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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

YAP:Ce scintillator characteristics for neutron detection

YAP:Ce (YAlO{sub 3}:Ce{sup +}, Yttrium Aluminum Perovskite, Ce{sup +} doped) crystals with appropriate converters seem like prospective scintillators for neutron detection. An important aspect for neutron detection with inorganic scintillators is the ability to discriminate neutron radiation from gamma radiation by pulse height of signals. For a detailed measurement of the aspect, a YAP:Ce crystal scintillator with lithium or hydrogen converters and a photomultiplier was used. A plutonium-beryllium neutron source and horizontal neutron channel beams of the LVR-15 research reactor were used as neutron sources. The measurement confirmed the possibility to use the YAP:Ce scintillator for neutron radiation detection. The degree of discrimination between neutron and gamma radiation for different detection configurations was studied. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Response of the REWARD detection system to the presence of a Radiological Dispersal Device

In recent years an increased international concern has emerged about the radiological and nuclear (RN) threats associated with the illicit trafficking of nuclear and radioactive materials that could be potentially used for terrorist attacks. The objective of the REWARD (Real Time Wide Area Radiation Surveillance System) project, co-funded by the European Union 7. Framework Programme Security, consisted in building a mobile system for real time, wide area radiation surveillance, using a CdZnTe detector for gamma radiation and a neutron detector based on novel silicon technologies. The sensing unit includes a GPS system and a wireless communication interface to send the data remotely to a monitoring base station, where it will be analyzed in real time and correlated with historical data from the tag location, in order to generate an alarm when an abnormal situation is detected. Due to its portability and accuracy, the system will be extremely useful in many different scenarios such as nuclear terrorism, lost radioactive sources, radioactive contamination or nuclear accidents. This paper shortly introduces the REWARD detection system, depicts some terrorist threat scenarios involving radioactive sources and special nuclear materials and summarizes the simulation work undertaken during the past three years in the framework of the REWARD project. The main objective consisted in making predictions regarding the behavior of the REWARD system in the presence of a Radiological Dispersion Device (RDD), one of the reference scenarios foreseen for REWARD, using the Monte Carlo simulation program MCNP6. The reference scenario is characterized in detail, from the i) radiological protection, ii) radiation detection requirements and iii) communications points of view. Experimental tests were performed at the Fire Brigades Facilities in Rome and at the Naples Fire Brigades, and the results, which validate the simulation work, are presented and analyzed. The response of the REWARD detection system to the presence of an RDD is predicted and discussed. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

On-Line Monitoring to Detect Sensor and Process Degradation under Normal Operational Transients - 327

Calibration assessment of nuclear plant sensors and transmitters is currently performed through periodic maintenance actions during plant refueling outages. On-line monitoring (OLM) can be used to reduce the time and cost of this maintenance process by using empirical models to evaluate sensor calibration. The implementation of OLM can also mitigate human error associated with unnecessary maintenance on sensors operating within calibration tolerances. OLM has been studied and validated for plants under steady-state baseload operation, but the extension to load-following operation is not well-documented in the literature. Auto-associative kernel regression (AAKR) provides an error-corrected prediction of process sensor measurements for OLM through a weighted average of historic data. The current study evaluates the performance of AAKR for prediction under normal operational transients (such as expected under load-following operation). Performance of the AAKR model with two anomaly detection routines, error uncertainty limit monitoring (EULM) and sequential probability ratio test (SPRT), was evaluated for normal operation, sensor faults, and process degradation. The data used to develop and evaluate the OLM system were collected from a forced flow loop with temperature and flow sensors. Three models of well-correlated groups of sensors are trained to predict normal system behavior under planned transients. The anomaly detection routines are tested for sensor faults and process faults. The results of this experimental study suggest AAKR and an appropriate anomaly detection routine can be applied for sensor calibration and process fault detection under transient operation. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Evaluation of MSNDs for Fast-Neutron Detection and the TREAT Hodoscope

