assumed that the complex spatial arrangement of myelinated and unmyelinated axons in peripheral nerves is random, however, in reality the axonal organization is inhomogeneous and anisotropic. Present quantitative neuroanatomy methods analyze peripheral nerves in terms of the number of axons and the morphometric characteristics of the axons, such as area and diameter. In this study, we employed spatial statistics and point process models to describe the spatial arrangement of axons and Sinkhorn distances to compute the similarities between these arrangements (in terms of first- and second-order statistics) in various vagus and pelvic nerve cross-sections. We utilized high-resolution transmission electron microscopy (TEM) images that have been segmented using a custom-built high-throughput deep learning system based on a highly modified U-Net architecture. Our findings show a novel and innovative approach to quantifying similarities between spatial point patterns using metrics derived from the solution to the optimal transport problem. We also present a generalizable pipeline for quantitative analysis of peripheral nerve architecture. Our data demonstrate differences between male- and female-originating samples and similarities between the pelvic and abdominal vagus nerves.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Ultra-Structural Imaging Provides 3D Organization of 46 Chromosomes of a Human Lymphocyte Prophase Nucleus

Three dimensional (3D) ultra-structural imaging is an important tool for unraveling the organizational structure of individual chromosomes at various stages of the cell cycle. Performing hitherto uninvestigated ultra-structural analysis of the human genome at prophase, we used serial block-face scanning electron microscopy (SBFSEM) to understand chromosomal architectural organization within 3D nuclear space. Acquired images allowed us to segment, reconstruct, and extract quantitative 3D structural information about the prophase nucleus and the preserved, intact individual chromosomes within it. Our data demonstrate that each chromosome can be identified with its homolog and classified into respective cytogenetic groups. Thereby, we present the first 3D karyotype built from the compact axial structure seen on the core of all prophase chromosomes. The chromosomes display parallel-aligned sister chromatids with familiar chromosome morphologies with no crossovers. Furthermore, the spatial positions of all 46 chromosomes revealed a pattern showing a gene density-based correlation and a neighborhood map of individual chromosomes based on their relative spatial positioning. A comprehensive picture of 3D chromosomal organization at the nanometer level in a single human lymphocyte cell is presented.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS1982

Proceedings of the NASA Workshop on Image Analysis

Three major topics of image analysis are addressed: segmentation, shape and texture analysis, and structural analysis.

Guseman, L. F., Jr.↗

NASA NTRS1988

An Imaging Spectrometric Observatory (ISO)

The Imaging Spectrometric Observatory (ISO) is designed for low light level spectroscopy of both the day and night side of the earth. The instrument is composed of five spectrometers, each of which covers part of the total wavelength range of 30 to 1300 nm spanned by the instrument. Wavelength resolution varies between 0.2 and 0.6 nm over the spectral range. The five spectrometers are each optimized for a portion of the spectrum by the choice of mirror reflective coatings and detector photocathode materials. The full spectral range for each spectrometer is covered in a total of 11 grating steps. The Imaging Spectrometric Observatory was flown for the first time on the Spacelab 1 mission during which it acquired almost 40 hr of observations. The ISO investigation to be flown on the Atmospheric Laboratory for Applications and Science (ATLAS 1) mission will draw on the experience gained from the data gathered on Spacelab 1. The detector system in each spectrometer was upgraded to provide both higher sensitivity at low light levels and simultaneous imaging over larger spectral segments than was achieved on Spacelab 1. In addition, the instrument and the observing sequences were modified to allow observation of the sun in the extreme ultraviolet. A summary of ISO parameters for ATLAS 1 (scheduled for late 1990) is given.

Torr, M. R.↗

NASA NTRS2006

Commissioning Results on the JWST Testbed Telescope

The one-meter 18 segment JWST Testbed Telescope (TBT) has been developed at Ball Aerospace to facilitate commissioning operations for the JWST Observatory. Eight different commissioning activities were tested on the TBT: telescope focus sweep, segment ID and Search, image array, global alignment, image stacking, coarse phasing, fine phasing, and multi-field phasing. This paper describes recent commissioning results from experiments performed on the TBT.

