Neuroimaging clinics of North America
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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.
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Neuroimaging Clin. N. Am. · Aug 2020
ReviewMagnetic Resonance Spectroscopy of the Head and Neck: Principles, Applications, and Challenges.
Several investigations have revealed the utility of magnetic resonance spectroscopy (MRS) as an adjunct in the evaluation of lesions of the head and neck. This technique remains a challenge in the head and neck because of its low signal-to-noise ratio and long acquisition times. In this review article, the basics of image acquisition technique and reported clinical utilities of head and neck MRS are presented.
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Neuroimaging Clin. N. Am. · Aug 2020
ReviewCommon Data Elements in Head and Neck Radiology Reporting.
Radiologists must convert the complex information in head and neck imaging into text reports that can be understood and used by clinicians, patients, and fellow radiologists for patient care, research, and quality initiatives. Common data elements in reporting, through use of defined questions with constrained answers and terminology, allow radiologists to incorporate best practice standards and improve communication of information regardless of individual reporting style. Use of common data elements for head and neck reporting has the potential to improve outcomes, reduce errors, and transition data consumption not only for humans but future machine learning systems.
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Neuroimaging Clin. N. Am. · Aug 2020
ReviewNeck Imaging Reporting and Data System: Principles and Implementation.
Head and neck cancer surveillance imaging is diagnostically challenging, often with highly distorted anatomy after surgery and chemoradiation therapy. In the era of standardized reporting, the Neck Imaging Reporting and Data System (NI-RADS) was developed as a numerical classification system to provide clear and concise radiology reports and recommend next management step. There are 5 categories, each conveying a certain level of suspicion for the presence of persistent or recurrent disease. This article reviews the goals of NI-RADS, NI-RADS categories and lexicon, current research, and the future direction of NI-RADS in posttreatment head and neck cancer surveillance.