The need for next-generation, accident-tolerant fuels has generated significant interest in the restart of domestic fuels testing at the Transient Reactor Test (TREAT) facility. A key feature of the TREAT reactor core is an open, horizontal channel that allows line-of-sight viewing of test specimens using various instrumentation during steady-state and transient operation. This instrumentation includes the hodoscope, which consists of over 300 steel-collimated channels coupled to Hornyak buttons (and other detectors) for detection of fast neutrons emitted from fission of the test specimen. Posterior analysis can be used to reconstruct fuel motion and other properties from the collimated channel signals. A large-scale effort is underway to develop next generation instrumentation for transient testing at TREAT (and, potentially, other facilities). Work at KSU specifically aims to provide improved detection systems for hodoscope applications. It was initially proposed to adapt KSU-developed Microstructured Semiconductor Neutron Detectors (MSNDs) for use as fast-neutron detectors. The current generation of MSNDs is designed for thermal-neutron detection based on {sup 6}Li and can achieve intrinsic efficiencies nearing 30% owing to the large (n, t) cross section of {sup 6}Li and clever optimization of the geometry. In order to evaluate MSNDs for fast-neutron detection, two approaches were considered: (1) use of a hydrogenous material to thermalize neutrons to exploit the intrinsic efficiencies of current MSNDs, and (2) substitution of a fast-sensitive reactant for {sup 6}Li. The former approach is attractive because it makes use of proven, off-the-shelf technology and leads to reasonable time resolution and efficiency. Unfortunately, use of thermalization leads to prohibitive degradation of spatial resolution. The second approach will require further experimental efforts, but scoping analyses suggest {sup 235}U- or {sup 237}Th-loaded MSNDs may achieve sufficient efficiencies. The rest of this summary examines these issues in more detail. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

Improvements to neutron detection of Pu in solvent for an aqueous buffer tank, magnox reprocessing plant, Sellafield - 14216

The PP Active Feed Tank (PPAFT) is a buffer tank that is not safe by geometry and which is located between the Medium Active (MA) and Plutonium Purification (PP) cycles of the chemical separation process in the Magnox Reprocessing Plant. The PPAFT receives Pu nitrate in acid which has been conditioned ready for the extraction of Pu with TBP/OK (tri-butyl phosphate in kerosene) solvent in the PP Cycle. The criticality safety case for the PPAFT recognises that during both normal operations and potential maloperations solvent which is dissolved (TBP which is present in solution but can be released upon a change in acidity) and entrained (TBP/OK physically carried over but not in solution) will accumulate in the PPAFT. The only practicable route out for Pu in solvent in the PPAFT is the normal process route out from the base of the PPAFT to the PP cycle through intermediate vacuum lift and feed vessels. However, the dished end base of the PPAFT and the intermediate vacuum lift and feed vessels are of particularly challenging geometry. There are five neutron monitors placed around the PPAFT for the detection of Pu in solvent. The original design intent of the neutron detection system was that any single monitor should be able to reveal an accumulation of Pu in solvent before it would potentially be unsafe if fed downstream. However, over time there have been changes to the fuel feed and chemical process of the plant which have had the potential to affect the performance of the neutron monitoring system. The ability of the neutron monitoring system to function to its original intent was questioned. Work has recently been carried out to investigate the capabilities of the PPAFT neutron monitoring system for current plant conditions and to identify potential improvements while making best use of the existing neutron monitors. It was found that moving a neutron monitor from the side to the top of the PPAFT and adding alarms based on the ratio of signals from multiple neutron detectors would be effective in improving detection of Pu in solvent. This paper describes the challenges involved in detecting thin layers of Pu in solvent using neutron monitors, discusses the improvements made to the PPAFT neutron monitors and details the combination of real plant data and modelling with the shielding code MCBEND used to provide confidence in the enhanced neutron monitoring system. (author)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

AI-based Detection and Defense Against Cyberattacks in Distributed Energy Resources

