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Related Concept Videos

Gradient and Del Operator01:14

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In mathematics and physics, the gradient and del operator are fundamental concepts used to describe the behavior of functions and fields in space. The gradient is a mathematical operator that gives both the magnitude and direction of the maximum spatial rate of change. Consider a person standing on a mountain. The slope of the mountain at any given point is not defined unless it is quantified in a particular direction. For this reason, a "directional derivative" is defined, which is a vector...
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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
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Normalized gradient fields for nonlinear motion correction of DCE-MRI time series.

Erlend Hodneland1, Arvid Lundervold2, Jarle Rørvik3

  • 1Department of Biomedicine, University of Bergen, Bergen, Norway.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|January 21, 2014
PubMed
Summary

This study introduces a new method for correcting motion artifacts in dynamic contrast-enhanced MRI (DCE-MRI) of moving organs. Normalized gradients offer improved accuracy for image registration compared to mutual information.

Keywords:
DCE-MRIImage registrationMR renographyMutual informationNormalized gradients

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Area of Science:

  • Medical Imaging
  • Image Processing
  • Biomedical Engineering

Background:

  • Dynamic contrast-enhanced MRI (DCE-MRI) captures organ motion and contrast agent dynamics.
  • Motion artifacts in DCE-MRI hinder accurate physiological parameter assessment.
  • Deformable image registration is crucial for correcting motion in DCE-MRI.

Purpose of the Study:

  • To present a novel partial differential equation-based method for deformable multimodal image registration.
  • To address motion challenges in DCE-MRI time series, particularly for moving organs.
  • To compare the performance of normalized gradients and mutual information as cost functionals for motion correction.

Main Methods:

  • A partial differential equation-based method using normalized gradients and Fourier transform.
  • Solving Euler-Lagrange equations within a multilevel hierarchy.
  • Validation on ten DCE-MRI datasets from moving kidney studies.

Main Results:

  • Both normalized gradients and mutual information proved effective for DCE-MRI motion correction.
  • Normalized gradients demonstrated superior performance over mutual information based on multiple metrics.
  • The method was validated on real-world DCE-MRI data from moving kidneys.

Conclusions:

  • Normalized gradients are a viable and accurate alternative to mutual information for DCE-MRI registration.
  • The proposed method shows promise for clinical applications involving DCE-MRI of moving organs.
  • Accurate motion correction enhances the reliability of physiological parameter assessment from DCE-MRI data.