Premium
Impact of outliers on diffusion tensor and Q‐ball imaging: Clinical implications and correction strategies
Author(s) -
Sharman Michael A.,
CohenAdad Julien,
Descoteaux Maxime,
Messé Arnaud,
Benali Habib,
Lehericy Stéphane
Publication year - 2011
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.22577
Subject(s) - diffusion mri , outlier , medicine , radiology , computer science , nuclear magnetic resonance , magnetic resonance imaging , physics , artificial intelligence
Abstract Purpose: To measure the impact of corrupted images often found to occur in diffusion‐weighted magnetic resonance imaging (DW‐MRI). To propose a robust method for the correction of outliers, applicable to diffusion tensor imaging (DTI) and q‐ball imaging (QBI). Materials and Methods: Monte Carlo simulations were carried out to measure the impact of outliers on DTI and QBI reconstruction in a single voxel. Methods to correct outliers based on q‐space interpolation and direction removal were then implemented and validated in real image data. Results: Corruption in a single voxel led to clear variations in DTI and QBI metrics. In real data, the method of q‐space interpolation was successful in identifying corrupted voxels and restoring them to values consistent with those of uncorrupted images. Conclusion: For images containing few gradient directions, where outlier removal was either impossible due to limited volumes or resulted in large changes in DTI/QBI metrics, q‐space interpolation proved to be the method of choice for image restoration. A simple decision support system is proposed to assist clinicians in the correction of their corrupted DW data. J. Magn. Reson. Imaging 2011;33:1491–1502. © 2011 Wiley‐Liss, Inc.