Purpose: To combine the benefits of time-resolved dynamic imaging and single elliptical centric acquisitions in a reasonable scan time.
Materials And Methods: A time series of images with moderate spatial resolution was acquired using the 3D Time-Resolved Imaging of Contrast KineticS (3D TRICKS) technique with elliptical centric encoding during contrast arrival. Following venous opacification, a complete large centrically encoded k-space volume was acquired. The high-spatial-frequency portions of this volume were combined with a 3D TRICKS time frame to form a high-resolution image. An additional single image is formed by suppressing background and signal averaging all acquired data, including post-venous low-spatial-frequency data. For this image, 2D temporal correlation analysis is used to suppress low-spatial-frequency vein contributions. Arrival time and spatial correlations are used to suppress background.
Results: The 3D TRICKS time frame may be selected to ensure a combined high-resolution image that has optimal central k-space sampling for any vascular region. The single image formed by signal averaging all acquired data has increased contrast-to-noise (CNR) and signal-to-noise (SNR) ratios.
Conclusion: The advantages of time-resolved and high-spatial-resolution imaging were combined using an extended dual-phase acquisition. Some SNR and CNR gain was achieved by signal averaging. This process is facilitated by background and vein suppression.
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http://dx.doi.org/10.1002/jmri.10071 | DOI Listing |
Since late 2021, a panzootic of highly pathogenic H5N1 avian influenza virus has driven significant morbidity and mortality in wild birds, domestic poultry, and mammals. In North America, infections in novel avian and mammalian species suggest the potential for changing ecology and establishment of new animal reservoirs. Outbreaks among domestic birds have persisted despite aggressive culling, necessitating a re-examination of how these outbreaks were sparked and maintained.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
January 2025
The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China.
Background: The diagnosis and treatment of epilepsy continue to face numerous challenges, highlighting the urgent need for the development of rapid, accurate, and non-invasive methods for seizure detection. In recent years, advancements in the analysis of electroencephalogram (EEG) signals have garnered widespread attention, particularly in the area of seizure recognition.
Methods: A novel hybrid deep learning approach that combines feature fusion for efficient seizure detection is proposed in this study.
Sci Rep
January 2025
Department of Biosystems Engineering, Graduate School of Science and Engineering, Yamagata University (emeritus), Yonezawa, Japan.
We developed a deep learning-based extraction of electrocardiographic (ECG) waves from ballistocardiographic (BCG) signals and explored their use in R-R interval (RRI) estimation. Preprocessed BCG and reference ECG signals were inputted into the bidirectional long short-term memory network to train the model to minimize the loss function of the mean squared error between the predicted ECG (pECG) and genuine ECG signals. Using a dataset acquired with polyvinylidene fluoride and ECG sensors in different recumbent positions from 18 participants, we generated pECG signals from preprocessed BCG signals using the learned model and evaluated the RRI estimation performance by comparing the predicted RRI with the reference RRI obtained from the ECG signal using a leave-one-subject-out cross-validation scheme.
View Article and Find Full Text PDFPLoS One
January 2025
Australian National Phenome Center and Center for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia.
Understanding the distribution and variation in inflammatory markers is crucial for advancing our knowledge of inflammatory processes and evaluating their clinical utility in diagnosing and monitoring acute and chronic disease. 1H NMR spectroscopy of blood plasma and serum was applied to measure a composite panel of inflammatory markers based on acute phase glycoprotein signals (GlycA and GlycB) and sub-regions of the lipoprotein derived Supramolecular Phospholipid Composite signals (SPC1, SPC2 and SPC3) to establish normal ranges in two healthy, predominantly white cohorts from Australia (n = 398) and Spain (n = 80; ages 20-70 years). GlycA, GlycB, SPC1 and SPC3 were not significantly impacted by age or sex, but SPC2 (an HDL-related biomarker) was significantly higher in women across all age ranges by an average of 33.
View Article and Find Full Text PDFPLoS One
January 2025
Cnooc Information Technology Co., Ltd., Shenzhen, Guangdong, China.
A data transmission delay compensation algorithm for an interactive communication network of an offshore oil field operation scene in severe weather is proposed. To solve the problem of unstable microwave signals and a large amount of noise in the communication network caused by bad weather, the communication network signal denoising method based on Lagrange multiplier symplectic singular value mode decomposition is adopted, and the communication network data denoising process is realized through five steps; phase space reconstruction, symplectic geometric similarity transformation, grouping, diagonal averaging, and adaptive reconstruction. Simultaneously, the weak communication signal is compensated after being captured, that is, the characteristics of the weak signal are enhanced.
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