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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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Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
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Interframe coding of magnetic resonance images.

A Nosratinia1, N Mohsenian, M T Orchard

  • 1Beckman Inst., Illinois Univ., Urbana, IL.

IEEE Transactions on Medical Imaging
|January 1, 1996
PubMed
Summary

This study introduces an advanced interframe coding method for magnetic resonance (MR) images. It overcomes previous limitations by using a novel estimation model and median filtering to effectively reduce noise and improve image compression.

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Area of Science:

  • Medical Imaging
  • Image Compression
  • Signal Processing

Background:

  • Interframe coding for medical images, especially magnetic resonance (MR) images, has faced challenges due to unsuitable estimation models and inherent MR noise.
  • Previous attempts to leverage interframe redundancies in MR image coding have been largely unsuccessful.

Purpose of the Study:

  • To develop a novel interframe coding method for MR images that addresses the limitations of existing techniques.
  • To improve the efficiency and effectiveness of MR image compression for medical applications.

Main Methods:

  • A continuous affine mapping model, optimized by deforming triangles, is employed for interframe estimation.
  • A median filter is integrated within the estimation loop to mitigate the effects of inherent magnetic resonance imaging (MRI) noise.
  • Residue frames are quantized using a zero-tree wavelet coder with arithmetic entropy coding.

Main Results:

  • The proposed method successfully utilizes interframe redundancies in MR images, a feat not achieved by prior techniques.
  • The integration of a median filter effectively handles the inherent noise in MRI data.
  • The zero-tree wavelet coder with arithmetic entropy coding enables progressive transmission of quantized residue frames.

Conclusions:

  • The developed interframe coding method offers a significant advancement in MR image compression.
  • The technique's ability to handle noise and support progressive transmission makes it highly attractive for medical imaging applications.
  • This approach enhances the efficiency of storing and transmitting medical images, potentially improving diagnostic workflows.