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At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

42 ENGINEERING↗
DOE OSTI2020

Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers

Backdoor data poisoning attacks have recently been demonstrated in computer vision research as a potential safety risk for machine learning (ML) systems. Traditional data poisoning attacks manipulate training data to induce unreliability of an ML model, whereas backdoor data poisoning attacks maintain system performance unless the MLmodel is presented with an input containing an embedded“trigger” that provides a predetermined response advantageous to the adversary. Our work builds upon prior back-door data-poisoning research for ML image classifiers and systematically assesses different experimental conditions including types of trigger patterns, persistence of trigger patterns during retraining, poisoning strategies, architectures (ResNet-50, NasNet, NasNet-Mobile), datasets (Flowers, CIFAR-10), and potential defensive regularization techniques (Contrastive Loss, Logit Squeezing, Manifold Mixup,Soft-Nearest-Neighbors Loss). Experiments yield four key findings. First, the success rate of backdoor poisoning at-tacks varies widely, depending on several factors, including model architecture, trigger pattern and regularization technique. Second, we find that poisoned models are hard to detect through performance inspection alone. Third, regularization typically reduces backdoor success rate, although it can have no effect or even slightly increase it, depending on the form of regularization. Finally, backdoors inserted through data poisoning can be rendered ineffective after just a few epochs of additional training on a small set of clean data without affecting the model’s performance.

Truong, Loc T.↗
DOE OSTI2021

A Novel Fiber-Bragg-Grating-Based Unique Identifier for Use in Tags and Seals

Tags and seals are important tools used in safeguards regimes for the control of nuclear materials. Vital to a successful tag or seal is the ability to securely, uniquely, and positively distinguish it from others of the same kind. This allows one to validate that items entered into an inspection regime to which the tag or seal is affixed are as claimed. We are developing a new type of unique identifier (UID) for such applications that is based on fiber-Bragg grating (FBG) technology. The index of refraction of the core glass of a fiber optic can be locally and permanently altered if sensitized fibers are exposed to intense ultraviolet light. This can be used to create index modulations with a regular periodicity comparable to the wavelengths of light with which the fiber is used. If broadband illumination is sent down the fiber, any light that is in resonance with the modulations will be reflected back to the input end of the fiber where it can be detected. The shape of the returned spectrum can be set by the modulation pattern written into the fiber’s core. We take advantage of these properties to create a compact UID that contains several FBGs potted into a monolithic body. By connecting the UID to an optical interrogator, the complex spectral return can be uniquely identified with a short measurement, providing a robust identification. As a device based on narrowband spectral returns, FBGs are inherently radiation tolerant, making them ideal for use with nuclear materials. Further, they can be read out remotely via a long fiber optic or locally via brief connection to a portable optical interrogator. Because the spectral signature of FBGs is sensitive to external mechanical perturbations, we are exploring the ability to reversibly alter the signature obtained from the UID by applying controlled mechanical forces with a simple device. Such a “key” will allow different stakeholders to obtain their own unique signature from the device, providing a new level of surety to safeguards inspections.

Ziock, Klaus-Peter↗
DOE OSTI2023

Coded-mask-based X-ray phase-contrast and dark-field imaging

Phase contrast and dark-field X-ray imaging enable imaging of objects that absorb or reflect very little X-ray light. Disclosed is a method and systems for performing coded-mask-based multi-contrast imaging (CMMI). The method includes providing radiation to a coded mask that has a known phase and absorption profile according to a pre-determined pattern. The radiation is then impingent upon a sample, and the radiation is detected to perform phase-reconstruction and image processing. The method and associated systems allow for the use of maximum-likelihood and machine learning methods for reconstruction images of the sample from the detected radiation.

Shi, Xianbo↗
DOE OSTI2016

Iterative Strategies for Aftershock Classification in Automatic Seismic Processing Pipelines

