Journal of neuroimaging : official journal of the American Society of Neuroimaging
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Differentiating idiopathic normal pressure hydrocephalus (iNPH) from neurodegenerative disorders such as progressive supranuclear palsy (PSP), Multiple System Atrophy-parkinsonian type (MSA-P), and vascular dementia (VaD) is challenging due to overlapping clinical and neuroimaging findings. This study assesses if quantitative brain stem and cerebellum metrics can aid in this differentiation. ⋯ Our study questions MRPI's diagnostic performance in distinguishing PSP from iNPH. Simpler indices such as midbrain to pons ratio and midbrain area showed similar or better accuracy. However, all these indices displayed low sensitivity despite significant differences among PSP, MSA-P, and VaD.
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High-resolution magnetic resonance imaging (HR-MRI) can provide valuable insights into the histopathological characteristics of moyamoya disease (MMD). However, the patterns of vessel wall contrast enhancement have not been well established. We aimed to identify the contrast enhancement patterns of the vessel walls associated with acute cerebral infarction using HR-MRI in MMD. ⋯ Concentric wall enhancement was a significant predictor of acute cerebral infarction in patients with MMD.
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Review Meta Analysis
Artificial intelligence/machine learning for neuroimaging to predict hemorrhagic transformation: Systematic review/meta-analysis.
Early and reliable prediction of hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) is crucial for treatment decisions and early intervention. The purpose of this study was to conduct a systematic review and meta-analysis on the performance of artificial intelligence (AI) and machine learning (ML) models that utilize neuroimaging to predict HT. ⋯ AI/ML models can reliably predict the occurrence of HT in AIS patients. More prospective studies are needed for subgroup analyses and higher clinical certainty and usefulness.
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Meningiomas are the most common neoplasms of the central nervous system, accounting for approximately 40% of all brain tumors. Surgical resection represents the mainstay of management for symptomatic lesions. Preoperative planning is largely informed by neuroimaging, which allows for evaluation of anatomy, degree of parenchymal invasion, and extent of peritumoral edema. ⋯ We also summarize the role of advanced imaging techniques, including magnetic resonance perfusion and spectroscopy, for the preoperative evaluation of meningiomas. In addition, we describe the potential impact of emerging technologies, such as artificial intelligence and machine learning, on meningioma diagnosis and management. A strong foundation of knowledge in the latest meningioma imaging techniques will allow the neuroradiologist to help optimize preoperative planning and improve patient outcomes.
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Comparative Study
Validation of bedside manual versus automated measurements of brain arterial diameters from MR angiography.
Brain arterial luminal diameters are reliably measured with automated imaging software. Nonautomated imaging software alternatives such as a Picture Archiving Communication System are more common bedside tools used for manual measurement. This study is aimed at validating manual measurements against automated methods. ⋯ Results suggest that manual measurements of ICA and BA diameters, but not MCA or ACA, are valid and could be used to identify dilated brain arteries at the bedside and for eventual selection of patients with dolichoectasia into clinical trials.