title: Denoising of complex MRI data by wavelet-domain filtering: application to high-b-value diffusion-weighted imaging.
abstract: 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).
authors.affiliation: Department of Medical Radiation Physics, Lund University, Lund, Sweden. Ronnie.Wirestam@med.lu.se
authors.forename: Ronnie
authors.initials: R
authors.lastname: Wirestam
authors.forename: Adnan
authors.initials: A
authors.lastname: Bibic
authors.forename: Jimmy
authors.initials: J
authors.lastname: Lätt
authors.forename: Sara
authors.initials: S
authors.lastname: Brockstedt
authors.forename: Freddy
authors.initials: F
authors.lastname: Ståhlberg
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000033:anatomy & histology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted