Noise correlation between multiple receiver coils is discussed using principles of statistical physics. Using the general fluctuation-dissipation theorem we derive the prototypic correlation formula originally determined by Redpath (Magn Res Med 1992;24:85-89), which states that correlation of current spectral noise depends on the real part of the inverse impedance matrix at a given frequency. A distinct correlation formula is also derived using the canonical partition function, which states that correlation of total current noise over the entire frequency spectrum depends on the inverse inductance matrix. The Kramers-Kronig relation is used to equate the inverse inductance matrix to the spectral integral of the inverse impedance matrix, implying that the total noise is equal to the summation of the spectral noise over the entire frequency spectrum. Previous conflicting arguments on noise correlation may be reconciled by differentiating between spectral and total noise correlation. These theoretical derivations are verified experimentally using two-coil arrays.
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Magn Reson Med
January 2025
Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany.
Purpose: Pulmonary MRI faces challenges due to low proton density, rapid transverse magnetization decay, and cardiac and respiratory motion. The fermat-looped orthogonally encoded trajectories (FLORET) sequence addresses these issues with high sampling efficiency, strong signal, and motion robustness, but has not yet been applied to phase-resolved functional lung (PREFUL) MRI-a contrast-free method for assessing pulmonary ventilation during free breathing. This study aims to develop a reconstruction pipeline for FLORET UTE, enhancing spatial resolution for three-dimensional (3D) PREFUL ventilation analysis.
View Article and Find Full Text PDFUltrasound Med Biol
January 2025
Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong; Biomedical Engineering Programme, The University of Hong Kong, Hong Kong. Electronic address:
Objective: Near-field (NF) clutter filters are critical for unveiling true myocardial structure and dynamics. Randomized singular value decomposition (rSVD) stands out for its proven computational efficiency and robustness. This study investigates the effect of rSVD-based NF clutter filtering on myocardial motion estimation.
View Article and Find Full Text PDFJ Neurosci
January 2025
Université Paris Cité, CNRS, Integrative Neuroscience and Cognition Center, F-75006 Paris, France.
Attention is key to perception and human behavior, and evidence shows that it periodically samples sensory information (<20Hz). However, this view has been recently challenged due to methodological concerns and gaps in our understanding of the function and mechanism of rhythmic attention. Here we used an intensive ∼22-hour psychophysical protocol combined with reverse correlation analyses to infer the neural representation underlying these rhythms.
View Article and Find Full Text PDFSLAS Discov
January 2025
The Hormel Institute, University of Minnesota, Austin, MN 55912. Electronic address:
Metabolic reprogramming of purine biosynthesis is a hallmark of cancer metabolism and represents a critical vulnerability. The enzyme phosphoribosylformylglycinamidine synthase (PFAS) catalyzes the fourth step in de novo purine biosynthesis and has been demonstrated to be prognostic for survival of liver cancer. Despite the importance of this protein as a drug target, there are no known specific inhibitors of PFAS activity.
View Article and Find Full Text PDFPhys Med Biol
January 2025
Radiological Sciences, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, California, 90095, UNITED STATES.
Objective: The study aims to systematically characterize the effect of CT parameter variations on images and lung radiomic and deep features, and to evaluate the ability of different image harmonization methods to mitigate the observed variations.
Approach: A retrospective in-house sinogram dataset of 100 low-dose chest CT scans was reconstructed by varying radiation dose (100%, 25%, 10%) and reconstruction kernels (smooth, medium, sharp). A set of image processing, convolutional neural network (CNNs), and generative adversarial network-based (GANs) methods were trained to harmonize all image conditions to a reference condition (100% dose, medium kernel).
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