The study purpose was to develop and validate a quality assurance test for CT automatic exposure control (AEC) systems based on a set of nested polymethylmethacrylate CTDI phantoms. The test phantom was created by offsetting the 16 cm head phantom within the 32 cm body annulus, thus creating a three part phantom. This was scanned at all acceptance, routine, and some nonroutine quality assurance visits over a period of 45 months, resulting in 115 separate AEC tests on scanners from four manufacturers. For each scan the longitudinal mA modulation pattern was generated and measurements of image noise were made in two annular regions of interest. The scanner displayed CTDIvol and DLP were also recorded. The impact of a range of AEC configurations on dose and image quality were assessed at acceptance testing. For systems that were tested more than once, the percentage of CTDIvol values exceeding 5%, 10%, and 15% deviation from baseline was 23.4%, 12.6%, and 8.1% respectively. Similarly, for the image noise data, deviations greater than 2%, 5%, and 10% from baseline were 26.5%, 5.9%, and 2%, respectively. The majority of CTDIvol and noise deviations greater than 15% and 5%, respectively, could be explained by incorrect phantom setup or protocol selection. Barring these results, CTDIvol deviations of greater than 15% from baseline were found in 0.9% of tests and noise deviations greater than 5% from baseline were found in 1% of tests. The phantom was shown to be sensitive to changes in AEC setup, including the use of 3D, longitudinal or rotational tube current modulation. This test methodology allows for continuing performance assessment of CT AEC systems, and we recommend that this test should become part of routine CT quality assurance programs. Tolerances of ± 15% for CTDIvol and ± 5% for image noise relative to baseline values should be used.
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http://dx.doi.org/10.1120/jacmp.v17i4.6165 | DOI Listing |
Environ Manage
January 2025
United States Department of Agriculture, Animal Plant Health Inspection Service, Wildlife Services, Fort Collins, CO, USA.
The great horned owl (Bubo virginianus) is a generalist predator that inhabits wide-ranging territories that are relatively stable throughout the year. These owls are also involved in a variety of human-owl conflicts, including killing of domestic poultry, predating colonially nesting seabirds and shorebirds, and pose a hazard to safe aircraft operations. Managing these conflict situations presents unique challenges as great horned owls are nocturnally active and occupy a wide range of habitats.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Computer Science and Systems Engineering, Faculty of Information and Communication Technology, Wrocław University of Science and Technology, 50-370 Wrocław, Poland.
The distributed nature of IoT systems and new trends focusing on fog computing enforce the need for reliable communication that ensures the required quality of service for various scenarios. Due to the direct interaction with the real world, failure to deliver the required QoS level can introduce system failures and lead to further negative consequences for users. This paper introduces a prediction-based resource allocation method for Multi-Access Edge Computing-capable networks, aimed at assurance of the required QoS and optimization of resource utilization for various types of IoT use cases featuring adaptability to changes in users' requests.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Zhejiang HOUDAR Intelligent Technology Co., Ltd., Hangzhou 310023, China.
In industrial contexts, anomaly detection is crucial for ensuring quality control and maintaining operational efficiency in manufacturing processes. Leveraging high-level features extracted from ImageNet-trained networks and the robust capabilities of the Deep Support Vector Data Description (SVDD) model for anomaly detection, this paper proposes an improved Deep SVDD model, termed Feature-Patching SVDD (FPSVDD), designed for unsupervised anomaly detection in industrial applications. This model integrates a feature-patching technique with the Deep SVDD framework.
View Article and Find Full Text PDFCancers (Basel)
January 2025
Department of Economic and Medical Informatics, University of Lodz, 90-214 Lodz, Poland.
: The certification of hospitals as colorectal cancer centers aims to improve treatment quality, but evidence supporting its effectiveness remains limited. This study evaluated the impact of certification on treatment outcomes for rectal cancer patients in Germany. : We conducted a retrospective analysis of 14,905 patients with primary rectal cancer (UICC Stages I-III) treated at 271 hospitals.
View Article and Find Full Text PDFAnaesthesiologie
January 2025
Abteilung für Anästhesie und operative Intensivmedizin, Krankenhaus Vilshofen, Vilshofen, Deutschland.
Background: The electronic cognitive aid for emergencies in anesthesia (eGENA) is an app that offers digital support in anesthesiological emergency situations as a cognitive aid tool via checklists for memory and making decisions. The eGENA was published by the German Society of Anesthesiology and has been implemented in the emergency management of the anesthesiological team of the clinic in Potsdam, Germany.
Objective: The primary endpoint was to observe the influence of eGENA on the anesthesiological emergency management on the subjective feeling of assurance as well as on quality of treatment and, therefore, patient safety.
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