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Area of Science:

  • Medical Imaging
  • Signal Processing
  • Computational Science

Background:

  • Magnetic Resonance Imaging (MRI) is crucial for diagnostics.
  • Improving MRI efficiency and image quality is an ongoing challenge.
  • Transform-domain sparsification and compressed sensing (CS) offer potential solutions.

Purpose of the Study:

  • To evaluate transform-domain sparsification and CS techniques for MRI reconstruction.
  • To benchmark discrete wavelet transform (DWT), fast Fourier transform (FFT), discrete cosine transform (DCT), and basis pursuit (BP).
  • To assess reconstruction quality, computational efficiency, and clinical relevance.

Main Methods:

  • Simulated MRI reconstruction using DWT, FFT, and DCT with inverse transforms.
  • Implemented basis pursuit (BP) using L1-MAGIC for CS reconstruction.
  • Evaluated methods in MATLAB R2024b on DICOM images at various sampling rates.
  • Assessed performance using PSNR, RMSE, SSIM, execution time, memory usage, and compression efficiency.

Main Results:

  • DCT showed high PSNR and SSIM in simulations but is physically inconsistent with MRI acquisition.
  • Basis pursuit (BP) demonstrated a theoretically grounded approach with acceptable accuracy and clinical relevance.
  • Trade-offs between reconstruction quality and computational complexity were identified.

Conclusions:

  • Basis pursuit (BP) is a promising CS reconstruction algorithm for MRI.
  • The study provides a reproducible framework for evaluating MRI reconstruction techniques.
  • Future work should explore advanced CS algorithms for state-of-the-art MRI reconstruction.