Increase of pitch in spiral CT decreases data acquisition time; dual-source CT (DSCT) systems provide improved temporal resolution. We evaluated the combination of these two features. Measurements were performed using a commercial DSCT system equipped with prototype software allowing pitch factors from p = 0.35 to 3.0. We measured slice sensitivity profiles as a function of pitch to assess spatial resolution in the z-direction and the contrast of structures moved periodically to measure temporal resolution. Additionally we derived modulation transfer functions to provide objective parameters; both spatial and temporal resolution were essentially unchanged even at high pitch. CT of the cardiac region of three pigs was performed at p = 3.0. In vivo CT images confirmed good image quality; direct comparison with standard low-pitch phase-correlated CT image datasets showed no significant difference. For a normalized z-axis acquisition of 12 cm, the corresponding effective dose value was 2.0 mSv for the high-pitch CT protocol. We conclude that spiral DSCT imaging with a pitch of 3.0 can provide unimpaired image quality with respect to spatial and temporal resolution. Applications to cardiac and thoracic imaging with effective dose below 1 mSv are possible.
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Malar J
January 2025
MIVEGEC, Univ. Montpellier, CNRS, IRD, Montpellier, France.
Background: The increasing availability of electronic health system data and remotely-sensed environmental variables has led to the emergence of statistical models capable of producing malaria forecasts. Many of these models have been operationalized into malaria early warning systems (MEWSs), which provide predictions of malaria dynamics several months in advance at national and regional levels. However, MEWSs rarely produce predictions at the village-level, the operational scale of community health systems and the first point of contact for the majority of rural populations in malaria-endemic countries.
View Article and Find Full Text PDFNat Commun
January 2025
School of Safety Science, Tsinghua University, Beijing, China.
Ultrafine particles (UFPs) under 100 nm pose significant health risks inadequately addressed by traditional mass-based metrics. The WHO emphasizes particle number concentration (PNC) for assessing UFP exposure, but large-scale evaluations remain scarce. In this study, we developed a stacking-based machine learning framework integrating data-driven and physical-chemical models for a national-scale UFP exposure assessment at 1 km spatial and 1-hour temporal resolutions, leveraging long-term standardized PNC measurements in Switzerland.
View Article and Find Full Text PDFSci Data
January 2025
Water and Development Research Group, Aalto University, Espoo, Finland.
We present a comprehensive gridded GDP per capita dataset downscaled to the admin 2 level (43,501 units) covering 1990-2022. It updates existing outdated datasets, which use reported subnational data only up to 2010. Our dataset, which is based on reported subnational GDP per capita data from 89 countries and 2,708 administrative units, employs various novel methods for extrapolation and downscaling.
View Article and Find Full Text PDFJMIR Infodemiology
January 2025
Salzburg University of Applied Sciences, Puch/Salzburg, Austria.
Background: The novel coronavirus disease (COVID-19) sparked significant health concerns worldwide, prompting policy makers and health care experts to implement nonpharmaceutical public health interventions, such as stay-at-home orders and mask mandates, to slow the spread of the virus. While these interventions proved essential in controlling transmission, they also caused substantial economic and societal costs and should therefore be used strategically, particularly when disease activity is on the rise. In this context, geosocial media posts (posts with an explicit georeference) have been shown to provide a promising tool for anticipating moments of potential health care crises.
View Article and Find Full Text PDFData Brief
February 2025
Faculty of Civil and Environmental Engineering, Technion, Haifa 320003, Israel.
Effective spatio-temporal measurements of water surface elevation (water waves) in laboratory experiments are essential for scientific and engineering research. Existing techniques are often cumbersome, computationally heavy and generally suffer from limited wavenumber/frequency response. To address these challenges a novel method was developed, using polarization filter equipped camera as the main sensor and Machine Learning (ML) algorithms for data processing [1,2].
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