Adaptive non-local means denoising of MR images with spatially varying noise levels.
OBJECTIVE
To adapt the so-called nonlocal means filter to deal with magnetic resonance (MR) images with spatially varying noise levels (for both Gaussian and Rician distributed noise).


METHODS
Most filtering techniques assume an equal noise distribution across the image. When this assumption is not met, the resulting filtering becomes suboptimal. This is the case of MR images with spatially varying noise levels, such as those obtained by parallel imaging (sensitivity-encoded), intensity inhomogeneity-corrected images, or surface coil-based acquisitions. We propose a new method where information regarding the local image noise level is used to adjust the amount of denoising strength of the filter. Such information is automatically obtained from the images using a new local noise estimation method.


RESULTS
The proposed method was validated and compared with the standard nonlocal means filter on simulated and real MRI data showing an improved performance in all cases.


CONCLUSIONS
The new noise-adaptive method was demonstrated to outperform the standard filter when spatially varying noise is present in the images.
Manjón
José V
JV
Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universidad Politécnica de Valencia, Valencia, Spain. jmanjon@fis.upv.es
Coupé
Pierrick
P
Martí-Bonmatí
Luis
L
Collins
D Louis
DL
Robles
Montserrat
M
D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted