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At least 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

A Domain-Specific Language for High-Consequence Control Software

While most software development for control systems is directed at what the system is supposed to do (i.e., function), high-consequence controls must account for what the system is not supposed to do (i.e., safety, security and reliability requirements). A Domain Specific Language (DSL) for high-consequence digital controls is proposed. As with similar tools for the design of controls, the DSL will have plug-in modules for common controller functions. However, the DSL will also augment these modules with attendant "templates" that aid in the proof of safety, security and reliability requirements, not available in current tools. The object is to create a development methodology that makes construction of high-assurance control systems as easy as controls that are designed for function alone.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Advanced Reactor Safeguards & Security Program: Cybersecurity Scenarios

The use of digital control systems and automation in advanced nuclear power systems introduces different types of vulnerabilities compared to legacy (i.e. analog) control systems that cyber adversaries can exploit. These vulnerabilities pose a challenge to reactor operators and cyber operations staff due to the dynamic nature of the event in which a human response or a lack of response can potentially evolve into a worsening plant condition. Using the Department of Homeland Security Cyber and Infrastructure Security Agency’s (CISA) critical infrastructure exercise framework, this document presents several cyber security scenarios typical of digital control systems that could be used in advanced reactor designs. These scenarios can be used in tabletop exercises to evaluate cyber security posture or conduct training on different aspects of cyber security, including detection, threat hunting using indicators of compromise, evaluating incident response, risk mitigation, incident reporting, information sharing and recovery.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2012

THE FAINT END OF THE LUMINOSITY FUNCTION AND LOW SURFACE BRIGHTNESS GALAXIES

Smithsonian Hectospec Lensing Survey (SHELS) is a dense redshift survey covering a 4 deg{sup 2} region to a limiting R = 20.6. In the construction of the galaxy catalog and in the acquisition of spectroscopic targets, we paid careful attention to the survey completeness for lower surface brightness dwarf galaxies. Thus, although the survey covers a small area, it is a robust basis for computation of the slope of the faint end of the galaxy luminosity function to a limiting M{sub R} = -13.3 + 5log h. We calculate the faint-end slope in the R band for the subset of SHELS galaxies with redshifts in the range 0.02 {<=}z < 0.1, SHELS{sub 0.1}. This sample contains 532 galaxies with R < 20.6 and with a median surface brightness within the half-light radius of SB{sub 50,R} = 21.82 mag arcsec{sup -2}. We used this sample to make one of the few direct measurements of the dependence of the faint end of the galaxy luminosity function on surface brightness. For the sample as a whole the faint-end slope, {alpha} = -1.31 {+-} 0.04, is consistent with both the Blanton et al. analysis of the Sloan Digital Sky Survey and the Liu et al. analysis of the COSMOS field. This consistency is impressive given the very different approaches of these three surveys. A magnitude-limited sample of 135 galaxies with optical spectroscopic redshifts with mean half-light surface brightness, SB{sub 50,R} {>=} 22.5 mag arcsec{sup -2} is unique to SHELS{sub 0.1}. The faint-end slope is {alpha}{sub 22.5} = -1.52 {+-} 0.16. SHELS{sub 0.1} shows that lower surface brightness objects dominate the faint-end slope of the luminosity function in the field, underscoring the importance of surface brightness limits in evaluating measurements of the faint-end slope and its evolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Dosimetric characterization and organ dose assessment in digital breast tomosynthesis: Measurements and Monte Carlo simulations using voxel phantoms

