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At least 1,081 records · Page 60

Laser Additive Manufacturing of F/M Steels for Radiation Tolerant Nuclear Components

According to the Nuclear Energy R&D Roadmap Report submitted to Congress in 2010, one the key challenges facing the nuclear energy industry involves development of new reactor designs with reduced capital costs. Two related R&D objectives outlined in the report include: 1) Making improvements in the affordability of new reactors; and 2) Development of structural materials to withstand irradiation for longer periods. Laser additive manufacturing (LAM) is particularly well suited for more rapid and economical fabrication of reactor components relative to current fabrication methods. The proposed work involving LAM directly addresses the two R&D objectives outlined above relevant to the pertinent mission problems. The classical Materials Science approach involving development of Process/Structure/Property/Performance (P/S/P/P) relations was employed in this project. Processing included LAM and heat-treating. Thermal cycling during LAM is discussed here, and phase diagrams and continuous cooling transformation (CCT) diagrams are used to rationalize microstructural evolution. Structures were characterized including grain size & morphology, volume fraction, morphology, composition and location of carbides in as-deposited and heat-treated conditions. In the simplest sense, the goal was to control microstructures through process manipulation with a view toward optimizing properties and performance in service.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Evaluation of DED and LPBF Fe-based Alloys Process Application Envelopes based on Performance, Process Economics, Supply Chain Risks, and Reactor-specific Targeted Components

The U.S. Department of Energy (DOE), Office of Nuclear Energy (NE), Advanced Materials and Manufacturing Technologies (AMMT) program aims to develop extreme-environment materials solutions for use in the deployment of advanced nuclear reactors and the sustainment of the current fleet. To achieve this objective, a combination of experiment, a computational tool, and machine learning (ML) for the design of materials is adopted for the maturation of materials for nuclear technology. Through advanced manufacturing techniques such as laser powder bed fusion (LPBF) and laser powder direct energy deposition (LP-DED), components with complex geometries can be fabricated with reduced time and effort. Such advanced manufacturing methods can also provide the opportunity to improve materials performance through optimized microstructures and mechanical properties. However, existing engineering alloys are not always well suited for fabrication with additive manufacturing (AM), as their compositions have been tuned to optimize fabrication via conventional methods. Thus, similar alloys with modified compositions that are better suited for AM can be studied for improved performance. Over the past three years, the AMMT teams from Argonne National Laboratory (ANL) and Pacific Northwest National Laboratory (PNNL) studied various known Fe-based alloys by evaluating their initial printability using LPBF, and an AMMT-developed down-selection and decision matrix reduced the number of alloys to be studied from six to three in fiscal year (FY) 2024. Additionally, in FY 2024, for parallel evaluation, these three alloys were studied using LPDED. While LPBF is better for small- to medium-sized components with high detail and internal features, LP-DED combines a material feed system to place the powder onto the exact spot where the laser will melt the material. This AM method can be easily scaled to extremely large components and provides high build rate speeds compared to those of conventional LPBF systems. Additionally, DED is a better choice for complex geometries and compositional gradients.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

A Review on Binder Jet Additive Manufacturing of 316L Stainless Steel

Binder jet additive manufacturing enables the production of complex components for numerous applications. Binder jetting is the only powder bed additive manufacturing process that is not fusion-based, thus manufactured parts have no residual stresses as opposed to laser-based additive manufacturing processes. Binder jet technology can be adopted for the production of various small and large metallic parts for specific applications, including in the biomedical and energy sectors, at a lower cost and shorter lead time. One of the most well-known types of stainless steels for various industries is 316L, which has been extensively manufactured using binder jet technology. Binder jet manufactured 316L parts have obtained near full density and, in some cases, similar mechanical properties compared to conventionally manufactured parts. This article introduces methods, principles, and applications of binder jetting of SS 316L. Details of binder jetting processes, including powder characteristics (shape and size), binder properties (binder chemistry and droplet formation mechanism), printing process parameters (such as layer thickness, binder saturation, drying time), and post-processing sintering mechanism and densification processes, are carefully reviewed. Furthermore, critical factors in the selection of feedstock, printing parameters, sintering temperature, time, atmosphere, and heating rate of 316L binder jet manufactured parts are highlighted and summarized. Finally, the above-mentioned processing parameters are correlated with final density and mechanical properties of 316L components to establish a guideline on feedstock selection and process parameters optimization to achieve desired density, structure and properties for various applications.