We report aftershock sequences following very large earthquakes present enormous challenges to near-real-time generation of seismic bulletins. The increase in analyst resources needed to relocate an inflated number of events is compounded by failures of phase-association algorithms and a significant deterioration in the quality of underlying, fully automatic event bulletins. Current processing pipelines were designed a generation ago, and, due to computational limitations of the time, are usually limited to single passes over the raw data. With current processing capability, multiple passes over the data are feasible. Processing the raw data at each station currently generates parametric data streams that are then scanned by a phase-association algorithm to form event hypotheses. We consider the scenario in which a large earthquake has occurred and propose to define a region of likely aftershock activity in which events are detected and accurately located, using a separate specially targeted semiautomatic process. This effort may focus on so-called pattern detectors, but here we demonstrate a more general grid-search algorithm that may cover wider source regions without requiring waveform similarity. Given many well-located aftershocks within our source region, we may remove all associated phases from the original detection lists prior to a new iteration of the phase-association algorithm. We provide a proof-of-concept example for the 2015 Gorkha sequence, Nepal, recorded on seismic arrays of the International Monitoring System. Even with very conservative conditions for defining event hypotheses within the aftershock source region, we can automatically remove about half of the original detections that could have been generated by Nepal earthquakes and reduce the likelihood of false associations and spurious event hypotheses. Lastly, further reductions in the number of detections in the parametric data streams are likely, using correlation and subspace detectors and/or empirical matched field processing.

58 GEOSCIENCES↗
DOE OSTI2018

Explosion-Generated Infrasound Recorded on Ground and Airborne Microbarometers at Regional Distances

Recent work in deploying infrasound (low–frequency sound) sensors on aerostats and free–flying balloons has shown them to be viable alternatives to ground stations. However, no study to date has compared the performance of surface and free–floating infrasound microbarometers with respect to acoustic events at regional (100s of kilometers) range. The prospect of enhanced detection of aerial explosions at similar ranges, such as those from bolides, has not been investigated either. We examined infrasound signals from three 1–ton trinitrotoluene (TNT) equivalent chemical explosions using microbarometers on two separate balloons at 280– to 400–km ranges and ground stations at 6.3– to 350–km ranges. Signal celerities were consistent with acoustic waves traveling in the stratospheric duct. However, significant differences were noted between the observed arrival patterns and those predicted by an acoustic propagation model. Very low–background noise levels on the balloons were consistent with previous studies that suggest wind interference is minimal on freely drifting sensors. In conclusion, simulated propagation patterns and observed noise levels also confirm that balloon–borne microbarometers should be very effective at detecting explosions in the middle and upper atmosphere as well as those on the surface.

58 GEOSCIENCES↗
DOE OSTI2015

Nitrogen-modified nano-titania: True phase composition, microstructure and visible-light induced photocatalytic NO{sub x} abatement

Titanium dioxide (TiO{sub 2}) is a popular photocatalyst used for many environmental and anti-pollution applications, but it normally operates under UV light, exploiting ∼5% of the solar spectrum. Nitrification of titania to form N-doped TiO{sub 2} has been explored as a way to increase its photocatalytic activity under visible light, and anionic doping is a promising method to enable TiO{sub 2} to harvest visible-light by changing its photo-absorption properties. In this paper, we explore the insertion of nitrogen into the TiO{sub 2} lattice using our green sol–gel nanosynthesis method, used to create 10 nm TiO{sub 2} NPs. Two parallel routes were studied to produce nitrogen-modified TiO{sub 2} nanoparticles (NPs), using HNO{sub 3}+NH{sub 3} (acid-precipitated base-peptised) and NH{sub 4}OH (totally base catalysed) as nitrogen sources. These NPs were thermally treated between 450 and 800 °C. Their true phase composition (crystalline and amorphous phases), as well as their micro-/nanostructure (crystalline domain shape, size and size distribution, edge and screw dislocation density) was fully characterised through advanced X-ray methods (Rietveld-reference intensity ratio, RIR, and whole powder pattern modelling, WPPM). As pollutants, nitrogen oxides (NO{sub x}) are of particular concern for human health, so the photocatalytic activity of the NPs was assessed by monitoring NO{sub x} abatement, using both solar and white-light (indoor artificial lighting), simulating outdoor and indoor environments, respectively. Results showed that the onset of the anatase-to-rutile phase transformation (ART) occurred at temperatures above 450 °C, and NPs heated to 450 °C possessed excellent photocatalytic activity (PCA) under visible white-light (indoor artificial lighting), with a PCA double than that of the standard P25 TiO{sub 2} NPs. However, higher thermal treatment temperatures were found to be detrimental for visible-light photocatalytic activity, due to the effects of four simultaneous occurrences: (i) loss of OH groups and water adsorbed on the photocatalyst surface; (ii) growth of crystalline domain sizes with decrease in specific surface area; (iii) onset and progress of the ART; (iv) the increasing instability of the nitrogen in the titania lattice. - Graphical abstract: Nitrogen modified TiO{sub 2} synthesised via a green aqueous sol–gel method showed to degrade nitrogen oxides (NO{sub x}) under visible white-light (indoor artificial lighting), with a photocatalytic activity double than that of the standard P25 TiO{sub 2} NPs. - Highlights: • N–TiO{sub 2} synthesised via a green aqueous sol–gel method. • Advanced X-ray methods used to detect both crystalline and amorphous contents. • Microstructure fully addressed via XRPD and whole powder pattern modelling. • Photocatalytic NO{sub x} removal assessed using both solar and visible-light lamps.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Quantum dot-based microfluidic biosensor for cancer detection

