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Compressed sensing MRI based on image decomposition model and group sparsity.

Xiaoyu Fan1, Qiusheng Lian2, Baoshun Shi2

  • 1School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; School of Electrical and Electronic Engineering, Anhui Science and Technology University, Chuzhou 233100, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China.

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|March 27, 2019
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Summary
This summary is machine-generated.

This study introduces a new cartoon-texture decomposition compressed sensing MRI (CD-MRI) algorithm. It enhances magnetic resonance (MR) image reconstruction from undersampled data, improving quality and noise reduction.

Keywords:
Adaptive tight frameGroup sparsityImage cartoon-texture decompositionMR image reconstructionTotal variation

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Area of Science:

  • Medical Imaging
  • Signal Processing
  • Computational Science

Background:

  • Compressed Sensing Magnetic Resonance Imaging (CSMRI) relies on effective image representation for accurate reconstruction from undersampled data.
  • Reconstructing high-quality MR images from highly undersampled data corrupted by noise remains a significant challenge.
  • Existing CSMRI methods struggle with adaptive sparse representation, impacting image fidelity.

Purpose of the Study:

  • To propose and evaluate a novel CSMRI algorithm, termed cartoon-texture decomposition CSMRI (CD-MRI), for improved MR image reconstruction.
  • To enhance the adaptive sparse representation of MR images by integrating tight frame-based group sparsity (TFGS) regularization.
  • To improve the robustness and quality of MR image reconstruction in the presence of noise and undersampling.

Main Methods:

  • The study proposes a tight frame-based group sparsity (TFGS) regularization to capture image structure information.
  • TFGS regularization is integrated into an image cartoon-texture decomposition model to form the CD-MRI algorithm.
  • The CD-MRI algorithm utilizes total variation for global sparse representation of cartoon components and TFGS for group sparse representation of texture components, solved via an alternating iterative method with majorization-minimization.

Main Results:

  • The proposed CD-MRI algorithm achieves high-quality MR image reconstruction from undersampled K-space data.
  • The algorithm effectively removes noise across various undersampling schemes.
  • Simulation experiments demonstrate superior image reconstruction performance and noise robustness compared to existing CSMRI algorithms.

Conclusions:

  • The CD-MRI algorithm, integrating TFGS regularization and cartoon-texture decomposition, significantly enhances MR image reconstruction quality.
  • The method offers improved sparse representation capabilities for both cartoon and texture image components.
  • CD-MRI presents a more robust and effective solution for CSMRI, particularly in noisy and undersampled scenarios.