36 MATERIALS SCIENCE↗

SRM-Assisted Trajectory for the GTX Reference Vehicle

A goal of the GTX effort has been to demonstrate the feasibility of a single stage- to- orbit (SSTO) vehicle that delivers a small payload to low earth orbit. The small payload class was chosen in order to minimize the risk and cost of development of this revolutionary system. A preliminary design study by the GTX team has resulted in the current configuration that offers considerable promise for meeting the stated goal. The size and gross lift-off weight resulting from scaling the current design to closure however may be considered impractical for the small payload. In lieu of evolving the project's reference vehicle to a large-payload class, this paper offers the alternative of using solid-rocket motors in order to close the vehicle at a practical scale. This approach offers a near-term, quasi-reusable system that easily evolves to reusable SSTO following subsequent development and optimization. This paper presents an overview of the impact of the addition of SRM's to the GTX reference vehicle's performance and trajectory. The overall methods of vehicle modeling and trajectory optimization will also be presented. A key element in the trajectory optimization is the use of the program OTIS 3.10 that provides rapid convergence and a great deal of flexibility to the user. This paper will also present the methods used to implement GTX requirements into OTIS modeling.

Riehl, John↗

Accelerating Multivariate Functional Approximation Computation with Domain Decomposition Techniques⋆

Modeling large datasets through Multivariate Functional Approximations (MFA) provide an elegant way to handle many visualization and scientific analysis workflows. The process necessitates scalable data partitioning methods to compute MFA representations efficiently without compromising the accuracy or continuity of the reconstructed solution. We propose a domain -decomposed method for computing the MFA with B -spline bases, which reduces the total work per task and uses a restricted Additive Schwarz (RAS) method to converge the control point data degrees -of -freedom along subdomain boundaries. We provide an in-depth analysis of the parallel approach with domain decomposition solvers, aiming to minimize local subdomain error residuals and recover high -order continuity at subdomain interfaces with appropriate choices of knot overlaps. The communication cost, determined by the overlap regions in the RAS implementation, is optimized to recover the numerical error profile of the single subdomain case. Our proposed method stands in contrast to previous methods, which typically only recover either C 0 or at best C 1 continuity for arbitrary B -spline degree expansions, or those that require post -processing to blend discontinuities in the reconstructed data. We demonstrate the effectiveness of our approach using analytical and real -world datasets in 1D, 2D, and 3D through both strong and weak scaling studies. The performance results indicate that the overall cost of computing the approximation is directly proportional to the underlying nearest -neighbor communication implementation, and is only weakly dependent on the overlap region size that determines the size of the messages. This finding underscores the efficiency and scalability of our proposed method, making it a promising solution for handling large datasets in scientific workflows.

additive Schwarz solvers↗

Automated Performance Prediction of Message-Passing Parallel Programs

The increasing use of massively parallel supercomputers to solve large-scale scientific problems has generated a need for tools that can predict scalability trends of applications written for these machines. Much work has been done to create simple models that represent important characteristics of parallel programs, such as latency, network contention, and communication volume. But many of these methods still require substantial manual effort to represent an application in the model's format. The NIK toolkit described in this paper is the result of an on-going effort to automate the formation of analytic expressions of program execution time, with a minimum of programmer assistance. In this paper we demonstrate the feasibility of our approach, by extending previous work to detect and model communication patterns automatically, with and without overlapped computations. The predictions derived from these models agree, within reasonable limits, with execution times of programs measured on the Intel iPSC/860 and Paragon. Further, we demonstrate the use of MK in selecting optimal computational grain size and studying various scalability metrics.

Block, Robert J.↗

Sizing ramping reserve using probabilistic solar forecasts: A data-driven method

Ramping products have been introduced or proposed in several U.S. power markets to mitigate the impact of load and renewable uncertainties on market efficiency and reliability. Current methods often rely on historical data to estimate the requirements of ramping products and fail to take into account the effects of the latest weather conditions and their uncertainties, which could lead to overly conservative or insufficient requirements. This study proposes a k-nearest-neighbor-based method to give weather-informed estimates of ramping needs based on short-term probabilistic solar irradiance forecasts. Forecasts from multiple sites are employed in conjunction with principal component analysis to derive numerical classifiers to characterize system-level weather conditions. In addition, we develop a data-driven method to optimize the model parameters in a rolling-forward manner. By using real-world data from the California Independent System Operator, we design two metrics to evaluate method performance: 1) frequency of shortage and 2) oversupply of ramping product. Our proposed method presents advantages in comparison with the baseline and a set of benchmark methods: without compromising system reliability, it reduces system ramping requirements by up to 25%, therefore improving both system reliability and economics.