Purpose: Due to its capability to more accurately detect deep lesions inside the breast by removing the effect of overlying anatomy, digital breast tomosynthesis (DBT) has the potential to replace the standard mammography technique in clinical screening exams. However, the European Guidelines for DBT dosimetry are still a work in progress and there are little data available on organ doses other than to the breast. It is, therefore, of great importance to assess the dosimetric performance of DBT with respect to the one obtained with standard digital mammography (DM) systems. The aim of this work is twofold: (i) to study the dosimetric properties of a combined DBT/DM system (MAMMOMAT Inspiration Siemens{sup ®}) for a tungsten/rhodium (W/Rh) anode/filter combination and (ii) to evaluate organs doses during a DBT examination. Methods: For the first task, measurements were performed in manual and automatic exposure control (AEC) modes, using two homogeneous breast phantoms: a PMMA slab phantom and a 4 cm thick breast-shaped rigid phantom, with 50% of glandular tissue in its composition. Monte Carlo (MC) simulations were performed using Monte Carlo N-Particle eXtended v.2.7.0. A MC model was implemented to mimic DM and DBT acquisitions for a wide range of x-ray spectra (24 –34 kV). This was used to calculate mean glandular dose (MGD) and to compute series of backscatter factors (BSFs) that could be inserted into the DBT dosimetric formalism proposed by Dance et al. Regarding the second aim of the study, the implemented MC model of the clinical equipment, together with a female voxel phantom (“Laura”), was used to calculate organ doses considering a typical DBT acquisition. Results were compared with a standard two-view mammography craniocaudal (CC) acquisition. Results: Considering the AEC mode, the acquisition of a single CC view results in a MGD ranging from 0.53 ± 0.07 mGy to 2.41 ± 0.31 mGy in DM mode and from 0.77 ± 0.11 mGy to 2.28 ± 0.32 mGy in DBT mode. Regarding the BSF, the results achieved may lead to a MGD correction of about 6%, contributing to the improvement of the current guidelines used in these applications. Finally, considering the MC results obtained for the organ dose study, the radiation doses found for the tissues of the body other than the breast were in the range of tens of μSv, and are in part comparable to the ones obtained in standard DM. Nevertheless, in a single DBT examination, some organs (such as lung and thyroid) receive higher doses (of about 9% and 21%, respectively) with respect to the CC DM acquisition. Conclusions: Taking into account an average breast with a thickness of 4.5 cm, the MGDs for DM and DBT acquisitions were below the achievable value (2.0 mGy) defined by the European protocol. Additionally, in the case of a fusion imaging study (DM + DBT), the MGD for a 4.5 cm thick breast is of the order of 1.88 ± 0.36 mGy. Finally, organ dose evaluations underline the need to improve awareness concerning dose estimation of DBT exams for some organs, especially when radiation risk is assessed by using the effective dose.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Emerging Technologies in the Built Environment: Geographic Information Science (GIS), 3D Printing, and Additive Manufacturing

Abstract 1: Geographic information systems emerged as a computer application in the late 1960s, led in part by projects at ORNL. The concept of a GIS has shifted through time in response to new applications and new technologies, and is now part of a much larger world of geospatial technology. This presentation discusses the relationship of GIS and estimating hourly and seasonal energy consumption profiles in the building sector at spatial scales down to the individual parcel. The method combines annual building energy simulations for city-specific prototypical buildings and commonly available geospatial data in a GIS framework. Abstract 2: This presentation focuses on 3D printing technologies and how they have rapidly evolved over the past couple of years. At a basic level, 3D printing produces physical models quickly and easily from 3D CAD, BIM (Building Information Models), and other digital data. Many AEC firms have adopted 3D printing as part of commercial building design development and project delivery. This presentation includes an overview of 3D printing, discusses its current use in building design, and talks about its future in relation to the HVAC industry. Abstract 3: This presentation discusses additive manufacturing and how it is revolutionizing the design of commercial and residential facilities. Additive manufacturing utilizes a broad range of direct manufacturing technologies, including electron beam melting, ultrasonic, extrusion, and laser metal deposition for rapid prototyping. While there is some overlap with the 3D printing talk, this presentation focuses on the materials aspect of additive manufacturing and also some of the more advanced technologies involved with rapid prototyping. These technologies include design of carbon fiber composites, lightweight metals processing, transient field processing, and more.

