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At least 289 records · Page 16

All sources NASA NTRS DOE OSTI

DOE OSTI2015

A new stationary gridline artifact suppression method based on the 2D discrete wavelet transform

Purpose: In digital x-ray radiography, an antiscatter grid is inserted between the patient and the image receptor to reduce scattered radiation. If the antiscatter grid is used in a stationary way, gridline artifacts will appear in the final image. In most of the gridline removal image processing methods, the useful information with spatial frequencies close to that of the gridline is usually lost or degraded. In this study, a new stationary gridline suppression method is designed to preserve more of the useful information. Methods: The method is as follows. The input image is first recursively decomposed into several smaller subimages using a multiscale 2D discrete wavelet transform. The decomposition process stops when the gridline signal is found to be greater than a threshold in one or several of these subimages using a gridline detection module. An automatic Gaussian band-stop filter is then applied to the detected subimages to remove the gridline signal. Finally, the restored image is achieved using the corresponding 2D inverse discrete wavelet transform. Results: The processed images show that the proposed method can remove the gridline signal efficiently while maintaining the image details. The spectra of a 1D Fourier transform of the processed images demonstrate that, compared with some existing gridline removal methods, the proposed method has better information preservation after the removal of the gridline artifacts. Additionally, the performance speed is relatively high. Conclusions: The experimental results demonstrate the efficiency of the proposed method. Compared with some existing gridline removal methods, the proposed method can preserve more information within an acceptable execution time.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Defect Characterization Through Automated Laser Track Trace Identification in SLM Processes Using Laser Profilometer Data

This paper presents recent developments in statistical image processing to demonstrate feasibility of layer-by-layer nondestructive inspection for selective laser melting (SLM) of metallic parts. A matrix of 10 mm × 10 mm stainless 316 squares was deposited with the first row consisting of just the sintered surface, Row 2 a single layer, Row 3 with 2 layers and so on. These layers were scanned to emulate the layer-by-layer collection of top surface geometry using a laser sensor. The resultant data were utilized to generate ISO 25718 roughness parameters and subsequently to perform track identification. This calculation consisted of two salient steps: (1) computing the gradients of surface roughness, which vanish at the peaks and pits, and (2) extracting the scanning direction by taking Hough transform of the geometry. This algorithm was repeated for all layers, and the alignment of the roughness with the rotating scan was observed to be persistent for all layers. Correlation between surface properties obtained along various scan directions and defect probability is explored.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Automated Characterization of Uranium-Molybdenum Fuel Microstructures

Currently, performance evaluators rely on visual inspection or manual segmentation to assess most parameters of interest within an irradiated microstructure. Automating this process has the potential to significantly speed up the data extraction process, enhance its reliability, and improve its correctness. Automatic image segmentation of target features is a critical tool for providing measurements of features that may be used to increase the fidelity of fuel performance modeling. This summary will present the progress made in developing and testing automated image processing routines capable of fully characterizing the presence of fission gas bubbles in irradiated U-Mo plate-type fuel microstructures.

Image Processing↗

DOE OSTI2020

Analysis of laser-induced modification of collagen structure using nonlinear optical microscopy

Nonlinear optical microscopy is used to monitor changes in the collagen structure of patellar ligament after IR laser heating up to a temperature of 60 °C. Pre-denaturation modification of the tissue matrix exhibits specific properties and consists in splitting of initially parallel fibre bundles into array of divergent fibres. Image processing employing Fast Fourier transform analysis is used for characterising images of the fibre structure and determining the collagen orientation index. An increase in manifestation of laser-induced disordering with an increase in IR laser radiation power from 0.3 to 2 W is accompanied by a decrease in the collagen orientation index. The specific disorganisation of the collagen tissue structure during laser heating is supposed to be due to fast speed and local action of the laser heating, which leads to mechanical distortions of the structural elements of tissue matrix. (paper)