This study will provide comprehensive artificial intelligence (AI)-based solution tools for network security, malware prevention, and sensor data anomaly detection for distributed energy resource (DER) research, development, and demonstration. DER technologies are energy systems (e.g., solar panels, wind turbines, and energy storage systems) that are often connected to the internet and thus vulnerable to cyberattacks. Cybersecurity should be of primary concern for DERs, which is why we propose an integrated multi-layer cyber-defense system for DERs. This system encompasses risk assessments, network security, malware prevention, and detection of anomalies in the sensor data. Implementation of a comprehensive risk assessment with an overview of the model architecture should be the primary step, and should include the potential impact of experiencing, at a given time, one or more cyberattacks on the system. The second step is to ensure that the network security includes firewalls, intrusion detection, and malware prevention. The third step is to provide solution tools that enable sensor data anomaly detection for DERs. By incorporating these considerations into DER research, development, and demonstration, organizations can help ensure the safety and security of their systems and protect against potential cyberattacks.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Enhancing Unknown Waveform Detection by Learning Intra and Inter-domain Dependencies with Advanced Attention Fusion Mechanisms

Detection of unknown waveforms in mission-critical communications is a crucial area of interest for the Department of Energy (DoE). Traditional methods and recent deep learning-based approaches often assume that the training set includes all possible classes, which is impractical for detecting new waveforms. This limitation gives rise to the problem of open-set recognition (OSR), which involves correctly identifying known classes while detecting and rejecting unknown or unseen classes. To address this limitation, we propose a novel dual-domain complex-valued neural architecture that jointly processes time-domain and frequency-domain signal representations using transformer mechanisms. A transformer model is a deep learning architecture that uses self-attention mechanisms to process and learn relationships in sequential data. Our model employs a cosine similarity loss to extract domain-specific features and incorporates a transformer architecture in the latent space to weigh the importance of different features from the time and frequency domains. The transformer layer includes stacked self-attention and cross-attention modules to learn intra-domain and inter-domain dependencies, creating a more holistic signal representation. An attention-based fusion module intelligently combines the time and frequency-domain features using multi-head attention, enabling the network to learn the optimal feature for each domain in each input signal. Quantitative results demonstrate the impact of these architectural choices on overall performance, showing significant improvement after incorporating self and cross-attention modules and using complex attention fusion over simple weighted fusion. Our ongoing work will focus on addressing the limitations of threshold-based OSR methods by developing a novel generative framework that integrates a conditional diffusion probabilistic model (DPM). DPM is a generative framework that learns to synthesize complex data by reversing a gradual noising process using a neural network trained to denoise step-by-step. Our goal is to leverage the inherent strengths of DPMs for identifying unknown signals more robustly. One primary advantage of using a DPM is its ability to provide a more reliable anomaly score based on the model's reconstruction error, rather than relying solely on classifier confidence. Additionally, the iterative denoising process of DPMs makes this approach naturally resilient to low Signal-to-Noise Ratio (SNR) conditions, where traditional methods often fail. By implementing this generative framework, we aim to enhance the model's capability to accurately detect unknown waveforms and maintain performance in challenging environments.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

“Development and application of analytical detection techniques for droplet-based microfluidics”-A review

Droplet-based microfluidics has emerged as a powerful platform for high-throughput and low-volume analysis and screening. At present, droplet-based microfluidics is transitioning from the proof-of-concept stage to real-world applications. During this process, analytical detection techniques play indispensable roles for successfully implementing droplet-based chemical or biological assays. Here, we provide an overview of recent developments in analytical techniques for droplet analysis and elucidate the advantages and limitations of each technique. We cover the majority of technology categories, including optical detection, electrical detection, mass spectrometry, and nuclear magnetic resonance spectroscopy. Additionally, we highlight new research areas that have been enabled by these technical advances. Finally, we provide perspectives on both future technological directions and potential enabling applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Atomically Precise Hexanuclear Ce(IV) Clusters as Functional Fluorescent Nanosensors for Rapid One-Step Detection of PFAS