14 SOLAR ENERGY↗

Investigation on zinc substituted highly porous improved catalytic activity of NiFe2O4 nanocrystal by co-precipitation method

Highlights: • A thriftily viable co-precipitation technique has been used to synthesize Zn substituted NiFe{sub 2}O{sub 4} nanoparticles. • An essential structural, optical and magnetic property has been investigated. • Augmentation of Photo-Fenton activity after increasing the zinc concentration has been observed and deliberated. • Strong dependence of photo-fenton activity and cumulative increase degradation efficiency. - Abstract: Connecting to various applications of nickel ferrite as a ceramic magnetic nanocomposite in diverse manufacturing and technical categories, it is essential to optimize its optical, magnetic and microstructural characteristics. Hence a facile route was developed to fabricate nickel zinc ferrite (Ni{sub 1-x}Zn{sub x}Fe{sub 2}O{sub 4}) nanocomposites by co-precipitation route. The enviable spinel phase was studied by X-ray diffractometer and the crystallite size was found to vary from 8 to 15 nm. By Transmission Electron Microscopy, the micro structural and speck distribution was found in the range of 5–12.5 nm. Surface characterization was done using the isothermal BET technique which presents a high surface area that is exhibited by synthesized nanoparticles. Using Kubelka-Munk plot, bandgap was found to be decreasing with the increase of Zn concentration.The synthesized Ni{sub 1-x}Zn{sub x}Fe{sub 2}O{sub 4} was studied for its application in photo-Fenton degradation of organic Methylene blue (MB) dye. The synthesized nanocatalyst can bereused further which makes itself a proficient candidate in industrial effluent degradation.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Can classical DEM simultaneously capture compressibility and flowability of milled biomass?

Accurate prediction of the rheological behavior of biomass is essential for the design and operation of hoppers, feeders, and storage systems in biorefineries. This study examines whether the classical, coarse-grained discrete element method (DEM) formulation can simultaneously reproduce the compressibility and flowability of milled herbaceous biomass, using Miscanthus × giganteus as a representative material. The model represents particles as rigid spheres interacting through Hertz-Mindlin elastic-frictional contacts augmented with an area-dependent cohesion term. Laboratory cyclic compression and wedge-shaped hopper discharge experiments were used as calibration benchmarks. Although the model can independently reproduce each behavior by appropriately tuning particle Young's modulus E and cohesion energy density k, an extensive parametric investigation comprising more than 600 simulations reveals that the optimal parameter regions for compression and hopper flow are distinct and non-overlapping in (E, k) space. Surrogate surface analysis further shows that the corresponding objective-function valleys exhibit similar trends but are approximately parallel and spatially offset, precluding a unified calibration within the explored domain. Sensitivity analysis indicates that compressibility is governed predominantly by stiffness and cohesion, whereas the slope of the mass flow rate-opening relation in hopper discharge is primarily controlled by tangential friction. Extensions incorporating particle size distribution and clumped-sphere representations do not eliminate the incompatibility. These results systematically reveal, for the first time, the structural limitation of simplified DEM formulations in representing biomass rheological behavior, underscoring the necessity for models incorporating additional physical mechanisms, such as particle deformability or enhanced interlocking, to achieve unified predictive capability for biomass handling behavior.

09 BIOMASS FUELS↗

Adaptive mesh refinement in stress-constrained topology optimization

We present a topology structural optimization framework with adaptive mesh refinement and stress-constraints. Finite element approximation and geometry representation benefit from such refinement by enabling more accurate stress field predictions and greater resolution of the optimal structural boundaries. We combine a volume fraction filter to impose a minimum design feature size, the RAMP penalization to generate “black-and-white designs” and a RAMP-like stress definition to resolve the “stress singularity problem.” Regions with stress concentrations dominate the optimized design. As such, rigorous simulations are required to accurately approximate the stress field. To achieve this goal, we invoke a threshold operation and mesh refinement during the optimization. We do so in an optimal fashion, by applying adaptive mesh refinement techniques that use error indicators to refine and coarsen the mesh as needed. In this way, we obtain more accurate simulations and greater resolution of the design domain. We present results in two dimensions to demonstrate the efficiency of our method.

