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Quantification of Measurement Error in DTI: Theoretical Predictions and Validation

  • Casey Goodlett
  • P. Thomas Fletcher
  • Weili Lin
  • Guido Gerig
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4791)

Abstract

The presence of Rician noise in magnetic resonance imaging (MRI) introduces systematic errors in diffusion tensor imaging (DTI) measurements. This paper evaluates gradient direction schemes and tensor estimation routines to determine how to achieve the maximum accuracy and precision of tensor derived measures for a fixed amount of scan time. We present Monte Carlo simulations that quantify the effect of noise on diffusion measurements and validate these simulation results against appropriate in-vivo images. The predicted values of the systematic and random error caused by imaging noise are essential both for interpreting the results of statistical analysis and for selecting optimal imaging protocols given scan time limitations.

Keywords

Gradient Direction Weighted Little Square Frobenius Norm Tensor Model Baseline Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Casey Goodlett
    • 1
  • P. Thomas Fletcher
    • 2
  • Weili Lin
    • 3
  • Guido Gerig
    • 1
    • 4
  1. 1.Department of Computer Science, University of North Carolina 
  2. 2.School of Computing, University of Utah 
  3. 3.Department of Radiology, University of North Carolina 
  4. 4.Department of Psychiatry, University of North Carolina 

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