Here, the presence of poly- and perfluoroalkyl substances (PFAS) in the environment is associated with adverse health effects but measuring PFAS is challenging due to the associated high cost and technical complexities of the analysis. Here, the reactivity of atomically precise metal-oxo clusters is reported and the foundation for their use is provided as fluorescent nanosensors for PFAS detection. The material comprises crystalline, water soluble, hexanuclear cerium-oxo clusters [Ce 6 (µ 3 -O) 4 (µ 3 -OH) 4 ] 12+ decorated with glycine molecules (Ce-Gly) characterized by fluorescence emission at 353 nm. The Ce-Gly fluorescence is found sensitive to long chain carboxylated PFAS of CF 3 –(CF 2 ) n –, where n ≥ 6, such as perfluorooctanoic, perfluorononanoic and perfluorodecanoic acids. This unique reactivity leads to a change in the emission spectra in a concentration dependent manner, enabling PFAS detection through ligand exchange and aggregation-induced emission (AIE) enhancement. No significant cross-reactivity from potentially co-existing species, including sulfonated PFAS, octanoic and dodecanoic acids, humic acid, and inorganic ions is observed. With an optimal concentration of 3.3 µg mL -1 Ce-Gly, the method demonstrated detection limits of 0.24 ppb for PFOA and 0.4 ppb for PFNA. These findings highlight the potential of fluorescence-based detection strategies utilizing nanoscale probes such as Ce-Gly as fluorescent probes and nanosensors for PFAS.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Perovskite CsPbBr 3 Single-Crystal Detector Operating at 10 10 Photons s -1 mm -2 for Ultra-High Flux X-ray Detection

Semiconductors for detecting hard radiation are confronted with considerable problems when operating at high photon fluxes. A perovskite CsPbBr 3 single-crystal detector capable of operating at X-ray fluxes of up to 10 10 photons s -1 mm -2 with beam area ≤ 0.25 mm 2 at 58.61 keV for current-mode X-ray detection is reported. The spectrometer-grade melt-grown CsPbBr 3 detectors show a gamma-ray energy resolution of ~7.5% at 122 keV for 57 Co and dark current as low as 4.3 nA (0.5 nA mm -2 ) at a reverse bias voltage of 200 V (118 V mm -1 ). The detector is tested at X-ray energies of 8.2, 10, and 58.61 keV at a synchrotron light source under a reverse bias voltage of up to 1000 V (588 V mm -1 ). Under a sufficiently high bias voltage and within several hundreds of seconds X-ray exposure, good photocurrent linearity (goodness of fit R 2 > 0.99) and reproducibility are obtained up to a flux of ≈10 10 photons s -1 mm -2 at beam area 0.25 mm 2 with Lower Limit of Detection of ≈10 5 photons s -1 mm -2 and Charge Collection Efficiency of ≈100% for 58.61 keV X-rays. Therefore, wide application of CsPbBr 3 detectors in high-flux X-ray detection is anticipated.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Process prediction and detection of faults using probabilistic bidirectional recurrent neural networks on real plant data

Attaining Industry 4.0 for manufacturing operations requires advanced monitoring systems and real-time data analytics of plant data, among other topics. We propose a Probabilistic Bidirectional Recurrent Network (PBRN) for industrial process monitoring for the early detection of faults. The model is based on a Gated Recurrent Unit (GRU) neural network that allows the model to retain long-term dependencies between sensor data along a time horizon, hence learning the dynamic behavior of the process. To reduce the false-positive detection rate of the model, we compel the model to learn from a highly noisy sensor reading while outputting noise-free sensor outputs. The performance of the proposed model is compared to other data-driven statistical process monitoring schemes using real plant data from an industrial Air Separations Unit (ASU) containing noisy sensor readings. We show that the model can learn from noisy data without reducing its performance. Using two different fault cases, we demonstrate the model’s ability to carry out early fault detection with average false-positive rates of 2.9% and 4.9% for both fault cases. The missed detection rates are 0.1% and 0.2%, respectively.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Utilization of Data Augmentation Techniques in Automated Inspection Systems for Defect Detection in Metals With Limited Data