36 MATERIALS SCIENCE↗

DOE OSTI2014

Discovery of a new class of coronal structures in white light eclipse images

White light images of the solar corona, taken during total solar eclipses, capture the complex dynamic relationship between the coronal plasma and the magnetic field. This relationship can be recorded on timescales of seconds to minutes, within a few solar radii above the solar surface. Rays, large-scale loops, and streamers, which are the brightest structures in these images, have shaped current models of the coronal magnetic field and solar wind flow. We show in this work how the application of novel image processing techniques to unique high-resolution white light eclipse images reveals the presence of a new class of structures, reminiscent of smoke rings, faint nested expanding loops, expanding bubbles, and twisted helical structures. These features are interpreted as snapshots of the dynamical evolution of instabilities developing at prominence-corona interfaces and propagating outward with the solar wind.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2018

Method for phase retrieval to reduce a sampling requirement when imaging a dynamic process

A method for retrieving phase information in a coherent diffraction imaging process includes acquiring a plurality of 3D data sets, each 3D data set corresponding to one of a plurality of time states, and reconstructing a 3D image of the object at a given time state using the 3D data set from all of the time states. Each 3D data set is acquired by: illuminating an object positioned in a first position with a coherent beam; measuring a first 2D diffraction pattern using an area detector; rotating the object around a tilt axis thereof to a second position that is different from the first position; re-illuminating the object positioned in the second position with the coherent beam; re-measuring a second 2D diffraction pattern using the area detector; and repeating the rotating, re-illuminating and re-measuring steps such that each 3D data set includes a predetermined number of diffraction patterns.

36 MATERIALS SCIENCE↗

DOE OSTI2014

SU-C-9A-03: Simultaneous Deconvolution and Segmentation for PET Tumor Delineation Using a Variational Method

Purpose: To implement a new method that integrates deconvolution with segmentation under the variational framework for PET tumor delineation. Methods: Deconvolution and segmentation are both challenging problems in image processing. The partial volume effect (PVE) makes tumor boundaries in PET image blurred which affects the accuracy of tumor segmentation. Deconvolution aims to obtain a PVE-free image, which can help to improve the segmentation accuracy. Conversely, a correct localization of the object boundaries is helpful to estimate the blur kernel, and thus assist in the deconvolution. In this study, we proposed to solve the two problems simultaneously using a variational method so that they can benefit each other. The energy functional consists of a fidelity term and a regularization term, and the blur kernel was limited to be the isotropic Gaussian kernel. We minimized the energy functional by solving the associated Euler-Lagrange equations and taking the derivative with respect to the parameters of the kernel function. An alternate minimization method was used to iterate between segmentation, deconvolution and blur-kernel recovery. The performance of the proposed method was tested on clinic PET images of patients with non-Hodgkin's lymphoma, and compared with seven other segmentation methods using the dice similarity index (DSI) and volume error (VE). Results: Among all segmentation methods, the proposed one (DSI=0.81, VE=0.05) has the highest accuracy, followed by the active contours without edges (DSI=0.81, VE=0.25), while other methods including the Graph Cut and the Mumford-Shah (MS) method have lower accuracy. A visual inspection shows that the proposed method localizes the real tumor contour very well. Conclusion: The result showed that deconvolution and segmentation can contribute to each other. The proposed variational method solve the two problems simultaneously, and leads to a high performance for tumor segmentation in PET. This work was supported in part by National Natural Science Foundation of China (NNSFC), under Grant Nos. 60971112 and 61375018, and Fundamental Research Funds for the Central Universities, under Grant No. 2012QN086. Wei Lu was supported in part by the National Institutes of Health (NIH) Grant No. R01 CA172638.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2016

MO-FG-202-04: Gantry-Resolved Linac QA for VMAT: A Comprehensive and Efficient System Using An Electronic Portal Imaging Device

