Purpose: To evaluate the feasibility and reproducibility of quantitative cartilage imaging with parallel imaging at 3T and to determine the impact of the acceleration factor (AF) on morphological and relaxation measurements.
Materials And Methods: An eight-channel phased-array knee coil was employed for conventional and parallel imaging on a 3T scanner. The imaging protocol consisted of a T2-weighted fast spin echo (FSE), a 3D-spoiled gradient echo (SPGR), a custom 3D-SPGR T1rho, and a 3D-SPGR T2 sequence. Parallel imaging was performed with an array spatial sensitivity technique (ASSET). The left knees of six healthy volunteers were scanned with both conventional and parallel imaging (AF = 2).
Results: Morphological parameters and relaxation maps from parallel imaging methods (AF = 2) showed comparable results with conventional method. The intraclass correlation coefficient (ICC) of the two methods for cartilage volume, mean cartilage thickness, T1rho, and T2 were 0.999, 0.977, 0.964, and 0.969, respectively, while demonstrating excellent reproducibility. No significant measurement differences were found when AF reached 3 despite the low signal-to-noise ratio (SNR).
Conclusion: The study demonstrated that parallel imaging can be applied to current knee cartilage quantification at AF = 2 without degrading measurement accuracy with good reproducibility while effectively reducing scan time. Shorter imaging times can be achieved with higher AF at the cost of SNR.
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http://dx.doi.org/10.1002/jmri.21122 | DOI Listing |
Biomed Phys Eng Express
January 2025
Shandong University, No. 72, Binhai Road, Jimo, Qingdao City, Shandong Province, Qingdao, 266200, CHINA.
U-Net is widely used in medical image segmentation due to its simple and flexible architecture design. To address the challenges of scale and complexity in medical tasks, several variants of U-Net have been proposed. In particular, methods based on Vision Transformer (ViT), represented by Swin UNETR, have gained widespread attention in recent years.
View Article and Find Full Text PDFAnn Clin Transl Neurol
January 2025
NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, University College London, London, UK.
Objective: To assess the pathological mechanisms contributing to white matter (WM) lesion expansion or contraction and remyelination in multiple sclerosis (MS).
Methods: We assessed 1,613 lesions in 49 people with relapsing-remitting MS in the CCMR-One bexarotene trial (EudraCT 2014-003145-99). We measured lesion orientation relative to WM tracts, surface-in gradients and veins.
J Physiol
January 2025
Université Paris Cité, CNRS, Saints-Pères Paris Institute for the Neurosciences, Paris, France.
Fañanas cells (FCs) are cerebellar glia of unknown function. First described more than a century ago, they have been almost absent from the scientific literature ever since. Here, we combined whole-cell, patch clamp recordings, near-UV laser photolysis, dye-loading and confocal imaging for a first characterization of FCs in terms of their morphology, electrophysiology and glutamate-evoked currents.
View Article and Find Full Text PDFFront Cell Dev Biol
January 2025
Department of Physiology, Immunology and Pathophysiology, Faculty of Medicine, University of Rijeka, Rijeka, Croatia.
Introduction: Cytomegalovirus (CMV) infection reorganizes early endosomes (EE), recycling endosome (RE), and trans-Golgi network (TGN) and expands their intermediates into a large perinuclear structure that forms the inner part of the cytoplasmic assembly complex (AC). The reorganization begins and results with the basic configuration (known as pre-AC) in the early (E) phase of infection, but the sequence of developmental steps is not yet well understood. One of the first signs of the establishment of the inner pre-AC, which can be observed by immunofluorescence, is the accumulation of Rab10.
View Article and Find Full Text PDFFront Plant Sci
January 2025
School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang, China.
Introduction: With the advent of technologies such as deep learning in agriculture, a novel approach to classifying wheat seed varieties has emerged. However, some existing deep learning models encounter challenges, including long processing times, high computational demands, and low classification accuracy when analyzing wheat seed images, which can hinder their ability to meet real-time requirements.
Methods: To address these challenges, we propose a lightweight wheat seed classification model called LWheatNet.
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