Impact of outliers on diffusion tensor and Q-ball imaging: clinical implications and correction strategies

J Magn Reson Imaging. 2011 Jun;33(6):1491-502. doi: 10.1002/jmri.22577.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Brain / pathology
  • Brain Mapping / methods
  • Computer Simulation
  • Diffusion
  • Diffusion Tensor Imaging / methods*
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Magnetic Resonance Imaging / methods
  • Models, Statistical
  • Monte Carlo Method
  • Static Electricity