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Multiple-mouse Neuroanatomical Magnetic Resonance Imaging
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Robustly reconstructing magnetic resonance images via structure decomposition.

Xiaomei Yang1, Wen Xu1, Ruisen Luo1

  • 1College of Electrical Engineering and Information Technology, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu, China.

Magnetic Resonance Imaging
|December 1, 2018
PubMed
Summary

This study introduces a new magnetic resonance (MR) imaging reconstruction model. It effectively balances reducing staircase effects and preserving image textures from under-sampled k-space data.

Keywords:
Alternating minimization schemeMagnetic resonance imagingNon-local total variationSecond-degree total variationStructure decomposition

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Area of Science:

  • Medical Imaging
  • Image Reconstruction
  • Magnetic Resonance Imaging

Background:

  • Reconstructing magnetic resonance (MR) images from highly under-sampled k-space data is challenging.
  • Existing methods like high-degree total variation (HDTV) reduce staircase effects but blur textures.
  • Non-local total variation (NLTV) preserves textures but can introduce artifacts in noisy images.

Purpose of the Study:

  • To develop a robust MR image reconstruction model addressing limitations of HDTV and NLTV.
  • To simultaneously reduce staircase artifacts and preserve image textures in highly under-sampled MR data.

Main Methods:

  • Decomposition of MR images into smooth and texture components.
  • Application of isotropic second-order TV for the smooth component to minimize staircase effects.
  • Utilizing NLTV and contourlet-based sparsity for the texture component to recover details and avoid artifacts.

Main Results:

  • The proposed model effectively reconstructs MR images from highly under-sampled k-space data.
  • Experimental results show satisfied reconstruction quality, balancing artifact reduction and texture preservation.

Conclusions:

  • The novel reconstruction model offers a robust solution for under-sampled MR imaging.
  • This approach enhances image quality by effectively managing staircase effects and preserving textures.