Journal of neuroimaging : official journal of the American Society of Neuroimaging
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The central vein sign (CVS) is a diagnostic imaging biomarker for multiple sclerosis (MS). FLAIR* is a combined MRI contrast that provides high conspicuity for CVS at 3 Tesla (3T), enabling its sensitive and accurate detection in clinical settings. This study evaluated whether CVS conspicuity of 3T FLAIR* is reliable across imaging sites and MRI vendors and whether gadolinium (Gd) contrast increases CVS conspicuity. ⋯ CVS conspicuity on 3T FLAIR* is consistent across imaging sites and MRI vendors. Moreover, Gd-based contrast agent significantly improved CVS conspicuity on 3T FLAIR*. These findings support the implementation of FLAIR* in clinical settings for MS.
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Different types of physical training can lead to changes in brain activity and function, and these changes can vary depending on the type of training. However, it remains unclear whether there are commonalities in how different types of training affect brain activity and function. The purpose of this study is to compare the brain activity states of professional athletes with those of ordinary university students and to explore the relationship between training and differences in brain activity states. ⋯ The study results indicate that long-term physical training is associated with changes in brain activity in athletes, providing insights into the neural mechanisms underlying behavioral performance in professional athletes.
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Intracranial pressure (ICP) monitoring is a cornerstone of neurocritical care in managing severe brain injury. However, current invasive ICP monitoring methods carry significant risks, including infection and intracranial hemorrhage, and are contraindicated in certain clinical situations. Additionally, these methods are not universally available. ⋯ Automating both ONSD image acquisition and measurement could enhance accuracy and reliability, thereby improving its utility as a noninvasive ICP estimation tool. A range of image analysis and machine learning (ML) techniques have been applied to address these challenges. In this paper, we provide a narrative review of the current literature on ONSD automation, examining the strengths and limitations of classical image analysis and ML models in improving ONSD-based ICP assessment.
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Differentiation between functioning and nonfunctioning pituitary adenomas/pituitary neuroendocrine tumors (PAs) is clinically relevant. The goal of this study was to determine the feasibility of using time-dependent diffusion MRI (dMRI) for microstructural characterization of PAs. ⋯ The cADC derived from time-dependent dMRI could distinguish between functioning and nonfunctioning PAs, particularly those producing GH.
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Accurate and consistent lesion segmentation from magnetic resonance imaging is required for longitudinal multiple sclerosis (MS) data analysis. In this work, we propose two new transfer learning-based pipelines to improve segmentation performance for subjects in longitudinal MS datasets. ⋯ The proposed method improved lesion segmentation performance and can reduce manual effort to correct the automatic segmentations for final data analysis in longitudinal studies.