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Abstract #0430

Simultaneous detection and identification of MR artifact types in whole-body imaging

Thomas Kuestner1,2, Ke Liu2, Annika Liebgott2,3, Lukas Mauch2, Petros Martirosian1, Fabian Bamberg3, Konstantin Nikolaou3, Bin Yang2, Fritz Schick1, and Sergios Gatidis3

1Section on Experimental Radiology, University Hospital of Tuebingen, Tuebingen, Germany, 2Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany, 3Department of Radiology, University Hospital of Tuebingen, Tuebingen, Germany

Varying acquisition and reconstruction conditions as well as long examination times make MRI susceptible to various kinds of artifacts. If suitable correction techniques are not available/applicable, if human experts who judge the achieved quality are not present or for epidemiological cohort studies in which a manual quality analysis of the large database is impracticable, an automated detection and identification of these artifacts is of interest. Convolutional neural networks with residual and inception layers localize and identify occurring artifacts. Artifacts (motion and field inhomogeneity) can be precisely identified with an accuracy of 92% in a whole-body setting with varying contrasts.

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