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Abstract #1350

Automatic Phase Image Texture Analysis for Motion Detection in Diffusion MRI (APITA-MDD) with Adaptive Thresholding

Xiao Liang1, Pan Su2, Steve Roys1, Rao P Gullapalli1, Jerry L Prince3, and Jiachen Zhuo1
1Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, United States, 2Siemens Medical Solutions USA Inc, Malvern, PA, United States, 3Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States

In this study, we propose a more robust PITA-MDD method with automatic brain ROI selection and adaptive thresholding for the motion threshold (termed APITA-MDD). Automatic brain ROI is initially identified by Otsu’s method and then improved by erosion and dilation. Haralick’s Homogeneity Index (HHI) of each slice is converted to a deviation score independent of image SNR and ROI size for motion detection. APITA-MDD was tested on brain dMRI data acquired with head motion and leg crossing motion and correctly detected motion slices missed by PITA-MDD due to insufficient coverage at edge slices and single thresholding.

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