New, Joshua Ryan↗
DOE OSTI2019

Pulse Pileup Analysis for a Double-Sided Silicon Strip Detector Using Variable Pulse Shapes

On account of pulse pileup, photon counting detectors (PCDs) suffer from count loss and energy distortion when operating in high count rate environments. Here, we researched the pulse pileup of a double-sided silicon strip detector (DSSSD) to evaluate its potential application in a mammography system. We analyzed the pulse pileup using pulses of varied shapes, where the shape of the pulse depends on the location of photon interaction within the detector. To obtain the shaped pulses, first, transient currents for photons interacting at different locations were simulated using a Technology Computer-Aided Design (TCAD) software. Next, the currents were shaped by a CR- RC 2 shaping circuit, calculated using Simulink. After obtaining these pulses, both the different orders of pileup and the energy spectrum were calculated by taking into account the following two factors: 1) spatial distribution of photon interactions within the detector and 2) time interval distribution between successive photons under a given photon flux. We found that for a DSSSD with a thickness of 300 μm, a pitch of 25 μm, and a strip length of 1 cm, under a bias voltage of 50 V, the variable pulse shape model predicts that the fraction free of pileup can be >90% under a photon flux of 3.75 Mcps/mm 2 (million counts per second per square mm). The DSSSD is a promising candidate for digital mammography applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Patient motion effects on the quantification of regional myocardial blood flow with dynamic PET imaging

Purpose: Patient motion is a common problem during dynamic positron emission tomography (PET) scans for quantification of myocardial blood flow (MBF). The purpose of this study was to quantify the prevalence of body motion in a clinical setting and evaluate with realistic phantoms the effects of motion on blood flow quantification, including CT attenuation correction (CTAC) artifacts that result from PET–CT misalignment. Methods: A cohort of 236 sequential patients was analyzed for patient motion under resting and peak stress conditions by two independent observers. The presence of motion, affected time-frames, and direction of motion was recorded; discrepancy between observers was resolved by consensus review. Based on these results, patient body motion effects on MBF quantification were characterized using the digital NURBS-based cardiac-torso phantom, with characteristic time activity curves (TACs) assigned to the heart wall (myocardium) and blood regions. Simulated projection data were corrected for attenuation and reconstructed using filtered back-projection. All simulations were performed without noise added, and a single CT image was used for attenuation correction and aligned to the early- or late-frame PET images. Results: In the patient cohort, mild motion of 0.5 ± 0.1 cm occurred in 24% and moderate motion of 1.0 ± 0.3 cm occurred in 38% of patients. Motion in the superior/inferior direction accounted for 45% of all detected motion, with 30% in the superior direction. Anterior/posterior motion was predominant (29%) in the posterior direction. Left/right motion occurred in 24% of cases, with similar proportions in the left and right directions. Computer simulation studies indicated that errors in MBF can approach 500% for scans with severe patient motion (up to 2 cm). The largest errors occurred when the heart wall was shifted left toward the adjacent lung region, resulting in a severe undercorrection for attenuation of the heart wall. Simulations also indicated that the magnitude of MBF errors resulting from motion in the superior/inferior and anterior/posterior directions was similar (up to 250%). Body motion effects were more detrimental for higher resolution PET imaging (2 vs 10 mm full-width at half-maximum), and for motion occurring during the mid-to-late time-frames. Motion correction of the reconstructed dynamic image series resulted in significant reduction in MBF errors, but did not account for the residual PET–CTAC misalignment artifacts. MBF bias was reduced further using global partial-volume correction, and using dynamic alignment of the PET projection data to the CT scan for accurate attenuation correction during image reconstruction. Conclusions: Patient body motion can produce MBF estimation errors up to 500%. To reduce these errors, new motion correction algorithms must be effective in identifying motion in the left/right direction, and in the mid-to-late time-frames, since these conditions produce the largest errors in MBF, particularly for high resolution PET imaging. Ideally, motion correction should be done before or during image reconstruction to eliminate PET-CTAC misalignment artifacts.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Learning and meta-learning of stochastic advection–diffusion–reaction systems from sparse measurements

