A review on noise estimation methods in multi-coil MRI

Abstract

Noise in an image is variation of information from the actual data. If there is any noise in the medical image then consequence will be huge. Physician won't be able to do proper diagnose of the disease and can also affects the quality of post processing techniques like registration and segmentation. Modern techniques use multiple coil MRI(Magnetic Resonance Imaging) where noise varies with position in the image i.e., non-stationary noise. Many methods are available for the nonstationary noise estimation in the literature but those needs multiple acquisitions and additional details. These limitations are avoided in homomorphic approach. Aja-Fernandez et.al used homomorphic approach on Devore et al. and is found to be a good method. In this paper homomorphic approach is applied on different known noise estimation methods in multi-coil MRI and a comparison is made. Visual comparison and quantitative analysis shows that homomorphic approach on Devore et.al gives better result for both Gausssian and Rician than other methods.

Multi-Exponential Transverse Relaxation Times Estimation From Magnetic Resonance Images Under Rician Noise and Spatial Regularization

“…A detailed analysis of the bias and the variance of these estimators for MRI signal estimation is given by [36]. In the sequel, it will be assumed that the noise variance σ 2 j , in each voxel is known or has been previously estimated using a dedicated algorithm (See for instance the survey of [38] and the references therein).…”

Kernel Principal Component Analysis of Coil Compression in Parallel Imaging

“…Due to the increased number of coils in parallel Magnetic Resonance Imaging, the numbers of coils (e.g., 128 channels) have been developed to improve the image quality of reconstruction and the sampling speed of acquisition [3–5]. On the other hand, the calculated cost increases as the number of coils increases, especially for coil-based reconstruction methods such as GRAPPA [2].…”

“…A number of coil compression methods have been proposed [6–19] to reduce computational time. They can be divided into two categories, one based on the hardware approach [5] and the other based on the software approach [6–19]. Those software-based coil compression methods provide a more flexible way to reduce computation workload.…”