De-noising of 3D multiple-coil MR images using modified LMMSE estimator

• Published in: Magnetic resonance imaging 2018-10, Vol.52, p.102-117
• Main Authors: Yaghoobi, Nima, Hasanzadeh, Reza P.R.
• Format: Article
• Language: English
• Subjects: De-noising; GRAPPA reconstruction; Linear minimum mean square error; Multiple-coil MRI; Noncentral chi distribution
• ISSN: 0730-725X; 1873-5894
• DOI: 10.1016/j.mri.2018.06.014

De-noising is a crucial topic in Magnetic Resonance Imaging (MRI) which focuses on less loss of Magnetic Resonance (MR) image information and details preservation during the noise suppression. Nowadays multiple-coil MRI system is preferred to single one due to its acceleration in the imaging process. Due to the fact that the model of noise in single-coil and multiple-coil MRI systems are different, the de-noising methods that mostly are adapted to single-coil MRI systems, do not work appropriately with multiple-coil one. The model of noise in single-coil MRI systems is Rician while in multiple-coil one (if no subsampling occurs in k-space or GRAPPA reconstruction process is being done in the coils), it obeys noncentral Chi (nc-χ). In this paper, a new filtering method based on the Linear Minimum Mean Square Error (LMMSE) estimator is proposed for multiple-coil MR Images ruined by nc-χ noise. In the presented method, to have an optimum similarity selection of voxels, the Bayesian Mean Square Error (BMSE) criterion is used and proved for nc-χ noise model and also a nonlocal voxel selection methodology is proposed for nc-χ distribution. The results illustrate robust and accurate performance compared to the related state-of-the-art methods, either on ideal nc-χ images or GRAPPA reconstructed ones.