Accurate identification of defects on metal surfaces is of great interest to many industry sectors, such as the automotive and aerospace industries. In contrast to conventional manual inspection techniques, recent automated inspection systems employ deep learning models trained to detect defects rapidly and precisely. The development of these models often requires a substantial image dataset to acquire adequate knowledge of defect features and enhance their predictive accuracy. When data is limited, augmentation techniques are often used to improve the precision and accuracy of defect detection systems. This study examined the prediction performance of two object detection models, namely Faster Region‐based Convolutional Neural Network (Faster R‐CNN) and You Only Look Once version 8 (YOLOv8), to identify dent defects in limited images of cast iron cylinder head surfaces. The original image set contains 46 images with 563 dents. To overcome limited data availability, common image augmentation techniques along with a copy‐paste method were applied. Results show that standard augmentation improved YOLOv8 accuracy by 8.00% and average precision (AP) by 3.00%. On the other hand, the copy‐paste technique achieved a 20.00% increase in accuracy and a 1% increase in AP with just 200 synthetic dents. Furthermore, these results provide support for using the copy‐paste augmentation strategy to enhance defect detection performance, with a limited dataset, contributing to more accurate defect identification in remanufacturing processes.

36 MATERIALS SCIENCE↗
DOE OSTI2017

AgHalo: A Facile Fluorogenic Sensor to Detect Drug-Induced Proteome Stress

Drug-induced proteome stress that involves protein aggregation may cause adverse effects and undermine the safety profile of a drug. Safety of drugs is regularly evaluated using cytotoxicity assays that measure cell death. However, these assays provide limited insights into the presence of proteome stress in live cells. A fluorogenic protein sensor is reported to detect drug-induced proteome stress prior to cell death. An aggregation prone Halo-tag mutant (AgHalo) was evolved to sense proteome stress through its aggregation. Detection of such conformational changes was enabled by a fluorogenic ligand that fluoresces upon AgHalo forming soluble aggregates. Using 5 common anticancer drugs, we exemplified detection of differential proteome stress before any cell death was observed. Thus, this sensor can be used to evaluate drug safety in a regime that the current cytotoxicity assays cannot cover and be generally applied to detect proteome stress induced by other toxins.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Application of multidimensional structural characterization to detect and describe moderate forest disturbance

The study of vegetation community and structural change has been central to ecology for over a century, yet the ways in which disturbances reshape the physical structure of forest canopies remain relatively unknown. Moderate severity disturbances affect different canopy strata and plant species, resulting in variable structural outcomes and ecological consequences. Terrestrial lidar (light detection and ranging) offers an unprecedented view of the interior arrangement and distribution of canopy elements, permitting the derivation of multidimensional measures of canopy structure that describe several canopy structural traits (CSTs) with known links to ecosystem function. We used lidar-derived CSTs within a machine learning framework to detect and describe the structural changes that result from various disturbance agents, including moderate severity fire, ice storm damage, age-related senescence, hemlock woolly adelgid, beech bark disease, and chronic acidification. We found that fire and ice storms primarily affected the amount and position of vegetation within canopies, while acidification, senescence, pathogen, and insect infestation altered canopy arrangement and complexity. Only two of the six disturbance agents significantly reduced leaf area, counter to common assumptions regarding many moderate severity disturbances. While findings are limited in their generalizability due to lack of replication among disturbances, they do suggest that the current limitations of standard disturbance detection methods—such as optical-based remote sensing platforms, which are often above-canopy perspectives—limit our ability to understand the full ecological and structural impacts of disturbance, and to evaluate the consistency of structural patterns within and among disturbance agents. A more broadly inclusive definition of ecological disturbance that incorporates multiple aspects of canopy structural change may potentially improve the modeling, detection, and prediction of functional implications of moderate severity disturbance as well as broaden our understanding of the ecological impacts of disturbance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Crack detection in fuel cell electrodes using a spatial filtering technique for overcoming noisy backgrounds

Image processing is a powerful tool that allows for rapid and automated data parsing in settings that occupy large variable spaces and require large data sets. Feature detection on difficultly discerned backgrounds is a subset of image processing that facilitates the extraction of quantitative metrics from otherwise subjective data. Crack detection and quantification is an important capability in polymer electrolyte membrane fuel cell quality control, failure analysis, and optimization. This work presents a technique to perform crack detection and quantification which overcomes challenges faced by commonly used image segmentation techniques. We demonstrate the use of a geometrically filtered noise‐level detection technique to select a binary threshold value from which we then quantify how cracked a sample is. Furthermore, we demonstrate the accuracy of our technique using programmatically generated test images of known crack amounts and their performance on real‐world fuel cell catalyst layer samples.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Challenges for unsupervised anomaly detection in particle physics

