Global Positioning System (GPS) is an important new technology for spatio-temporal behaviour studies of animals. Differential correction improves location accuracy. Previously, it mostly removed partially the influence of Selective Availability (SA). SA was deactivated in May 2000. The aim of this study was to quantify the influence of SA cancellation on location accuracy of various GPS receivers. We tested the accuracy of locations obtained from non-differential and differential GPS animal collars before and after SA removal. We found a significant improvement in accuracy for both types of GPS collars. However, differential GPS still provides more accurate locations.
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http://dx.doi.org/10.1016/s1631-0691(02)01414-2 | DOI Listing |
Eur Radiol
January 2025
Neuroradiology Section, Department of Radiology, Hospital Universitario 12 de Octubre, Madrid, Spain.
Objectives: Brain metastases are the most common intracranial malignancy in adults, and their detection is crucial for treatment planning. Post-contrast 3D T1 gradient-recalled echo (GRE) sequences are commonly used for this purpose, but contrast-enhanced 3D T1 turbo spin-echo (TSE) sequences with motion-sensitized driven-equilibrium (MSDE) technique ("black blood") may offer improved detection. This study aimed to compare the effectiveness of contrast-enhanced 3D black blood sequences to standard 3D T1 GRE sequences in detecting brain metastases on a 1.
View Article and Find Full Text PDFRadiol Oncol
January 2025
1Department of Cardiology, University Medical Center Ljubljana, Ljubljana, Slovenia.
Background: The differential diagnosis of cardiac myxomas (CM), the most common benign primary cardiac tumors, is broad and a thorough diagnostic workup is required to establish accurate diagnosis prior to surgical resection. Transthoracic echocardiography (TTE) is usually the first imaging modality used for diagnosis of suspected CM. In a single tertiary centre study, we sought to determine the accuracy, sensitivity, and specificity of TTE in the diagnosis of CM and to determine echocardiographic characteristics indicative of CM.
View Article and Find Full Text PDFBJOG
January 2025
Department of Obstetrics and Gynecology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Objective: To determine the diagnostic performance and clinical utility of the M4 prediction model and the NICE algorithm managing women with pregnancy of unknown location (PUL).
Design: The study has a superiority design regarding specificity for non-ectopic pregnancy for M4, given that the primary outcome of sensitivity for ectopic pregnancy (EP) is non-inferior in comparison with the NICE algorithm.
Setting: Emergency gynaecology units in Sweden.
Quant Imaging Med Surg
January 2025
Department of Ultrasonography, Nanjing Drum Tower Hospital, Drum Tower Clinical Medical College, Nanjing Medical University, Nanjing, China.
Background: The ability of conventional ultrasound (US)-guided liver biopsy to visualize certain liver lesions, particularly those affected by conditions like hepatitis or cirrhosis, which can obscure lesion boundaries and lead to inaccurate biopsy targeting, is limited. This study aimed to evaluate the potential of multimodal US techniques to improve the visibility of liver lesions that are indistinct under conventional US, and to enhance the success rate of percutaneous biopsies.
Methods: In total, 144 patients with liver masses and lesions that were not clearly visible on conventional US from October 2018 to January 2024 were enrolled in this retrospective analysis.
Quant Imaging Med Surg
January 2025
School of Computing, Mathematics and Engineering, Charles Sturt University, Albury, Australia.
Background: The limitation in spatial resolution of bone scintigraphy, combined with the vast variations in size, location, and intensity of bone metastasis (BM) lesions, poses challenges for accurate diagnosis by human experts. Deep learning-based analysis has emerged as a preferred approach for automating the identification and delineation of BM lesions. This study aims to develop a deep learning-based approach to automatically segment bone scintigrams for improving diagnostic accuracy.
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