97 MATHEMATICS AND COMPUTING↗

Comprehensive defect evaluation of advanced nuclear fuels using high-resolution acoustic signals and optimized sensor separation

Graphite pebble composite structures based on TRistructural-ISOtropic (TRISO) particles are being developed as core nuclear fuels in advanced power reactors, promising safe operation at increased temperatures. Ensuring the structural integrity of these nuclear fuels requires comprehensive and accurate non-destructive evaluation (NDE) techniques to characterize defects and damage in the pebbles. However, traditional acoustic evaluation methods face limitations in defect characterization due to the highly attenuative, and geometrically and compositionally complex nature of these structures. This study proposes an improved acoustic NDE technique for accurate detection and classification of anticipated relevant defects and damage in graphite pebbles using high-resolution acoustic signals and optimized transmit-receive sensor networks. The proposed approach utilizes a triangular three-sensor network as the base unit, comprising three transmit-receive sensors. The sensor separation distance, as well as acoustic excitation center frequency, pulse-width, and bandwidth are optimized to enhance spatial resolution and improve signal-to-noise ratio, enabling effective characterization of the smallest size and widest range of defects in pebbles. Furthermore, the use of the triangular sensor configuration instead of a more conventional transmit-receive sensor pair expands the inspection region from a one-dimensional linear path to a two-dimensional area, increasing spatial coverage. To mitigate challenges associated with processing of complex acoustic signals arising from high-frequency, high-bandwidth excitation in these structures, a machine-learning-based signal processing algorithm is integrated with the sensor network. In the machine-learning-based algorithm, multi-domain features are extracted from the acoustic signals to capture intricate signal characteristics, significantly improving defect identification and classification compared to traditional approaches. The proposed acoustic NDE technique offers considerable promise for practical and reliable defect/damage diagnostics of advanced nuclear pebble fuels.

42 ENGINEERING↗

Ultrathin Pt x Sn 1–x Nanowires for Methanol and Ethanol Oxidation Reactions: Tuning Performance by Varying Chemical Composition

Pt-based alloys denote promising catalysts for the methanol oxidation reaction (MOR) and the ethanol oxidation reaction (EOR), due to their enhanced activity toward alcohol-oxidation reactions and reduced cost as compared with Pt alone. Among all of these binary systems, PtSn has been reported to exhibit superior methanol/ethanol oxidation activity. In this paper, we deliberatively tailor chemical composition, reduce size, and optimize morphology of the catalyst in an effort to understand structure–property correlations that can be used to improve upon the electrocatalytic activity of these systems. Previous work performed by our group suggested that Pt-based catalysts, possessing an ultrathin one-dimensional (1D) structure, dramatically promote both cathodic and anodic reactions with respect to their zero-dimensional (0D) counterparts. Herein, a novel set of ultrathin binary Pt–Sn 1D nanowire (NW) catalysts with rationally controlled chemical compositions, i.e., Pt 9 Sn 1 , Pt 8 Sn 2 , and Pt 7 Sn 3 , has been synthesized using a facile, room-temperature, wet-solution-based method. The crystallinity and chemical composition of these as-prepared samples were initially characterized using XRD, XPS, and EDX. Results revealed that this synthetic protocol could successfully generate PtSn alloys with purposely tunable chemical compositions. TEM and HRTEM verified the structural integrity of our ultrathin 1D NW morphology for our Pt 9 Sn 1 , Pt 8 Sn 2 , and Pt 7 Sn 3 samples. The effects of varying Sn content within these alloy samples toward the electro-oxidation reaction of methanol and ethanol were probed using cyclic voltammetry (CV) in acidic media. Finally, within this series, we find that the optimized chemical composition for both the MOR and the EOR is Pt 7 Sn 3 .

36 MATERIALS SCIENCE↗

Grain boundary engineering for efficient and durable electrocatalysis

Grain boundaries in noble metal catalysts have been identified as critical sites for enhancing catalytic activity in electrochemical reactions such as the oxygen reduction reaction. However, conventional methods to modify grain boundary density often alter particle size, shape, and morphology, obscuring the specific role of grain boundaries in catalytic performance. This study addresses these challenges by employing gold nanoparticle assemblies to control grain boundary density through the manipulation of nanoparticle collision frequency during synthesis. We demonstrate a direct correlation between increased grain boundary density and enhanced two-electron oxygen reduction reaction activity, achieving a significant improvement in both specific and mass activity. Additionally, the gold nanoparticle assemblies with high grain boundary density exhibit remarkable electrochemical stability, attributed to boron segregation at the grain boundaries, which prevents structural degradation. This work provides a promising strategy for optimizing the activity, selectivity, and stability of noble metal catalysts through precise grain boundary engineering.

