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Improved Reconstruction of MR Scanned Images by Using a Dictionary Learning Scheme.

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  • 1Department of Electrical Engineering, International Islamic University Islamabad, Islamabad 44000, Pakistan. Shahid.ikram@iiu.edu.pk.

Sensors (Basel, Switzerland)
|April 26, 2019
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Summary

Compressed sensing (CS) in biomedical imaging enables accurate MRI reconstruction from undersampled data. The novel SiFo method enhances image quality by combining dictionary learning and sparse coding, outperforming traditional techniques.

Keywords:
and dictionary learning based MRI (DLMRI)compressed sensing (CS)dictionary learningfocal underdetermined system solver (FOCUSS)magnetic resonance imaging (MRI)simultaneous code word optimization (SimCO)

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Area of Science:

  • Biomedical imaging
  • Medical imaging reconstruction
  • Compressed sensing

Background:

  • Compressed sensing (CS) allows accurate biomedical image reconstruction by leveraging image sparsity.
  • The quality of CS reconstruction relies on effective sparsifying transforms like wavelets, curvelets, or total variation (TV).
  • High undersampling in CS can lead to aliasing artifacts and noise in reconstructed images.

Purpose of the Study:

  • To introduce a novel framework for enhanced biomedical image reconstruction using compressed sensing.
  • To address the challenges of aliasing and noise in undersampled MR images.
  • To develop an improved reconstruction technique that outperforms existing methods.

Main Methods:

  • The study proposes the SiFo (Simultaneous code word optimization and Focal underdetermined system solver) scheme.
  • SiFo integrates patch-based dictionary learning (SimCO) with sparse representation (FOCUSS).
  • An alternating reconstruction strategy is employed, involving dictionary learning, aliasing/noise elimination, and k-space data restoration.

Main Results:

  • The SiFo technique demonstrated superior performance in reconstructing both phantom and real brain images.
  • Experiments were conducted using various sampling schemes under both noisy and noiseless conditions.
  • SiFo showed improved results compared to conventional dictionary learning-based MRI (DLMRI) reconstruction methods like K-SVD with OMP.

Conclusions:

  • The proposed SiFo method offers a significant advancement in compressed sensing-based biomedical image reconstruction.
  • SiFo effectively mitigates aliasing and noise, leading to higher quality reconstructed images.
  • This novel approach holds promise for improving MRI acquisition and diagnostic capabilities.