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Stationary wavelet transform for under-sampled MRI reconstruction.

Mohammad H Kayvanrad1, A Jonathan McLeod1, John S H Baxter1

  • 1Robarts Research Institute, Western University, Canada; Biomedical Engineering, Western University, Canada.

Magnetic Resonance Imaging
|August 19, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces the stationary wavelet transform (SWT) for magnetic resonance imaging (MRI) reconstruction, significantly reducing visual artifacts compared to the traditional decimated wavelet transform (DWT). SWT offers improved accuracy and faster convergence in compressed sensing MRI.

Keywords:
Accelerated MR imagingCompressed sensingMRI reconstructionParallel imagingSparse reconstructionk-space under-sampling

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Area of Science:

  • Medical Imaging
  • Signal Processing
  • Computational Science

Background:

  • Undersampled magnetic resonance imaging (MRI) reconstruction often utilizes sparsity constraints, such as lp-penalties, alongside coil sensitivity data for parallel imaging.
  • Traditional compressed sensing approaches employing the decimated wavelet transform (DWT) can introduce visual pseudo-Gibbs artifacts due to the lack of translation invariance in the wavelet basis.

Purpose of the Study:

  • To investigate the efficacy of the translation-invariant stationary wavelet transform (SWT) in mitigating pseudo-Gibbs artifacts in compressed sensing MRI reconstruction.
  • To compare the performance of SWT-based reconstruction against DWT-based methods, considering various reconstruction constraints.

Main Methods:

  • Reconstruction of undersampled MRI data using sparsity penalties applied to stationary wavelet transform (SWT) coefficients.
  • Comparison of SWT reconstructions with traditional decimated wavelet transform (DWT) reconstructions.
  • Inclusion of additional constraints such as coil sensitivity profiles and total variation in the reconstruction process.
  • Extensive experimental validation using in vivo MRI data, with a focus on multi-channel acquisitions.

Main Results:

  • Penalizing SWT coefficients significantly reduces visual pseudo-Gibbs artifacts compared to penalizing DWT coefficients.
  • SWT-based reconstructions demonstrate lower error values and faster convergence rates than DWT-based reconstructions.
  • The benefits of SWT are observed across various reconstruction constraints, including parallel imaging and total variation.

Conclusions:

  • The stationary wavelet transform (SWT) is a superior alternative to the decimated wavelet transform (DWT) for compressed sensing MRI reconstruction, effectively minimizing visual artifacts.
  • SWT-based methods offer enhanced reconstruction quality, improved accuracy, and faster convergence, making them highly suitable for multi-channel MRI acquisitions.