Physics-informed neural networks (PINNs) were recently proposed in [18] as an alternative way to solve partial differential equations (PDEs). A neural network (NN) represents the solution, while a PDE-induced NN is coupled to the solution NN, and all differential operators are treated using automatic differentiation. Here, we first employ the standard PINN and a stochastic version, sPINN, to solve forward and inverse problems governed by a non-linear advection–diffusion–reaction (ADR) equation, assuming we have some sparse measurements of the concentration field at random or pre-selected locations. Subsequently, we attempt to optimise the hyper-parameters of sPINN by using the Bayesian optimisation method (meta-learning) and compare the results with the empirically selected hyper-parameters of sPINN. In particular, for the first part in solving the inverse deterministic ADR, we assume that we only have a few high-fidelity measurements, whereas the rest of the data is of lower fidelity. Hence, the PINN is trained using a composite multi-fidelity network, first introduced in [12], that learns the correlations between the multi-fidelity data and predicts the unknown values of diffusivity, transport velocity and two reaction constants as well as the concentration field. For the stochastic ADR, we employ a Karhunen–Loève (KL) expansion to represent the stochastic diffusivity, and arbitrary polynomial chaos (aPC) to represent the stochastic solution. Correspondingly, we design multiple NNs to represent the mean of the solution and learn each aPC mode separately, whereas we employ a separate NN to represent the mean of diffusivity and another NN to learn all modes of the KL expansion. For the inverse problem, in addition to stochastic diffusivity and concentration fields, we also aim to obtain the (unknown) deterministic values of transport velocity and reaction constants. The available data correspond to 7spatial points for the diffusivity and 20 space–time points for the solution, both sampled 2000 times. We obtain good accuracy for the deterministic parameters of the order of 1–2% and excellent accuracy for the mean and variance of the stochastic fields, better than three digits of accuracy. In the second part, we consider the previous stochastic inverse problem, and we use Bayesian optimisation to find five hyper-parameters of sPINN, namely the width, depth and learning rate of two NNs for learning the modes. Here, we obtain much deeper and wider optimal NNs compared to the manual tuning, leading to even better accuracy, i.e., errors less than 1% for the deterministic values, and about an order of magnitude less for the stochastic fields.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The Design and Evaluation of Zero Trust Architecture for Electric Vehicle Charging Infrastructure: EVs @ Scale Series on EV Charging Station Cybersecurity

Implementing a zero trust architecture can significantly bolster the security of electric vehicle (EV) charging infrastructure. EV charging infrastructure includes numerous networked interfaces, each of which can present potential vulnerabilities. When these vulnerabilities are exploited, they can compromise the entire system, leading to severe operational and security risks. Zero trust is a security model that operates on the principle of "never trust, always verify," which helps manage the attack surface and limit the scope of any potential compromises. Fundamentally, this model ensures that no entity, whether inside or outside the network, is trusted by default. The design principles of zero trust include continuous verification, strict deny-by-default access controls, and micro-segmentation. Continuous verification ensures that every request is thoroughly checked, regardless of its origin. Strict access controls enforce the principle of least privilege, allowing users and devices only the minimum necessary access to perform their functions. Micro-segmentation involves dividing the network into smaller, isolated segments to prevent lateral movement in case of a breach. In the context of EV charging infrastructure, zero trust can be implemented through various strategies. For example, multi-factor authentication (MFA) can be required for engineers to access the management interfaces and control systems of charging stations. Real-time monitoring and analysis of network traffic can help detect and respond to anomalies. Systems that do not need to communicate with each other can be micro-segmented to enhance security. All communications should adhere to predefined policies to be permitted. Additionally, encrypting communications can protect sensitive information exchanged between chargers and management systems. This paper presents a zero trust architecture specifically designed for EV charging infrastructure. Implementing zero trust not only mitigates risks but also builds a resilient infrastructure capable of withstanding and quickly recovering from cyber threats. The architecture addresses six defined security objectives. A comprehensive test plan is developed to assess the architecture against these objectives, and the results of the evaluation are reported. This approach is essential for maintaining the reliability and integrity of EV charging services in an increasingly interconnected and vulnerable digital landscape. This is the first in a planned series of papers exploring the implementation of zero trust in EV charging infrastructure. Each paper will delve into different aspects and applications of zero trust, highlighting how various work processes and requirements can lead to distinct architectural designs. These architectures will be tailored to address specific security challenges and operational needs within the EV charging ecosystem, ensuring a robust and adaptable security framework.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Enabling Secure and Resilient XFC: A Software/Hardware-Security Co-Design Approach

