The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Denoising of complex MRI data by wavelet-domain filtering: Application to high-b-value diffusion-weighted imaging.

På svenska

Author

Summary, in English

The Rician distribution of noise in magnitude magnetic resonance (MR) images is particularly problematic in low signal-to-noise ratio (SNR) regions. The Rician noise distribution causes a nonzero minimum signal in the image, which is often referred to as the rectified noise floor. True low signal is likely to be concealed in the noise, and quantification is severely hampered in low-SNR regions. To address this problem we performed noise reduction (or denoising) by Wiener-like filtering in the wavelet domain. The filtering was applied to complex MRI data before construction of the magnitude image. The noise-reduction algorithm was applied to simulated and experimental diffusion-weighted (DW) images. Denoising considerably reduced the signal standard deviation (SD, by up to 87% in simulated images) and decreased the background noise floor (by approximately a factor of 6 in simulated and experimental images).

Publishing year

2006

Language

English

Pages

1114-1120

Publication/Series

Magnetic Resonance in Medicine

Volume

56

Issue

5

Document type

Journal article

Publisher

John Wiley & Sons Inc.

Topic

  • Radiology, Nuclear Medicine and Medical Imaging

Keywords

  • magnetic resonance imaging
  • diffusion
  • wavelet
  • noise
  • filtering

Status

Published

ISBN/ISSN/Other

  • ISSN: 1522-2594