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Related Concept Videos

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model
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Estimating kinetic parameter maps from dynamic contrast-enhanced MRI using spatial prior knowledge.

Bernd Michael Kelm1, Bjoern H Menze, Oliver Nix

  • 1Siemens Corporate Technology, 91058 Erlangen, Germany. bmkelm@web.de

IEEE Transactions on Medical Imaging
|April 17, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using spatial information to improve dynamic contrast-enhanced MRI (DCE-MR) parameter estimation. The generalized Gaussian Markov random field (GGMRF) approach reduces errors and bias in microvascular imaging, enhancing diagnostic accuracy.

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Computational Biology

Background:

  • Dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging analyzes microvascular structure by tracking contrast agent dynamics.
  • Current pharmacokinetic modeling for DCE-MR parameter estimation suffers from high variance and bias in least squares estimates.
  • Accurate kinetic parameter estimation is crucial for reliable in vivo microvascular analysis.

Purpose of the Study:

  • To reduce bias and variance in DCE-MR kinetic parameter estimation.
  • To improve the accuracy of microvascular structure analysis using DCE-MR.
  • To develop a computationally efficient algorithm for enhanced DCE-MR parameter mapping.

Main Methods:

  • Application of spatial prior knowledge using a generalized Gaussian Markov random field (GGMRF).
  • Computation of maximum a posteriori (MAP) solutions for entire parameter maps.
  • Development of a generalized iterated conditional modes (ICM) algorithm using block-based processing for faster convergence.
  • Validation using simulated DCE-MR images and a prostate DCE-MRI patient dataset.

Main Results:

  • Significant reduction in root mean square error (RMSE) and variance of parameter estimates.
  • Demonstrated reduction in estimation bias, including mean residual bias (MRB), in both simulated and patient data.
  • Proposed generalized ICM algorithm shows considerably faster convergence compared to conventional ICM.
  • Minimal increase in computation time (1.18x-1.51x) compared to single-voxel analysis.

Conclusions:

  • The GGMRF approach with a generalized ICM algorithm effectively reduces bias and variance in DCE-MR parameter estimation.
  • This method enhances the accuracy of in vivo microvascular imaging, particularly for prostate cancer studies.
  • The developed algorithm offers a computationally feasible solution for improving DCE-MR analysis in clinical settings.