Extremely fast charging (XFC) has the potential to reduce the charging time of battery electric vehicles (BEV) to be equivalent to the filling time of internal combustion engine vehicles (ICEV), thus eliminating one of the few advantages ICEV still poses for light- and heavy-duty vehicles. Enabling XFC will, however, require coordination and cooperation between the grid, charging stations, and the vehicles themselves, which leads to an inevitable increase in the attack surface for all systems combined. In securing the overall system, we must not only embrace traditional cybersecurity, which is chiefly concerned with communications and the operation of digital systems, but also cyber-physical systems security as the proper operation of XFC is critically dependent on systems’ abilities to know about (sense) and interact with (actuate) the physical world. The project team consists of academic and industry researchers with backgrounds in cybersecurity, cyber-physical systems security, learning in adversarial environments, transportation security, grid security and resilience, wireless power transfer, converter design, and battery management systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

ASPCAP: THE APOGEE STELLAR PARAMETER AND CHEMICAL ABUNDANCES PIPELINE

The Apache Point Observatory Galactic Evolution Experiment (APOGEE) has built the largest moderately high-resolution ( R ≈ 22,500) spectroscopic map of the stars across the Milky Way, and including dust-obscured areas. The APOGEE Stellar Parameter and Chemical Abundances Pipeline (ASPCAP) is the software developed for the automated analysis of these spectra. ASPCAP determines atmospheric parameters and chemical abundances from observed spectra by comparing observed spectra to libraries of theoretical spectra, using χ {sup 2} minimization in a multidimensional parameter space. The package consists of a fortran90 code that does the actual minimization and a wrapper IDL code for book-keeping and data handling. This paper explains in detail the ASPCAP components and functionality, and presents results from a number of tests designed to check its performance. ASPCAP provides stellar effective temperatures, surface gravities, and metallicities precise to 2%, 0.1 dex, and 0.05 dex, respectively, for most APOGEE stars, which are predominantly giants. It also provides abundances for up to 15 chemical elements with various levels of precision, typically under 0.1 dex. The final data release (DR12) of the Sloan Digital Sky Survey III contains an APOGEE database of more than 150,000 stars. ASPCAP development continues in the SDSS-IV APOGEE-2 survey.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants (4th Annual Report)

Nuclear plant sites collect and store large volumes of data collected from various equipment and systems. These datasets typically include plant process parameters, maintenance records, technical logs, online monitoring data, and equipment failure data. The collection of such data affords an opportunity to leverage data-driven machine learning and artificial intelligence technologies to provide diagnostic and prognostic capabilities within the nuclear power industry to reduce operating and maintenance costs. In this way, nuclear energy can become more economically competitive with other energy sources, and premature closures can be avoided. From a maintenance standpoint, savings can be achieved by leveraging machine learning and artificial intelligence technologies to develop data-driven algorithms to better diagnose and predict potential faults within the system. Improved model accuracy can lead to reductions in unnecessary maintenance and more efficient planning of future maintenance, thus lowering the costs associated with parts, labor, and unnecessary planned, forced, or extended outages. From an operations perspective, cost savings can be generated by shifting from route-based monitoring to wireless technologies for online monitoring, and by transitioning from onsite- to cloud-based computing and storage services. Wireless monitoring would reduce the operator manhours required for taking routine measurements, while cloud computing services would generate cost savings by reducing the amount of hardware needing to be purchased and maintained—all while scaling to both computational and storage demands. This report summarizes this project’s effort to shift from costly, labor-intensive preventative maintenance to cheaper predictive maintenance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Random On-Board Pixel Sampling (ROPS) X-Ray Camera

Recent advances in compressed sensing theory and algorithms offer new possibilities for high-speed X-ray camera design. In many CMOS cameras, each pixel has an independent on-board circuit that includes an amplifier, noise rejection, signal shaper, an analog-to-digital converter (ADC), and optional in-pixel storage. When X-ray images are sparse, i.e., when one of the following cases is true: (a.) The number of pixels with true X-ray hits is much smaller than the total number of pixels; (b.) The X-ray information is redundant; or (c.) Some prior knowledge about the X-ray images exists, sparse sampling may be allowed. Here we first illustrate the feasibility of random on-board pixel sampling (ROPS) using an existing set of X-ray images, followed by a discussion about signal to noise as a function of pixel size. Next, we describe a possible circuit architecture to achieve random pixel access and in-pixel storage. The combination of a multilayer architecture, sparse on-chip sampling, and computational image techniques, is expected to facilitate the development and applications of high-speed X-ray camera technology.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

