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Abstract #4097

Machine learning algorithms for detection of motion artifacts: a general approach

Alessandro Sciarra1, Hendrik Mattern1, and Oliver Speck1,2,3,4

1Department of Biomedical Magnetic Resonance, Otto-von-Guericke University, Magdeburg, Magdeburg, Germany, 2German Center for Neurodegenerative Diseases, Magdeburg, Germany, 3Center for Behavioral Brain Sciences, Magdeburg, Germany, 4Leibniz Institute for Neurobiology, Magdeburg, Germany

Despite all the developments to overcome MRI motion artifacts, there are still open questions. When do we need to repeat a scan? Is the image quality sufficient for segmentation or to make a diagnosis? Is the motion correction working properly? Independent of the type of image acquired (structural, diffusion, functional, etc.), machine learning algorithms can detect automatically motion artifacts and provide feedback in real time. In this work different machine learning algorithms have been tested to detect motion artifacts in synthetic and in vivo data.

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