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Magnetic Resonance Imaging01:24

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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MRI Denoising Using Pixel-Wise Threshold Selection.

Nimesh Srivastava1,2, Gyana Ranjan Sahoo1, Henning U Voss3

  • 1Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853, USA.

IEEE Access : Practical Innovations, Open Solutions
|December 6, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel pixel-wise denoising method for Magnetic Resonance Imaging (MRI) that significantly improves image quality and reduces scan times. The technique enhances structural detail and noise reduction, outperforming traditional methods.

Keywords:
Magnetic resonance imagingSF-SVDcontrast-to-noise ratiodenoisingimage denoisingpeak-signal-to-noise ratiopixel-wise noise threshold selectionpixel-wise thresholdingsingular value decompositionstructural similarity index

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Area of Science:

  • Medical Imaging
  • Image Processing
  • Biomedical Engineering

Background:

  • Magnetic Resonance Imaging (MRI) offers non-invasive medical visualization.
  • A key challenge in MRI is balancing image quality with acquisition time.
  • Existing denoising methods often cause image distortion or inadequate noise removal.

Purpose of the Study:

  • To introduce a novel pixel-wise denoising technique for Magnetic Resonance (MR) images.
  • To address the limitations of global thresholding in current MRI denoising algorithms.
  • To improve both the visual quality and acquisition speed of MRI scans.

Main Methods:

  • A novel pixel-wise thresholding approach using singular vectors from singular value decomposition (SVD).
  • Localized singular values are used as thresholds at each pixel to account for spatial noise variations.
  • Validation performed on MR images of a standard phantom approved by the Magnetic Resonance Accreditation Program (MRAP).

Main Results:

  • Denoised MR images exhibit superior visual quality and recover suppressed structural information.
  • Significant improvements observed: Peak-Signal-to-Noise-Ratio (PSNR) increased by ≥ 18%, Contrast-to-Noise-Ratio (CNR) by ≥ 200%.
  • Structural Similarity Index (SSIM) of ≥ 0.95 indicates effective recovery of crucial structural information.
  • The method demonstrated superior performance compared to standard MRI denoising filters.
  • Scan time was reduced by 2-3 times.

Conclusions:

  • The pixel-wise denoising technique effectively removes noise and enhances image quality in MRI.
  • The method preserves critical structural details, outperforming conventional approaches.
  • This technique has the potential for integration into MRI systems to enable faster, higher-quality imaging.