We report results of the studies relating to fabrication of an impedimetric microfluidic–based nucleic acid sensor for quantification of DNA sequences specific to chronic myelogenous leukemia (CML). The sensor chip is prepared by patterning an indium–tin–oxide (ITO) coated glass substrate via wet chemical etching method followed by sealing with polydimethylsiloxane (PDMS) microchannel for fluid control. The fabricated microfluidic chip comprising of a patterned ITO substrate is modified by depositing cadmium selenide quantum dots (QCdSe) via Langmuir–Blodgett technique. Further, the QCdSe surface has been functionalized with specific DNA probe for CML detection. The probe DNA functionalized QCdSe integrated miniaturized system has been used to monitor target complementary DNA concentration by measuring the interfacial charge transfer resistance via hybridization. The presence of complementary DNA in buffer solution significantly results in decreased electro-conductivity of the interface due to presence of a charge barrier for transport of the redox probe ions. The microfluidic DNA biosensor exhibits improved linearity in the concentration range of 10{sup −15} M to 10{sup −11} M.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Effects of digital fringe projection operational parameters on detecting powder bed defects in additive manufacturing

Additive manufacturing is a technology transforming traditional production timelines. Specifically, metal additive manufacturing (MAM) has been increasingly adopted by a variety of industries, not only to prototype, but also to fulfill full production scale applications with much lower lead times. Like any maturing manufacturing technology, developments in verifying and validating processes are necessary to support continuous growth. Due to the complex nature of MAM, part quality and repeatability remain integral challenges that inhibit further adoption of MAM for critical component production. In this study, we present data taken from a developing in-process monitoring system designed to measure and detect powder bed defects (PBDs) in powder bed fusion MAM systems using surface height maps created with structured light illumination. We showcase the feasibility of the monitoring technique for in-process implementation by detecting streak PBDs with varying severities (height, width) created in a lab environment. We present results of powder bed measurements for varying experimental parameters of the structured light system such as illumination angle, illumination pattern, and number of illuminations. We also present an expression used to determine experimental height noise based on input parameters for PBD detection based on the instrument transfer function of the structured light monitoring system for arbitrary pixel intensity noise contributions. In conclusion, with the results of PBD detection across multiple experimental measurement parameters, we provide a best practices approach to in-process implementation of the monitoring system in powder bed fusion manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Integration of Electromagnetic Geophysics Forward Simulation in Coupled Flow and Geomechanics for Monitoring a Gas Hydrate Deposit Located in the Ulleung Basin, East Sea, Korea

We investigate the feasibility of electromagnetic (EM) geophysics methods to detect the dissociation of gas hydrate specifically from a gas hydrate deposit located in the Ulleung Basin, East Sea, Korea via an integrated flow-geomechanics-EM geophysics simulation. To this end, coupled flow and geomechanics simulation is first performed with the multiple porosity model employed, where a mixed formulation with the finite volume (FV) and finite element (FE) methods are taken for the flow and geomechanics, respectively. From the saturation and porosity fields obtained from the coupled flow and geomechanics, the electrical conductivity model is established for the EM simulation. Solving the partial differential equation of electrical diffusion which is linearized using the 3D finite element method (FEM), the EM fields are then computed. For numerical experiments, particularly two approaches in the configuration for the EM methods are compared in this contribution: the surface-to-surface and the surface-to-borehole methods. When the surface-to-surface EM method is employed, the EM is found to be less sensitive, implying low detectability. Especially for the short term of production, the low detectability is attributed to the similarity of electrical resistivity between the dissociated gas (CH4) and hydrate as well as the specific dissociation pattern within the intercalated composites of the field. On the other hand, when the surface-to-borehole EM method is employed, its sensitivity to capture the produced gas flow is improved, confirming its detectability in monitoring gas flow. Hence, the EM geophysics simulation integrated with coupled flow and geomechanics can be a potential tool for monitoring gas hydrate deposits.

depressurization↗
DOE OSTI2023

Mechanisms of Pacific decadal variability in ESMs: the roles of stochastic forcing, feedbacks & external forcing

