Yaghoobi Nima, Hasanzadeh Reza P R
Department of Electrical Engineering, University of Guilan, Rasht, Iran.
Department of Electrical Engineering, University of Guilan, Rasht, Iran.
Magn Reson Imaging. 2018 Oct;52:102-117. doi: 10.1016/j.mri.2018.06.014. Epub 2018 Jun 20.
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.
去噪是磁共振成像（MRI）中的一个关键课题，其重点在于在噪声抑制过程中减少磁共振（MR）图像信息的损失并保留细节。如今，多线圈MRI系统因其在成像过程中的加速作用而比单线圈系统更受青睐。由于单线圈和多线圈MRI系统中的噪声模型不同，大多数适用于单线圈MRI系统的去噪方法在多线圈系统中并不适用。单线圈MRI系统中的噪声模型是莱斯分布，而在多线圈系统中（如果在k空间中没有子采样或者在线圈中正在进行GRAPPA重建过程），它服从非中心卡方分布（nc-χ）。本文针对受nc-χ噪声破坏的多线圈MR图像，提出了一种基于线性最小均方误差（LMMSE）估计器的新滤波方法。在所提出的方法中，为了实现体素的最优相似性选择，使用了贝叶斯均方误差（BMSE）准则并针对nc-χ噪声模型进行了证明，还针对nc-χ分布提出了一种非局部体素选择方法。结果表明，与相关的现有方法相比，无论是在理想的nc-χ图像还是GRAPPA重建的图像上，该方法都具有稳健且准确的性能。
