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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