Dean, Bruce H.↗

DOE OSTI2021

Deep Learning Segmentation of Complex Features in Atomic-Resolution Phase-Contrast Transmission Electron Microscopy Images

Phase-contrast transmission electron microscopy (TEM) is a powerful tool for imaging the local atomic structure of materials. TEM has been used heavily in studies of defect structures of two-dimensional materials such as monolayer graphene due to its high dose efficiency. However, phase-contrast imaging can produce complex nonlinear contrast, even for weakly scattering samples. It is, therefore, difficult to develop fully automated analysis routines for phase-contrast TEM studies using conventional image processing tools. For automated analysis of large sample regions of graphene, one of the key problems is segmentation between the structure of interest and unwanted structures such as surface contaminant layers. In this study, we compare the performance of a conventional Bragg filtering method with a deep learning routine based on the U-Net architecture. We show that the deep learning method is more general, simpler to apply in practice, and produces more accurate and robust results than the conventional algorithm. We provide easily adaptable source code for all results in this paper and discuss potential applications for deep learning in fully automated TEM image analysis.

47 OTHER INSTRUMENTATION↗

DOE OSTI2016

SU-D-202-02: Quantitative Imaging: Correlation Between Image Feature Analysis and the Accuracy of Manually Drawn Contours On PET Images

Purpose: To evaluate the correlation between image features and the accuracy of manually drawn target contours on synthetic PET images Methods: A digital PET phantom was used in combination with Monte Carlo simulation to create a set of 26 simulated PET images featuring a variety of tumor shapes and activity heterogeneity. These tumor volumes were used as a gold standard in comparisons with manual contours delineated by 10 radiation oncologist on the simulated PET images. Metrics used to evaluate segmentation accuracy included the dice coefficient, false positive dice, false negative dice, symmetric mean absolute surface distance, and absolute volumetric difference. Image features extracted from the simulated tumors consisted of volume, shape complexity, mean curvature, and intensity contrast along with five texture features derived from the gray-level neighborhood difference matrices including contrast, coarseness, busyness, strength, and complexity. Correlation between these features and contouring accuracy were examined. Results: Contour accuracy was reasonably well correlated with a variety of image features. Dice coefficient ranged from 0.7 to 0.90 and was correlated closely with contrast (r=0.43, p=0.02) and complexity (r=0.5, p<0.001). False negative dice ranged from 0.10 to 0.50 and was correlated closely with contrast (r=0.68, p<0.001) and complexity (r=0.66, p<0.001). Absolute volumetric difference ranged from 0.0002 to 0.67 and was correlated closely with coarseness (r=0.46, p=0.02) and complexity (r=0.49, p=0.008). Symmetric mean absolute difference ranged from 0.02 to 1 and was correlated closely with mean curvature (r=0.57, p=0.02) and contrast (r=0.6, p=0.001). Conclusion: The long term goal of this study is to assess whether contouring variability can be reduced by providing feedback to the practitioner based on image feature analysis. The results are encouraging and will be used to develop a statistical model which will enable a prediction of contour accuracy based purely on image feature analysis.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2004

A Comparison of Local Variance, Fractal Dimension, and Moran's I as Aids to Multispectral Image Classification

The accuracy of traditional multispectral maximum-likelihood image classification is limited by the skewed statistical distributions of reflectances from the complex heterogenous mixture of land cover types in urban areas. This work examines the utility of local variance, fractal dimension and Moran's I index of spatial autocorrelation in segmenting multispectral satellite imagery. Tools available in the Image Characterization and Modeling System (ICAMS) were used to analyze Landsat 7 imagery of Atlanta, Georgia. Although segmentation of panchromatic images is possible using indicators of spatial complexity, different land covers often yield similar values of these indices. Better results are obtained when a surface of local fractal dimension or spatial autocorrelation is combined as an additional layer in a supervised maximum-likelihood multispectral classification. The addition of fractal dimension measures is particularly effective at resolving land cover classes within urbanized areas, as compared to per-pixel spectral classification techniques.

Emerson, Charles W.↗