Nahla M H Elsaid1, Jerry L Prince2, Steven Roys3, Rao P Gullapalli3, Jiachen Zhuo3. 1. Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States. Electronic address: nelsaid@iu.edu. 2. Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States. 3. Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, United States.
Abstract
PURPOSE: Pronounced spin phase artifacts appear in diffusion-weighted imaging (DWI) with only minor subject motion. While DWI data corruption is often identified as signal drop out in diffusion-weighted (DW) magnitude images, DW phase images may have higher sensitivity for detecting subtle subject motion. METHODS: This article describes a novel method to return a metric of subject motion, computed using an image texture analysis of the DW phase image. This Phase Image Texture Analysis for Motion Detection in dMRI (PITA-MDD) method is computationally fast and reliably detects subject motion from diffusion-weighted images. A threshold of the motion metric was identified to remove motion-corrupted slices, and the effect of removing corrupted slices was assessed on the reconstructed FA maps and fiber tracts. RESULTS: Using a motion-metric threshold to remove the motion-corrupted slices results in superior fiber tracts and fractional anisotropy maps. When further compared to a state-of-the-art magnitude-based motion correction method, PITA-MDD was able to detect comparable corrupted slices in a more computationally efficient manner. CONCLUSION: In this study, we evaluated the use of DW phase images to detect motion corruption. The proposed method can be a robust and fast alternative for automatic motion detection in the brain with multiple applications to inform prospective motion correction or as real-time feedback for data quality control during scanning, as well as after data is already acquired.
PURPOSE: Pronounced spin phase artifacts appear in diffusion-weighted imaging (DWI) with only minor subject motion. While DWI data corruption is often identified as signal drop out in diffusion-weighted (DW) magnitude images, DW phase images may have higher sensitivity for detecting subtle subject motion. METHODS: This article describes a novel method to return a metric of subject motion, computed using an image texture analysis of the DW phase image. This Phase Image Texture Analysis for Motion Detection in dMRI (PITA-MDD) method is computationally fast and reliably detects subject motion from diffusion-weighted images. A threshold of the motion metric was identified to remove motion-corrupted slices, and the effect of removing corrupted slices was assessed on the reconstructed FA maps and fiber tracts. RESULTS: Using a motion-metric threshold to remove the motion-corrupted slices results in superior fiber tracts and fractional anisotropy maps. When further compared to a state-of-the-art magnitude-based motion correction method, PITA-MDD was able to detect comparable corrupted slices in a more computationally efficient manner. CONCLUSION: In this study, we evaluated the use of DW phase images to detect motion corruption. The proposed method can be a robust and fast alternative for automatic motion detection in the brain with multiple applications to inform prospective motion correction or as real-time feedback for data quality control during scanning, as well as after data is already acquired.
Authors: Rodrigo D Perea; Rebecca C Rada; Jessica Wilson; Eric D Vidoni1; Jill K Morris; Kelly E Lyons; Rajesh Pahwa; Jeffrey M Burns; Robyn A Honea Journal: J Alzheimers Dis Parkinsonism Date: 2013-08-26
Authors: Xiao Liang; Pan Su; Sunil G Patil; Nahla M H Elsaid; Steven Roys; Maureen Stone; Rao P Gullapalli; Jerry L Prince; Jiachen Zhuo Journal: Magn Reson Med Date: 2021-03-04 Impact factor: 3.737