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MR image reconstruction based on framelets and nonlocal total variation using split Bregman method.

Varun P Gopi1, P Palanisamy, Khan A Wahid

  • 1Department of Electronics and Communication Engineering, National Institute of Technology (NIT), Tiruchirappalli, India, vpgcet@gmail.com.

International Journal of Computer Assisted Radiology and Surgery
|September 10, 2013
PubMed
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A new split Bregman algorithm enhances sparse magnetic resonance (MR) image reconstruction. This method improves image quality by preserving details using nonlocal total variation (NLTV) and framelet sparsity.

Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Signal Processing

Background:

  • Accelerated data acquisition in Magnetic Resonance (MR) imaging necessitates efficient reconstruction algorithms, particularly with sparse sampling.
  • Sparse sampling in MR imaging reduces scan times but poses challenges for image reconstruction quality.

Purpose of the Study:

  • To develop and evaluate a novel algorithm for sparse MR image reconstruction.
  • To address the challenges of sparse sampling by integrating nonlocal total variation (NLTV) and framelet sparsity.
  • To utilize the split Bregman method for solving the proposed reconstruction model.

Main Methods:

  • The proposed method reconstructs MR images from undersampled k-space data by minimizing a combination of NLTV, least squares data fitting, and framelet terms.

Related Experiment Videos

  • Nonlocal total variation (NLTV) and framelet sparsity are formulated as L1-regularization functionals.
  • The split Bregman method is employed to solve the optimization problem.

    • Main Results:

    • The new algorithm demonstrated superior performance in preserving geometrical features, textures, and fine structures in MR images across various sampling rates.
    • Comparative evaluations showed favorable reconstruction accuracy and computational efficiency against existing methods.
    • Both qualitative and quantitative analyses confirmed the effectiveness of the proposed approach.

    Conclusions:

    • An efficient algorithm for compressed MR image reconstruction has been developed, effectively combining NLTV and framelet sparsity via the split Bregman method.
    • The algorithm enhances image quality by preserving edges and boundaries more accurately (NLTV) and improving overall image fidelity (framelets).
    • The proposed method shows superiority over alternative algorithms for compressed MR image reconstruction.