A Digital Twin Framework Utilizing Machine Learning for Robust Predictive Maintenance: Enhancing Tire Health Monitoring

We introduce a novel digital twin (DT) framework for the predictive maintenance of long-term physical systems. Using monitoring tire health as an application, we show how the DT framework can be used to enhance automotive safety and efficiency, and how the technical challenges can be overcome using a three-step approach. First, to manage the data complexity over a long operation span, we employ data reduction techniques to concisely represent physical tires using historical performance and usage data. Relying on these data, for fast real-time prediction, we train a transformer-based model offline on our concise dataset to predict future tire health over time, represented as remaining casing potential (RCP). Based on our architecture, our model quantifies both epistemic and aleatoric uncertainties, providing reliable confidence intervals around predicted RCP. Second, to incorporate real-time data, we update the predictive model in the DT framework, ensuring its accuracy throughout its lifespan with the aid of hybrid modeling and the use of the discrepancy function. Third, to assist decision-making in predictive maintenance, we implement a tire state decision algorithm, which strategically determines the optimal timing for tire replacement based on RCP forecasted by our transformer model. This approach ensures that our DT accurately predicts system health, continually refines its digital representation, and supports predictive maintenance decisions. Furthermore, our framework effectively embodies a physical system, leveraging big data and machine learning (ML) for predictive maintenance, model updates, and decision-making.

advanced computing infrastructure↗
DOE OSTI2016

SU-F-J-129: Verification of Geometric and Dosimetric Accuracy of Respiratory Management Systems Using Homemade Phantom

Purpose: Different placements of Infrared Cameras (IRC) in CT and treatment rooms can cause gating window level (GWL) variations leading to differences between GWL used for planning and treatments. Although, Varian Clinac DHX-OBI sytem and CT are equipped with the same kind of IRC, Truebeam STx (TB) has a different type of IRC known as banana type. In this study; geometric and dosimetric accuracy of respiratory management system (RPM) for different machines were investigated with a special homemade phantom. Methods: Special phantom was placed on the respiratory simulator machine and a CT data set was obtained at the end of the expirium phase (EOE). Conformal and IMRT plans were generated on the EOE CT image series for both DHX-OBI and TB LINACs while a VMAT plan was generated only for TB.The acquired respiratory graphs in the CT were directly sent to DHX-OBI system, and they were converted with software before sending to TB. EBT3 films were placed inside the phantom and were irradiated using RPM system with two machines for different plans. Planar dose distributions were compared with gamma analysis (GA) method (3mm, %3) to evaluate planned-measured dose differences. In addition, radio-opac marker was placed in the center of the phantom to evaluate the geometric accuracy of treatment field with gated flouroscopy (GF). Results: There were no shifts detected between planning and treeatment GWL for both DHX-OBI and TB. Difference on the GF image between digital graticule and radio-opac marker was <1mm for TB and 1mm for DHX-OBI. Although, GA agreement was 97% for conformal and IMRT techniques in TB, it was 96% for VMAT technique. While GA agreement was 98% for conformal technique in DHX-OBI, IMRT was 95%.ConclusionThis study showed that RPM can be used accurately in spite of different IRC placements or different types of ICR used.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-CD-207-03: Time Evolution of Texture Parameters of Subtracted Images Obtained by Contrast-Enhanced Digital Mammography (CEDM)

