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COMPRESSED SENSING BASED INTENSITY NON-UNIFORMITY CORRECTION.

Snehashis Roy1, Aaron Carass1, Jerry L Prince1

  • 1Image Analysis and Communications Laboratory, Electrical and Computer Engineering, The Johns Hopkins University.

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|January 21, 2014
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Summary
This summary is machine-generated.

This study introduces a new compressed sensing method to correct radio-frequency (RF) coil inhomogeneity in brain MRI scans. The technique effectively removes shading artifacts, improving image quality for automated analysis, especially at higher field strengths.

Keywords:
7TMRIbias correctionbias fieldintensity inhomogeneityintensity non-uniformity

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Computational Neuroscience

Background:

  • Radio-frequency (RF) coil inhomogeneity causes shading artifacts in Magnetic Resonance (MR) images.
  • These artifacts degrade image quality and hinder automated analysis relying on intensity features.
  • Existing correction methods often assume smooth field inhomogeneity, limiting performance at higher field strengths.

Purpose of the Study:

  • To develop and evaluate a novel compressed sensing-based approach for correcting RF coil gain field inhomogeneity in human brain MR images.
  • To address the limitations of current methods in handling non-smooth inhomogeneity, particularly at higher field strengths.
  • To improve the robustness and performance of MR image analysis by mitigating intensity artifacts.

Main Methods:

  • A model-free, non-parametric, patch-based approach utilizing compressed sensing principles was developed.
  • The method corrects for gain field inhomogeneity without prior assumptions on field smoothness.
  • Performance was evaluated against the state-of-the-art N3 algorithm.

Main Results:

  • The proposed compressed sensing method achieved comparable performance to N3 on low-field MR images.
  • The algorithm significantly outperformed N3 on images with non-smooth inhomogeneity, such as those acquired at 7 Tesla (7T).
  • The patch-based compressed sensing approach demonstrated effectiveness in removing shading artifacts.

Conclusions:

  • Compressed sensing offers a powerful, flexible framework for correcting RF coil inhomogeneity in brain MR imaging.
  • This method provides superior performance over existing techniques when dealing with complex, non-smooth inhomogeneity.
  • The approach enhances the reliability of automated MR image analysis pipelines, particularly for high-field imaging.