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Fast Compressed Sensing MRI Based on Complex Double-Density Dual-Tree Discrete Wavelet Transform.

Shanshan Chen1, Bensheng Qiu1, Feng Zhao2

  • 1Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230027, China.

International Journal of Biomedical Imaging
|May 11, 2017
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Summary
This summary is machine-generated.

This study introduces a new compressed sensing (CS) method for faster MRI scans. By combining a novel wavelet transform with an efficient algorithm, it significantly reduces image artifacts and improves quality.

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Area of Science:

  • Medical Imaging
  • Signal Processing
  • Computer Vision

Background:

  • Compressed sensing (CS) accelerates Magnetic Resonance Imaging (MRI) but discrete wavelet transform (DWT) can cause artifacts due to lack of translation invariance.
  • Artifacts in undersampled MRI reconstruction hinder image quality and diagnostic accuracy.

Purpose of the Study:

  • To propose a novel CS-based MRI reconstruction scheme to reduce visual artifacts.
  • To enhance the quality of undersampled MRI images using advanced signal processing techniques.

Main Methods:

  • The proposed method combines complex double-density dual-tree discrete wavelet transform (CDDDT-DWT) with the fast iterative shrinkage/soft thresholding algorithm (FISTA).
  • CDDDT-DWT offers shift invariance, high degree, and directional selectivity, while FISTA provides fast convergence and a simple design.

Main Results:

  • Experimental results show the novel approach significantly reduces visual artifacts in MRI reconstruction.
  • The method achieved higher peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), and structural similarity index (SSIM) compared to conventional CS methods.
  • Lower relative error was observed, indicating improved reconstruction accuracy.

Conclusions:

  • The combination of CDDDT-DWT and FISTA provides an effective CS-based reconstruction scheme for MRI.
  • This novel approach successfully reduces artifacts and enhances image quality in accelerated MRI acquisition.