Denoising of complex MRI data by wavelet-domain filtering: Application to high-b-value diffusion-weighted imaging.
Författare
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).
Publiceringsår
2006
Språk
Engelska
Sidor
1114-1120
Publikation/Tidskrift/Serie
Magnetic Resonance in Medicine
Volym
56
Issue
5
Länkar
Dokumenttyp
Artikel i tidskrift
Förlag
John Wiley & Sons Inc.
Ämne
- Radiology, Nuclear Medicine and Medical Imaging
Nyckelord
- magnetic resonance imaging
- diffusion
- wavelet
- noise
- filtering
Status
Published
ISBN/ISSN/Övrigt
- ISSN: 1522-2594