This project explored how and why climate patterns in the Pacific Ocean—specifically Pacific Decadal Variability (PDV)—change over time, and how well leading Earth System Models (ESMs) capture these changes under different climate scenarios. By analyzing large climate model datasets and running new simulations, the team investigated how ocean and atmosphere processes interact to shape PDV. The study tested a leading scientific hypothesis using both simplified and complex models, helping to uncover the key mechanisms that drive long-term climate swings in the Pacific. This work improves our ability to detect, predict, and understand how human-driven climate change may influence major ocean-climate patterns in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Quantitative and high spatial resolution d{sub 33} measurement of piezoelectric bulk and thin films

A single beam laser interferometer based on a modified Mirau detection scheme with a vertical resolution of ∼5 pm was developed for localized d{sub 33} measurements on patterned piezoelectric films. The tool provides high spatial resolution (∼2 μm), essential for understanding scaling and processing effects in piezoelectric materials. This approach enables quantitative information on d{sub 33}, currently difficult in local measurement techniques such as piezoresponse force microscopy. The interferometer is built in a custom microscope and employs a phase lock-in technique in order to detect sub-Angstrom displacements. d{sub 33} measurements on single crystal 0.67PbMg{sub 0.33}Nb{sub 0.67}O{sub 3}-0.33PbTiO{sub 3} and bulk PbZrTiO{sub 3}-5A ceramics demonstrated agreement within <3% with measurements using a double beam laser interferometer. Substrate bending contributions to out-of-plane strain, observed in thin continuous PbZr{sub 0.52}Ti{sub 0.48}O{sub 3} films grown on Si substrates is reduced for electrode diameters smaller than 100 μm. Direct scanning across room temperature and 150 °C poled 5 μm and 10 μm features etched in 0.5 μm thick PbZr{sub 0.52}Ti{sub 0.48}O{sub 3} films doped with 1% Nb confirmed minimal substrate contributions to the effective d{sub 33,f}. Furthermore, enhanced d{sub 33,f} values were observed along the feature edges due to partial declamping from the substrate, thus validating the application of single beam interferometry on finely patterned electrodes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

A phantom design for assessment of detectability in PET imaging

Purpose: The primary clinical role of positron emission tomography (PET) imaging is the detection of anomalous regions of {sup 18}F-FDG uptake, which are often indicative of malignant lesions. The goal of this work was to create a task-configurable fillable phantom for realistic measurements of detectability in PET imaging. Design goals included simplicity, adjustable feature size, realistic size and contrast levels, and inclusion of a lumpy (i.e., heterogeneous) background. Methods: The detection targets were hollow 3D-printed dodecahedral nylon features. The exostructure sphere-like features created voids in a background of small, solid non-porous plastic (acrylic) spheres inside a fillable tank. The features filled at full concentration while the background concentration was reduced due to filling only between the solid spheres. Results: Multiple iterations of feature size and phantom construction were used to determine a configuration at the limit of detectability for a PET/CT system. A full-scale design used a 20 cm uniform cylinder (head-size) filled with a fixed pattern of features at a contrast of approximately 3:1. Known signal-present and signal-absent PET sub-images were extracted from multiple scans of the same phantom and with detectability in a challenging (i.e., useful) range. These images enabled calculation and comparison of the quantitative observer detectability metrics between scanner designs and image reconstruction methods. The phantom design has several advantages including filling simplicity, wall-less contrast features, the control of the detectability range via feature size, and a clinically realistic lumpy background. Conclusions: This phantom provides a practical method for testing and comparison of lesion detectability as a function of imaging system, acquisition parameters, and image reconstruction methods and parameters.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

The Impact of Time-Aware Design Choices in ICS Anomaly Detection

Industrial control systems (ICS) remain vulnerable to increasingly sophisticated cyberattacks, yet evaluating anomaly detection models in these environments is challenging due to temporal dependencies, missing-not-at-random patterns, and extremely imbalanced datasets. These factors make common practices—especially random data splits and na¨ıve imputation— prone to severe temporal leakage, which can inflate reported performance and obscure real-world limitations. In this work, we systematically examine classical machine learning models, temporal deep learning architecture, and tensordecomposition– based methods on a gas-pipeline dataset using a fully temporally separated evaluation pipeline designed to mimic realistic deployment conditions. Our findings show that proper temporal handling and MNAR-aware preprocessing significantly alter the relative performance of popular anomaly-detection methods, providing practical guidance for designing reliable, leakage-resistant ICS intrusion-detection systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Design Choices in Anomaly Detection for Industrial Control Systems: Insights from Gas Pipeline Data

