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Abstract #4191

Artificial intelligence-based denoising for clinical magnetic resonance imaging: from head to toe

Dallas Turley1,2, Pattana Wangaryattawanich2, Jalal Andre2, Majid Chailan2, Johannes M. Peeters3, Kim Van de Ven3, and Orpheus Kolokythas2
1Philips Healthcare, Seattle, WA, United States, 2Department of Radiology, University of Washington, Seattle, WA, United States, 3Philips Healthcare, Best, Netherlands

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial IntelligenceArtificial intelligence in MR is a diverse and growing field. In this work, we apply convolutional neural networks (CNN) to accelerated routine clinical protocols to investigate potential image quality improvements. For moderate increase in reconstruction time, CNNs were judged by experienced radiologists to significantly improve image quality by reducing noise and artifact.

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Keywords