36 MATERIALS SCIENCE↗

Accelerating flux balance calculations in genome-scale metabolic models by localizing the application of loopless constraints

Abstract Background Genome-scale metabolic network models and constraint-based modeling techniques have become important tools for analyzing cellular metabolism. Thermodynamically infeasible cycles (TICs) causing unbounded metabolic flux ranges are often encountered. TICs satisfy the mass balance and directionality constraints but violate the second law of thermodynamics. Current practices involve implementing additional constraints to ensure not only optimal but also loopless flux distributions. However, the mixed integer linear programming problems required to solve become computationally intractable for genome-scale metabolic models. Results We aimed to identify the fewest needed constraints sufficient for optimality under the loopless requirement. We found that loopless constraints are required only for the reactions that share elementary flux modes representing TICs with reactions that are part of the objective function. We put forth the concept of localized loopless constraints (LLCs) to enforce this minimal required set of loopless constraints. By combining with a novel procedure for minimal null-space calculation, the computational time for loopless flux variability analysis (ll-FVA) is reduced by a factor of 10–150 compared to the original loopless constraints and by 4–20 times compared to the current fastest method Fast-SNP with the percent improvement increasing with model size. Importantly, LLCs offer a scalable strategy for loopless flux calculations for multi-compartment/multi-organism models of large sizes, for example, shortening the CPU time for ll-FVA from 35 h to less than 2 h for a model with more than104 reactions. Availability and implementation Matlab functions are available in the Supplementary Material or at https://github.com/maranasgroup/lll-FVA Supplementary information Supplementary data are available at Bioinformatics online.

Chan, Siu H. J. (ORCID:000000027707656X)↗

Validation of RELAP-7 for Forced Convective and Natural Circulation Reactor Flows

New reactor designs such as small modular reactors and newly proposed passive safety systems rely on natural circulation two-phase flow cooling of the reactor core under accident conditions. Without a pump to drive the flow, the circulation rate is a strong function of void fraction. Under these conditions where the flow rate, pressure drop, and heat transfer are all closely coupled through the mixture density, the accurate prediction of void fraction is pivotal. Furthermore, while these passive systems boast a more reliable cooling option, the sensitivity of the system to two-phase flow instabilities is more likely than in forced convective cooling and can result in dangerous density wave or flow excursion conditions. Improvement of one-dimensional system codes and further development of three-dimensional code capability are all important efforts in addressing these issues. The proposed work is a critical step towards providing confidence through validation of the capability of thermal-hydraulic tools to meet the emerging challenges in reactor simulation. Historically, system analysis codes develop a stiffness over time due to fine-tuning of adjustable code parameters that result in a reluctance or inability to adopt improved models. Therefore, a rigorous effort should be invested in preparation for RELAP-7 to identify required modeling improvements of constitutive relations and validation of the code applicability to the wide range of possible reactor flow conditions. Part of this validation involves the uncertainty quantification which is a critical step in the Code Scaling, Applicability, and Uncertainty (CSAU) methodology and Best-Estimate Plus Uncertainty (BEPU) licensing method. BEPU allows recovery of safety margins and potentially optimizing plant performance, resulting in economic benefit for the reactor operators. Beginning with the collection of past experimental data covering prototypic LWR conditions, this document reviews three critical datasets available for the validation of system codes. The experimental methods, procedures, facilities, instrumentation, and supplementary information are consolidated to aid the validation efforts of this work and future projects. These three datasets (824 conditions) are chosen due to their wide acceptance and demonstrated application for code validation. Two experimental datasets from the Nuclear Power Engineering Corporation (NUPEC) span prototypic conditions of Boiling Water Reactors (BWR) and Pressurized Water Reactors (PWR). These datasets are labeled as the BFBT (BWR Full-size Fine-mesh Bundle Test) and the PSBT (PWR Subchannel and Bundle Test) datasets respectively. The third dataset is taken in an internally heated annulus that is scaled based on a BWR subassembly and includes two-phase flows important to post-blowdown analysis. Together, the datasets consider the full range of pressure and mass flux expected in LWR forced convective flows from steady-state to long term cooling conditions. Recognizing the deficiency of available data under low-pressure, low-flow, a new natural circulation dataset consisting of 107 conditions is obtained. With system pressures ranging between 145 and 950 kPa, natural circulation is driven by a heat flux up to 275 kW/m2 in a 3 m long heated region of the annulus test section which is followed by a 2 m long unheated region before entering a condenser. Stable natural circulation flow rates in this facility were measured to be between 182 and 590 kg/m2-s. Pressure and temperature are measured at ten locations in the facility. In addition, measurements of void fraction, interfacial area concentration, and gas velocity are obtained at five axial locations in the annulus test section. The RELAP5-3D code demonstrates good accuracy for void fraction predictions in BFBT and PSBT benchmarks, but shows relatively large discrepancy in the annulus experiments at lower pressures. In two-phase natural circulation, large oscillations are observed in many system parameters during the simulation of 19 experiment cases which were conducted at low pressures, and the model shows good predictive performance with the remaining 88 experiment cases. However, some discrepancies in void fraction and temperature predictions are found which may require improved constitutive modeling under these low-pressure, low-flow condition. The quantified uncertainty in void fraction predictions in three forced convection benchmarks due to uncertainties in boundary conditions and geometries are relatively small, except for a few cases in the annulus experiment. The output uncertainty of the natural circulation model is quantified by considering the measurement error in boundary conditions, and it is shown that the output uncertainty ranges are relatively small for the range of conditions considered. Through this work, a clear pathway is provided for validation of RELAP7 once the code is available.

