Neuroimaging clinics of North America
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This article reviews the history of artificial intelligence and introduces the reader to major events that prompted interest in the field, as well as pitfalls and challenges that have slowed its development. The purpose of this article is to provide a high-level historical perspective on the development of the field over the past decades, highlighting the potential of the field for transforming health care, but also the importance of setting realistic expectations for artificial intelligence applications to avoid repeating historical cyclical trends and a third "artificial intelligence winter."
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Neuroimaging Clin. N. Am. · Nov 2020
ReviewOverview of Machine Learning: Part 2: Deep Learning for Medical Image Analysis.
Deep learning has contributed to solving complex problems in science and engineering. This article provides the fundamental background required to understand and develop deep learning models for medical imaging applications. The authors review the main deep learning architectures such as multilayer perceptron, convolutional neural networks, autoencoders, recurrent neural networks, and generative adversarial neural networks. They also discuss the strategies for training deep learning models when the available datasets are imbalanced or of limited size and conclude with a discussion of the obstacles and challenges hindering the deployment of deep learning solutions in clinical settings.
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Neuroimaging Clin. N. Am. · Aug 2020
ReviewPET Imaging of Tumor Hypoxia in Head and Neck Cancer: A Primer for Neuroradiologists.
Tumor hypoxia is a known independent prognostic factor for adverse patient outcomes in those with head and neck cancer. Areas of tumor hypoxia have been found to be more radiation resistant than areas of tumor with normal oxygenation levels. ⋯ PET imaging is the gold standard method for imaging tumor hypoxia, with 18F-fluoromisonidazole the most extensively studied hypoxic imaging tracer. Newer tracers also show promise.
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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
ReviewDual Energy Computed Tomography in Head and Neck Imaging: Pushing the Envelope.
Multiple applications of dual energy computed tomography (DECT) have been described for the evaluation of disorders in the head and neck, especially in oncology. We review the body of evidence suggesting advantages of DECT for the evaluation of the neck compared with conventional single energy computed tomography scans, but the full potential of DECT is still to be realized. There is early evidence suggesting significant advantages of DECT for the extraction of quantitative biomarkers using radiomics and machine learning, representing a new horizon that may enable this technology to reach its full potential.