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Abstract #1366

Perceptual motion scoring: An algorithm for automated detection and grading of MRI motion artifacts

Rafael Brada1, Michael Rotman1, Sangtae Ahn2, and Christopher J. Hardy2
1GE Reserach, Herzliya, Israel, 2GE Research, Niskayuna, NY, United States

We introduce a method for the automatic detection and scoring for motion artifacts in 2D FSE images. The method is based on analyzing the difference in k-space data between two coil array elements. The relative motion score is a parameter free calculation. To match human observer rankings, linear regression coefficients were calculated on a development set of seventeen T1 brain series. The normalized score was tested on nine T1-FLAIR FSE brain series achieving an R2 of 0.91. The ability to automatically detect and grade the severity of motion artifacts is important for better clinical workflows, and for research purposes.

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