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Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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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Imaging Studies IV: Magnetic Resonance Imaging

Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...

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A wavelet multiscale denoising algorithm for magnetic resonance (MR) images.

Xiaofeng Yang1, Baowei Fei

  • 1Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China ; Department of Radiology, Emory University, Atlanta, GA 30329, USA.

Measurement Science & Technology
|July 16, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel wavelet multiscale denoising method for Magnetic Resonance (MR) images, effectively reducing Rician noise while preserving crucial image details. The approach utilizes the Radon transform for enhanced MR image denoising and reconstruction.

Keywords:
Radon transformRician distributionmagnetic resonance imaging (MRI)multiscale denoisingtranslation invariantwavelet transform

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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
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Area of Science:

  • Medical Imaging
  • Signal Processing
  • Wavelet Theory

Background:

  • Magnetic Resonance (MR) images are susceptible to Rician noise, which can degrade image quality and hinder accurate diagnosis.
  • Existing denoising methods often struggle to effectively remove Rician noise without compromising important image features.

Purpose of the Study:

  • To propose a novel wavelet multiscale denoising method for MR images that explicitly accounts for the Rician noise distribution.
  • To enhance the quality of MR images by reducing Rician noise while preserving essential image details and features.

Main Methods:

  • A Radon transform-based approach is applied to MR images, incorporating noise statistics and a Gaussian noise model for sinogram processing.
  • A translation-invariant wavelet transform is used for multiscale decomposition of the MR sinogram to facilitate denoising.
  • Noise variance is estimated across different scales based on the Rician noise characteristics.
  • An inverse Radon transform is applied to reconstruct the denoised MR images.

Main Results:

  • Experimental validation using phantom, simulated, and human brain MR images demonstrated the superiority of the proposed method over traditional techniques.
  • The method effectively reduced Rician noise in MR images.
  • Key image details and features were successfully preserved during the denoising process.

Conclusions:

  • The proposed wavelet multiscale denoising method offers an effective solution for Rician noise reduction in MR images.
  • This technique shows significant potential for wide applications in MRI and other imaging modalities, improving diagnostic accuracy and image interpretation.