Neuroimaging clinics of North America
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Neuroimaging Clin. N. Am. · Feb 2021
ReviewMethods of Analysis: Functional MRI for Presurgical Planning.
There are many technical and nontechnical steps involved in a successful clinical functional MRI (fMRI) scan. The output from scanning and analysis can only be as good as the input, so task instruction and rehearsal are the most important steps during an clinical fMRI procedure. Properly pre-processed data significantly affects statistical analysis, which has a great impact on image interpretation. Even though there is general agreement on how to process clinical fMRI data, such as algorithms for head motion detection and correction, the theory and practicalities associated with data processing remain complex and constantly evolving.
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Neuroimaging Clin. N. Am. · Feb 2021
ReviewAdult Primary Brain Neoplasm, Including 2016 World Health Organization Classification.
In 2016, the World Health Organization (WHO) central nervous system (CNS) classification scheme incorporated molecular parameters in addition to traditional microscopic features for the first time. Molecular markers add a level of objectivity that was previously missing for tumor categories heavily dependent on microscopic observation for pathologic diagnosis. This article provides a brief discussion of the major 2016 updates to the WHO CNS classification scheme and reviews typical MR imaging findings of adult primary CNS neoplasms, including diffuse infiltrating gliomas, ependymal tumors, neuronal/glioneuronal tumors, pineal gland tumors, meningiomas, nerve sheath tumors, solitary fibrous tumors, and lymphoma.
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Neuroimaging Clin. N. Am. · Nov 2020
ReviewArtificial Intelligence and Stroke Imaging: A West Coast Perspective.
Artificial intelligence (AI) advancements have significant implications for medical imaging. Stroke is the leading cause of disability and the fifth leading cause of death in the United States. ⋯ AI techniques are well-suited for dealing with vast amounts of stroke imaging data and a large number of multidisciplinary approaches used in classification, risk assessment, segmentation tasks, diagnosis, prognosis, and even prediction of therapy responses. This article addresses this topic and seeks to present an overview of machine learning and/or deep learning applied to stroke imaging.
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Neuroimaging Clin. N. Am. · Nov 2020
ReviewArtificial Intelligence Applications for Workflow, Process Optimization and Predictive Analytics.
There is great potential for artificial intelligence (AI) applications, especially machine learning and natural language processing, in medical imaging. Much attention has been garnered by the image analysis tasks for diagnostic decision support and precision medicine, but there are many other potential applications of AI in radiology and have potential to enhance all levels of the radiology workflow and practice, including workflow optimization and support for interpretation tasks, quality and safety, and operational efficiency. This article reviews the important potential applications of informatics and AI related to process improvement and operations in the radiology department.
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Neuroimaging Clin. N. Am. · Nov 2020
ReviewAn East Coast Perspective on Artificial Intelligence and Machine Learning: Part 1: Hemorrhagic Stroke Imaging and Triage.
Hemorrhagic stroke is a medical emergency. Artificial intelligence techniques and algorithms may be used to automatically detect and quantitate intracranial hemorrhage in a semiautomated fashion. ⋯ This article reviews artificial intelligence algorithms for intracranial hemorrhage detection, quantification, and prognostication. Multiple algorithms currently being explored are described and illustrated with the help of examples.