42 ENGINEERING↗

Coupled Gel/Seeded Growth of Cs2AgBiBr6 Single Crystals for Radiation Detection

Abstract— The all-inorganic lead free Cs2AgBiBr6 perovskite represents a highly promising environment-friendly radiation detector material. The first attempt to grow Cs2AgBiBr6 single crystals using the unique gel growth method is reported in this study. This technique enables controlled reagent diffusion in a gel medium at room temperature, fostering the growth of visible single crystals. It offers advantages over conventional methods, leading to fewer equilibrium and non-equilibrium defects and simplified execution. Interestingly, the as-grown crystals exhibited variations in size and colors within the gel matrix, indicating the influence of Bi solutes in Bi-poor and Bi-rich Cs2AgBiBr6 crystals. Once the crystals were confirmed by powder xray diffraction, a series of comprehensive material characterizations was performed. The resistivity of Cs2AgBiBr6 single crystals was measured at 1.12x107 Ω•cm. Trap density analysis yielded a value of 2.82 x1010 cm-3. The charge carrier mobility was determined to be 32.9 cm2V-1S-1, suggesting the suitability for radiation detector applications. Additional growth optimization is going on and further characterization will be reported during the presentation. Our work helps accelerate the development of lead-free perovskite materials toward radiation detector applications.

James, Ralph B.↗

Frequency optimization of repetitive lattice beam-like structures using a continuum model

A new method for obtaining the maximum frequency design of a beam-like repetitive lattice structure is presented. Using existing techniques, the lattice is first modeled as an equivalent anisotropic Timoshenko beam. The computation of the stiffness and inertial properties of the beam, determined by matching the strain and kinetic energies of the beam with those of the lattice, is facilitated by the repetitive nature of the lattice. The optimum design is obtained by maximizing Rayleigh's quotient using methods of variational calculus. For the problem selected, results show excellent agreement with those obtained by traditional finite-element methods. Moreover, unlike FE methods, cpu time is relatively unaffected by the size of the truss.

Reiss, Robert↗

Normal and abnormal tissue identification system and method for medical images such as digital mammograms

A system and method for analyzing a medical image to determine whether an abnormality is present, for example, in digital mammograms, includes the application of a wavelet expansion to a raw image to obtain subspace images of varying resolution. At least one subspace image is selected that has a resolution commensurate with a desired predetermined detection resolution range. A functional form of a probability distribution function is determined for each selected subspace image, and an optimal statistical normal image region test is determined for each selected subspace image. A threshold level for the probability distribution function is established from the optimal statistical normal image region test for each selected subspace image. A region size comprising at least one sector is defined, and an output image is created that includes a combination of all regions for each selected subspace image. Each region has a first value when the region intensity level is above the threshold and a second value when the region intensity level is below the threshold. This permits the localization of a potential abnormality within the image.

Heine, John J.↗