Anomaly detection relies on designing a score to determine whether a particular event is uncharacteristic of a given background distribution. One way to define a score is to use autoencoders, which rely on the ability to reconstruct certain types of data (background) but not others (signals). In this paper, we study some challenges associated with variational autoencoders, such as the dependence on hyperparameters and the metric used, in the context of anomalous signal (top and W) jets in a QCD background. We find that the hyperparameter choices strongly affect the network performance and that the optimal parameters for one signal are non-optimal for another. In exploring the networks, we uncover a connection between the latent space of a variational autoencoder trained using mean-squared-error and the optimal transport distances within the dataset. We then show that optimal transport distances to representative events in the background dataset can be used directly for anomaly detection, with performance comparable to the autoencoders. Whether using autoencoders or optimal transport distances for anomaly detection, we find that the choices that best represent the background are not necessarily best for signal identification. These challenges with unsupervised anomaly detection bolster the case for additional exploration of semi-supervised or alternative approaches.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

The role of contrast-enhanced computed tomography to detect renal stones

Purpose: To investigate the detectability of renal stones in corticomedullary and nephrographic phases on contrast-enhanced computed tomography (CT). Methods: All consecutive patients between January 2012 and February 2016 undergoing CT of the kidneys according to our department’s standard four-phase protocol and having at least one stone in the NC-phase (NCP) were included. Fifty patients with altogether 136 stones were eligible. Two radiologists in consensus evaluated the NCP from each examination and documented the number, location, and size of stones. Three abdominal radiologists blinded to the findings of the NCP reviewed independently the corticomedullary and nephrographic phases on two different occasions. They reported the number and location of stones in each kidney. For the inter-observer agreement the intra-class correlation coefficient (ICC) was estimated. The detection rate of renal stones was calculated for the three radiologists and compared between the two contrast-enhanced phases and the results were analyzed with concern to the size of the stones. Results: The ICC was 0.86. There was no statistically significant difference between corticomedullary and nephrographic phases (p = 0.94). The detection rate for stones measuring 3–5 mm was 82–88% and 98% for stones ≥ 6 mm. Conclusion: The detectability of renal stones ≥ 6 mm on contrast-enhanced CT is extremely high. This means that stones with a higher risk of not passing spontaneously can be safely diagnosed.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Usefulness of Cone-Beam Computed Tomography and Automatic Vessel Detection Software in Emergency Transarterial Embolization

BackgroundThis study was designed to evaluate the utility of dual phase cone beam computed tomography (DP-CBCT) and automatic vessel detection (AVD) software to guide transarterial embolization (TAE) of angiographically challenging arterial bleedings in emergency settings.MethodsTwenty patients with an arterial bleeding at computed tomography angiography and an inconclusive identification of the bleeding vessel at the initial 2D angiographic series were included. Accuracy of DP-CBCT and AVD software were defined as the ability to detect the bleeding site and the culprit arterial bleeder, respectively. Technical success was defined as the correct positioning of the microcatheter using AVD software. Clinical success was defined as the successful embolization. Total volume of iodinated contrast medium and overall procedure time were registered.ResultsThe bleeding site was not detected by initial angiogram in 20 % of cases, while impossibility to identify the bleeding vessel was the reason for inclusion in the remaining cases. The bleeding site was detected by DP-CBCT in 19 of 20 (95 %) patients; in one case CBCT-CT fusion was required. AVD software identified the culprit arterial branch in 18 of 20 (90 %) cases. In two cases, vessel tracking required manual marking of the candidate arterial bleeder. Technical success was 95 %. Successful embolization was achieved in all patients. Mean contrast volume injected for each patient was 77.5 ml, and mean overall procedural time was 50 min.ConclusionsC-arm CBCT and AVD software during TAE of angiographically challenging arterial bleedings is feasible and may facilitate successful embolization. Staff training in CBCT imaging and software manipulation is necessary.

62 RADIOLOGY AND NUCLEAR MEDICINE↗