Industrial control systems (ICS) remain vulnerable to increasingly sophisticated cyberattacks, yet evaluating anomaly detection models in these environments is challenging due to temporal dependencies, missing-not-at-random patterns, and extremely imbalanced datasets. These factors make common practices—especially random data splits and naïve imputation—prone to severe temporal leakage, which can inflate reported performance and obscure real-world limitations. In this work, we systematically examine classical machine learning models, temporal deep learning architecture, and tensor-decomposition–based methods on a gas-pipeline dataset using a fully temporally separated evaluation pipeline designed to mimic realistic deployment conditions. Our findings show that proper temporal handling and MNAR-aware preprocessing significantly alter the relative performance of popular anomaly-detection methods, providing practical guidance for designing reliable, leakage-resistant ICS intrusion-detection systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Detecting Large Explosions With Machine Learning Models Trained on Synthetic Infrasound Data

Explosions produce low-frequency acoustic (infrasound) waves capable of propagating globally, but the spatio-temporal variability of the atmosphere makes detecting events difficult. Machine learning (ML) is well-suited to identify the subtle and nonlinear patterns in explosion infrasound signals, but a previous lack of ground-truth data inhibited training of generalized models. We introduce a physics-based method that propagates infrasound sources through realistic atmospheres to create 28,000 synthetic events, which are used to train ML classifiers. A simple artificial neural network and modern temporal convolutional network discriminate synthetic events from background noise with >90% accuracy and, more importantly, successfully identify the majority of real-world explosion signals recorded during the Humming Road Runner experiment. ML models trained entirely on physics-based synthetics advance explosion detection capabilities and make ML more viable to related fields lacking training data.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2015

Detection of microcalcifications by characteristic magnetic susceptibility effects using MR phase image cross-correlation analysis

Purpose: To develop and evaluate a new method for detecting calcium deposits using their characteristic magnetic susceptibility effects on magnetic resonance (MR) images at high fields and demonstrate its potential in practice for detecting breast microcalcifications. Methods: Characteristic dipole signatures of calcium deposits were detected in magnetic resonance phase images by computing the cross-correlation between the acquired data and a library of templates containing simulated phase patterns of spherical deposits. The influence of signal-to-noise ratio and various other MR parameters on the results were assessed using simulations and validated experimentally. The method was tested experimentally for detection of calcium fragments within gel phantoms and calcium-like inhomogeneities within chicken tissue at 7 T with optimized MR acquisition parameters. The method was also evaluated for detection of simulated microcalcifications, modeled from biopsy samples of malignant breast cancer, inserted in silico into breast magnetic resonance imaging (MRIs) of healthy subjects at 7 T. For both assessments of calcium fragments in phantoms and biopsy-based simulated microcalcifications in breast MRIs, receiver operator characteristic curve analyses were performed to determine the cross-correlation index cutoff, for achieving optimal sensitivity and specificity, and the area under the curve (AUC), for measuring the method’s performance. Results: The method detected calcium fragments with sizes of 0.14–0.79 mm, 1 mm calcium-like deposits, and simulated microcalcifications with sizes of 0.4–1.0 mm in images with voxel sizes between (0.2 mm){sup 3} and (0.6 mm){sup 3}. In images acquired at 7 T with voxel sizes of (0.2 mm){sup 3}–(0.4 mm){sup 3}, calcium fragments (size 0.3–0.4 mm) were detected with a sensitivity, specificity, and AUC of 78%–90%, 51%–68%, and 0.77%–0.88%, respectively. In images acquired with a human 7 T scanner, acquisition times below 12 min, and voxel sizes of (0.4 mm){sup 3}–(0.6 mm){sup 3}, simulated microcalcifications with sizes of 0.6–1.0 mm were detected with a sensitivity, specificity, and AUC of 75%–87%, 54%–87%, and 0.76%–0.90%, respectively. However, different microcalcification shapes were indistinguishable. Conclusions: The new method is promising for detecting relatively large microcalcifications (i.e., 0.6–0.9 mm) within the breast at 7 T in reasonable times. Detection of smaller deposits at high field may be possible with higher spatial resolution, but such images require relatively long scan times. Although mammography can detect and distinguish the shape of smaller microcalcifications with superior sensitivity and specificity, this alternative method does not expose tissue to ionizing radiation, is not affected by breast density, and can be combined with other MRI methods (e.g., dynamic contrast-enhanced MRI and diffusion weighted MRI), to potentially improve diagnostic performance.

60 APPLIED LIFE SCIENCES↗