Purpose: To evaluate the time evolution of texture parameters, based on the gray level co-occurrence matrix (GLCM), in subtracted images of 17 patients (10 malignant and 7 benign) subjected to contrast-enhanced digital mammography (CEDM). The goal is to determine the sensitivity of texture to iodine uptake at the lesion, and its correlation (or lack of) with mean-pixel-value (MPV). Methods: Acquisition of clinical images followed a single-energy CEDM protocol using Rh/Rh/48 kV plus external 0.5 cm Al from a Senographe DS unit. Prior to the iodine-based contrast medium (CM) administration a mask image was acquired; four CM images were obtained 1, 2, 3, and 5 minutes after CM injection. Temporal series were obtained by logarithmic subtraction of registered CM minus mask images.Regions of interest (ROI) for the lesion were drawn by a radiologist and the texture was analyzed. GLCM was evaluated at a 3 pixel distance, 0° angle, and 64 gray-levels. Pixels identified as registration errors were excluded from the computation. 17 texture parameters were chosen, classified according to similarity into 7 groups, and analyzed. Results: In all cases the texture parameters within a group have similar dynamic behavior. Two texture groups (associated to cluster and sum mean) show a strong correlation with MPV; their average correlation coefficient (ACC) is r{sup 2}=0.90. Other two groups (contrast, homogeneity) remain constant with time, that is, a low-sensitivity to CM uptake. Three groups (regularity, lacunarity and diagonal moment) are sensitive to CM uptake but less correlated with MPV; their ACC is r{sup 2}=0.78. Conclusion: This analysis has shown that, at least groups associated to regularity, lacunarity and diagonal moment offer dynamical information additional to the mean pixel value due to the presence of CM at the lesion. The next step will be the analysis in terms of the lesion pathology. Authors thank PAPIIT-IN105813 for support. Consejo Nacional de Ciencia Y Tecnologia, PAPIIT-IN105813.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Cerebral perfusion computed tomography deconvolution via structure tensor total variation regularization

Purpose: Cerebral perfusion computed tomography (PCT) imaging as an accurate and fast acute ischemic stroke examination has been widely used in clinic. Meanwhile, a major drawback of PCT imaging is the high radiation dose due to its dynamic scan protocol. The purpose of this work is to develop a robust perfusion deconvolution approach via structure tensor total variation (STV) regularization (PD-STV) for estimating an accurate residue function in PCT imaging with the low-milliampere-seconds (low-mAs) data acquisition. Methods: Besides modeling the spatio-temporal structure information of PCT data, the STV regularization of the present PD-STV approach can utilize the higher order derivatives of the residue function to enhance denoising performance. To minimize the objective function, the authors propose an effective iterative algorithm with a shrinkage/thresholding scheme. A simulation study on a digital brain perfusion phantom and a clinical study on an old infarction patient were conducted to validate and evaluate the performance of the present PD-STV approach. Results: In the digital phantom study, visual inspection and quantitative metrics (i.e., the normalized mean square error, the peak signal-to-noise ratio, and the universal quality index) assessments demonstrated that the PD-STV approach outperformed other existing approaches in terms of the performance of noise-induced artifacts reduction and accurate perfusion hemodynamic maps (PHM) estimation. In the patient data study, the present PD-STV approach could yield accurate PHM estimation with several noticeable gains over other existing approaches in terms of visual inspection and correlation analysis. Conclusions: This study demonstrated the feasibility and efficacy of the present PD-STV approach in utilizing STV regularization to improve the accuracy of residue function estimation of cerebral PCT imaging in the case of low-mAs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

A compressed sensing X-ray camera with a multilayer architecture

Recent advances in compressed sensing theory and algorithms offer new possibilities for high-speed X-ray camera design. In many CMOS cameras, each pixel has an independent on-board circuit that includes an amplifier, noise rejection, signal shaper, an analog-to-digital converter (ADC), and optional in-pixel storage. When X-ray images are sparse, i.e., when one of the following cases is true: (a.) The number of pixels with true X-ray hits is much smaller than the total number of pixels; (b.) The X-ray information is redundant; or (c.) Some prior knowledge about the X-ray images exists, sparse sampling may be allowed. In this work, we first illustrate the feasibility of random on-board pixel sampling (ROPS) using an existing set of X-ray images, followed by a discussion about signal to noise as a function of pixel size. Next, we describe a possible circuit architecture to achieve random pixel access and in-pixel storage. The combination of a multilayer architecture, sparse on-chip sampling, and computational image techniques, is expected to facilitate the development and applications of high-speed X-ray camera technology.

47 OTHER INSTRUMENTATION↗