NMR imaging is used as an example of how spatial resolution can be improved in a signal-to-noise (S/N) limited situation. The NMR imaging process consists of two components-generating the NMR signal and localizing it in space. This paper will show that spatial resolution not only aids in identifying small structures, but improves the detectability of larger features by preserving their object contrast.
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http://dx.doi.org/10.1109/TMI.1984.4307656 | DOI Listing |
Med Phys
January 2025
Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
Background: The spatial resolution of new, photon counting detector (PCD) CT scanners is limited by the size of the focal spot. Smaller, brighter focal spots would melt the tungsten focal track of a conventional X-ray source.
Purpose: To propose focal spot multiplexing (FSM), an architecture to improve the power of small focal spots and thereby enable higher resolution clinical PCD CT.
Abdom Radiol (NY)
January 2025
Mayo Clinic, Rochester, MN, USA.
Purpose: To compare same-day photon-counting detector CT (PCD-CT) to conventional energy-integrating detector CT (EID-CT) for detection of small renal stones (≤ 3 mm).
Methods: Patients undergoing clinical dual-energy EID-CT for known or suspected stone disease underwent same-day research PCD-CT. Patients with greater than 10 stones and no visible stones under 3 mm were excluded.
Nat Commun
January 2025
Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
An essential task in spatial transcriptomics is identifying spatially variable genes (SVGs). Here, we present Celina, a statistical method for systematically detecting cell type-specific SVGs (ct-SVGs)-a subset of SVGs exhibiting distinct spatial expression patterns within specific cell types. Celina utilizes a spatially varying coefficient model to accurately capture each gene's spatial expression pattern in relation to the distribution of cell types across tissue locations, ensuring effective type I error control and high power.
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January 2025
Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China.
Deforestation-induced forest loss largely affects both the carbon budget and ecosystem services. Subsequent forest regrowth plays a crucial role in ecosystem restoration and carbon replenishment. However, there is an absence of comprehensive datasets explicitly delineating the forest regrowth following deforestation.
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January 2025
Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, 100091, China.
The vegetation index is a key satellite-based variable used to monitor global vegetation distribution and growth. However, existing vegetation index datasets face limitations in achieving both high spatial and temporal resolution, restricting their application potential. This study revised a machine learning spatiotemporal fusion model (InENVI) to produce a high-resolution NDVI dataset with 8-day temporal and 30 m spatial resolution, covering China from 2001 to 2020.
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