Spatially variable Rician noise in magnetic resonance imaging

• Published in: Medical image analysis Vol. 16; no. 2; pp. 536 - 548
• Main Authors: Maximov, Ivan I., Farrher, Ezequiel, Grinberg, Farida, Jon Shah, N.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.02.2012
• Subjects: Algorithms; Artificial Intelligence; Brain - anatomy & histology; Diffusion Magnetic Resonance Imaging - methods; Diffusion weighted imaging; Gaussian noise; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Low signal-to-noise ratio; Pattern Recognition, Automated - methods; Reproducibility of Results; Rician noise; Sensitivity and Specificity; Spatially variable noise; Spatially variable noise; Gaussian noise; Low signal-to-noise ratio; Rician noise; Diffusion weighted imaging
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2011.12.002

[Display omitted] ► Spatially variable noise correction algorithm is applied with the Rician correction. ► Automatic detection of a regions with the Gaussian or Rician noise distributions. ► Improved noise correction scheme for the diffusion-weighted imaging. Magnetic resonance images tend to be influenced by various random factors usually referred to as “noise”. The principal sources of noise and related artefacts can be divided into two types: arising from hardware (acquisition coil arrays, gradient coils, field inhomogeneity); and arising from the subject (physiological noise including body motion, cardiac pulsation or respiratory motion). These factors negatively affect the resolution and reproducibility of the images. Therefore, a proper noise treatment is important for improving the performance of clinical and research investigations. Noise reduction becomes especially critical for the images with a low signal-to-noise ratio, such as those typically acquired in diffusion tensor imaging at high diffusion weightings. The standard methods of signal correction usually assume a uniform distribution of the standard deviation of the noise across the image and evaluate a single correction parameter for the whole image. We pursue a more advanced approach based on the assumption of an inhomogeneous distribution of noise in space and evaluate correction factors for each voxel individually. The Rician nature of the underlying noise is considered for low and high signal-to-noise ratios. The approach developed here has been examined using numerical simulations and in vivo brain diffusion tensor imaging experiments. The efficacy and usefulness of this approach is demonstrated here and the resultant effective tool is described.