Purpose: To automate gantry-resolved linear accelerator (linac) quality assurance (QA) for volumetric modulated arc therapy (VMAT) using an electronic portal imaging device (EPID). Methods: A QA system for VMAT was developed that uses an EPID, frame-grabber assembly and in-house developed image processing software. The system relies solely on the analysis of EPID image frames acquired without the presence of a phantom. Images were acquired at 8.41 frames per second using a frame grabber and ancillary acquisition computer. Each image frame was tagged with a gantry angle from the linac’s on-board gantry angle encoder. Arc-dynamic QA plans were designed to assess the performance of each individual linac component during VMAT. By analysing each image frame acquired during the QA deliveries the following eight machine performance characteristics were measured as a function of gantry angle: MLC positional accuracy, MLC speed constancy, MLC acceleration constancy, MLC-gantry synchronisation, beam profile constancy, dose rate constancy, gantry speed constancy, dose-gantry angle synchronisation and mechanical sag. All tests were performed on a Varian iX linear accelerator equipped with a 120 leaf Millennium MLC and an aS1000 EPID (Varian Medical Systems, Palo Alto, CA, USA). Results: Machine performance parameters were measured as a function of gantry angle using EPID imaging and compared to machine log files and the treatment plan. Data acquisition is currently underway at 3 centres, incorporating 7 treatment units, at 2 weekly measurement intervals. Conclusion: The proposed system can be applied for streamlined linac QA and commissioning for VMAT. The set of test plans developed can be used to assess the performance of each individual components of the treatment machine during VMAT deliveries as a function of gantry angle. The methodology does not require the setup of any additional phantom or measurement equipment and the analysis is fully automated to allow for regular routine testing.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

In Situ Height and Width Estimation of Sorghum Plants from 2.5d Infrared Images

Plant phenotyping, or the measurement of plant traits such as stem width and plant height, is a critical step in the development and evaluation of higher yield biofuel crops. Phenotyping allows biologists to quantitatively estimate the biomass of plant varieties and therefore their potential for biofuel production. Manual phenotyping is costly, time-consuming, and errorprone, requiring a person to walk through the fields measuring individual plants with a tape measure and notebook. In this work we describe an alternative system consisting of an autonomous robot equipped with two infrared cameras that travels through fields, collecting 2.5D image data of sorghum plants. We develop novel image processing based algorithms to estimate plant height and stem width from the image data. Our proposed method has the advantage of working in situ using images of plants from only one side. This allows phenotypic data to be collected nondestructively throughout the growing cycle, providing biologists with valuable information on crop growth patterns. Our approach first estimates plant heights and stem widths from individual frames. It then uses tracking algorithms to refine these estimates across frames and avoid double counting the same plant in multiple frames. The result is a histogram of stem widths and plant heights for each plot of a particular genetically engineered sorghum variety. In-field testing and comparison with human collected ground truth data demonstrates that our system achieves 13% average absolute error for stem width estimation and 15% average absolute error for plant height estimation.

09 BIOMASS FUELS↗

DOE OSTI2019

Validation of a DIXON-based fat quantification technique for the measurement of visceral fat using a CT-based reference standard

Purpose: The purpose of the study is to determine whether a novel semi-automated DIXON-based fat quantification algorithm can reliably quantify visceral fat using a CT-based reference standard. Methods: This was an IRB-approved retrospective cohort study of 27 subjects who underwent abdominopelvic CT within 7 days of proton density fat fraction (PDFF) mapping on a 1.5T MRI. Cross-sectional visceral fat area per slice (cm{sup 2}) was measured in blinded fashion in each modality at intervertebral disc levels from T12 to L4. CT estimates were obtained using a previously published semi-automated computational image processing system that sums pixels with attenuation − 205 to − 51 HU. MR estimates were obtained using two novel semi-automated DIXON-based fat quantification algorithms that measure visceral fat area by spatially regularizing non-uniform fat-only signal intensity or de-speckling PDFF 2D images and summing pixels with PDFF ≥ 50%. Pearson’s correlations and Bland–Altman analyses were performed. Results: Visceral fat area per slice ranged from 9.2 to 429.8 cm{sup 2} for MR and from 1.6 to 405.5 cm{sup 2} for CT. There was a strong correlation between CT and MR methods in measured visceral fat area across all studied vertebral body levels (r = 0.97; n = 101 observations); the least (r = 0.93) correlation was at T12. Bland–Altman analysis revealed a bias of 31.7 cm{sup 2} (95% CI [− 27.1]–90.4 cm{sup 2}), indicating modestly higher visceral fat assessed by MR. Conclusion: MR- and CT-based visceral fat quantification are highly correlated and have good cross-modality reliability, indicating that visceral fat quantification by either method can yield a stable and reliable biomarker.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2016

Aerosol Plume Detection Algorithm Based on Image Segmentation of Scanning Atmospheric Lidar Data

An image-processing algorithm has been developed to identify aerosol plumes in scanning lidar backscatter data. The images in this case consist of lidar data in a polar coordinate system. Each full lidar scan is taken as a fixed image in time, and sequences of such scans are considered functions of time. The data are analyzed in both the original backscatter polar coordinate system and a lagged coordinate system. The lagged coordinate system is a scatterplot of two datasets, such as subregions taken from the same lidar scan (spatial delay), or two sequential scans in time (time delay). The lagged coordinate system processing allows for finding and classifying clusters of data. The classification step is important in determining which clusters are valid aerosol plumes and which are from artifacts such as noise, hard targets, or background fields. These cluster classification techniques have skill since both local and global properties are used. Furthermore, more information is available since both the original data and the lag data are used. Performance statistics are presented for a limited set of data processed by the algorithm, where results from the algorithm were compared to subjective truth data identified by a human.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

High-Multiplicity Muon Airshower Analysis at NOvA Far Detector

We process and analyze muon airshower data from the NOvA far detector using various image processing algorithms, such as Fast Fourier Transformation, and Hough line transformation. From the processed event images, we calculate multiple parameters for our study. We are looking for physics features, including East-West Asymmetry, anisotropies in right ascension, and seasonal variation. Additionally, we have developed an algorithm to count the multiplicity of muons in the airshower events using the single muon data.

Lima, Aklima Khanam [Syracuse U.]↗

DOE OSTI2018

Advanced data mining in field ion microscopy

Field ion microscopy (FIM) allows to image individual surface atoms by exploiting the effect of an intense electric field. Widespread use of atomic resolution imaging by FIM has been hampered by a lack of efficient image processing/data extraction tools. Recent advances in imaging and data mining techniques have renewed the interest in using FIM in conjunction with automated detection of atoms and lattice defects for materials characterization. After a brief overview of existing routines, we review the use of machine learning (ML) approaches for data extraction with the aim to catalyze new data-driven insights into high electrical field physics. Apart from exploring various supervised and unsupervised ML algorithms in this context, we also employ advanced image processing routines for data extraction from large sets of FIM images. The outcomes and limitations of such routines are discussed, and we conclude with the possible application of energy minimization schemes to the extracted point clouds as a way of improving the spatial resolution of FIM.

36 MATERIALS SCIENCE↗

DOE OSTI2015

MO-FG-303-04: A Smartphone Application for Automated Mechanical Quality Assurance of Medical Accelerators

Purpose: The mechanical quality assurance (QA) of medical accelerators consists of a time consuming series of procedures. Since most of the procedures are done manually – e.g., checking gantry rotation angle with the naked eye using a level attached to the gantry –, it is considered to be a process with high potential for human errors. To remove the possibilities of human errors and reduce the procedure duration, we developed a smartphone application for automated mechanical QA. Methods: The preparation for the automated process was done by attaching a smartphone to the gantry facing upward. For the assessments of gantry and collimator angle indications, motion sensors (gyroscope, accelerator, and magnetic field sensor) embedded in the smartphone were used. For the assessments of jaw position indicator, cross-hair centering, and optical distance indicator (ODI), an optical-image processing module using a picture taken by the high-resolution camera embedded in the smartphone was implemented. The application was developed with the Android software development kit (SDK) and OpenCV library. Results: The system accuracies in terms of angle detection error and length detection error were < 0.1° and < 1 mm, respectively. The mean absolute error for gantry and collimator rotation angles were 0.03° and 0.041°, respectively. The mean absolute error for the measured light field size was 0.067 cm. Conclusion: The automated system we developed can be used for the mechanical QA of medical accelerators with proven accuracy. For more convenient use of this application, the wireless communication module is under development. This system has a strong potential for the automation of the other QA procedures such as light/radiation field coincidence and couch translation/rotations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

DENNIS: a design and analysis tool for dynamic material x-ray diffraction experiments

We present DENNIS (Diffraction Experiment desigN and aNalysiS): a graphical software tool useful for the design and analysis of dynamic x-ray diffraction experiments, such as those performed on the Z Pulsed Power Facility, Thor Pulsed Power Generator, and Dynamic Compression Sector (DCS) of the Advanced Photon Source. DENNIS provides rapid powder and single-crystal diffraction pattern predictions and powder diffraction pattern image integration in three-dimensional geometries. Additional features include crystallographic information file reading, image processing, and synthetic diffraction pattern image generation. We overview the software's capabilities, detail the prediction and integration methodologies, and provide example implementations on Z and DCS experiments.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

LLM integration into EPICS

The utilization of large language models (LLMs) such as ChatGPT has seen a remarkable increase in various fields over the past few years. These models have demonstrated their versatility and capability in understanding and generating human-like text, making them invaluable tools in numerous applications. In this project, we explore the integration of a LLM into the Experimental Physics and Industrial Control System (EPICS). The primary focus of this integration is to employ the LLM for advanced image processing and spatial analysis on images obtained from the beamlines. By leveraging the capabilities of the LLM, we aim to enhance the accuracy and efficiency of image interpretation, enabling more precise data analysis and decision-making within the EPICS framework. This integration not only showcases the potential of LLMs in scientific and industrial applications but also sets the stage for future advancements in automated control systems.

Adams, Ethan↗

DOE OSTI2017

Noise-gating to Clean Astrophysical Image Data

I present a family of algorithms to reduce noise in astrophysical images and image sequences, preserving more information from the original data than is retained by conventional techniques. The family uses locally adaptive filters (“noise gates”) in the Fourier domain to separate coherent image structure from background noise based on the statistics of local neighborhoods in the image. Processing of solar data limited by simple shot noise or by additive noise reveals image structure not easily visible in the originals, preserves photometry of observable features, and reduces shot noise by a factor of 10 or more with little to no apparent loss of resolution. This reveals faint features that were either not directly discernible or not sufficiently strongly detected for quantitative analysis. The method works best on image sequences containing related subjects, for example movies of solar evolution, but is also applicable to single images provided that there are enough pixels. The adaptive filter uses the statistical properties of noise and of local neighborhoods in the data to discriminate between coherent features and incoherent noise without reference to the specific shape or evolution of those features. The technique can potentially be modified in a straightforward way to exploit additional a priori knowledge about the functional form of the noise.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

Magnetic Resonance–Based Automatic Air Segmentation for Generation of Synthetic Computed Tomography Scans in the Head Region

Purpose: To incorporate a novel imaging sequence for robust air and tissue segmentation using ultrashort echo time (UTE) phase images and to implement an innovative synthetic CT (synCT) solution as a first step toward MR-only radiation therapy treatment planning for brain cancer. Methods and Materials: Ten brain cancer patients were scanned with a UTE/Dixon sequence and other clinical sequences on a 1.0 T open magnet with simulation capabilities. Bone-enhanced images were generated from a weighted combination of water/fat maps derived from Dixon images and inverted UTE images. Automated air segmentation was performed using unwrapped UTE phase maps. Segmentation accuracy was assessed by calculating segmentation errors (true-positive rate, false-positive rate, and Dice similarity indices using CT simulation (CT-SIM) as ground truth. The synCTs were generated using a voxel-based, weighted summation method incorporating T2, fluid attenuated inversion recovery (FLAIR), UTE1, and bone-enhanced images. Mean absolute error (MAE) characterized Hounsfield unit (HU) differences between synCT and CT-SIM. A dosimetry study was conducted, and differences were quantified using γ-analysis and dose-volume histogram analysis. Results: On average, true-positive rate and false-positive rate for the CT and MR-derived air masks were 80.8% ± 5.5% and 25.7% ± 6.9%, respectively. Dice similarity indices values were 0.78 ± 0.04 (range, 0.70-0.83). Full field of view MAE between synCT and CT-SIM was 147.5 ± 8.3 HU (range, 138.3-166.2 HU), with the largest errors occurring at bone–air interfaces (MAE 422.5 ± 33.4 HU for bone and 294.53 ± 90.56 HU for air). Gamma analysis revealed pass rates of 99.4% ± 0.04%, with acceptable treatment plan quality for the cohort. Conclusions: A hybrid MRI phase/magnitude UTE image processing technique was introduced that significantly improved bone and air contrast in MRI. Segmented air masks and bone-enhanced images were integrated into our synCT pipeline for brain, and results agreed well with clinical CTs, thereby supporting MR-only radiation therapy treatment planning in the brain.

62 RADIOLOGY AND NUCLEAR MEDICINE↗