Estimation of the noise in magnitude MR images.

By Jan Sijbers, A. J. den Dekker, J. Van Audekerke, M. Verhoye, D. Van Dyck. 1998

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

An overview of deep learning in medical imaging focusing on MRI (2018, background): “Estimation of noise and image denoising in MRI has been an important field of research for many years [171,172], employing a plethora of methods.”
Image reconstruction in SNR units: A general method for SNR measurement † (2005, unknown): “[3]”
Noise and Signal Estimation in Magnitude MRI and Rician Distributed Images: A LMMSE Approach (2008, unknown): “Noise can also be estimated using multiple images taken under identical conditions [23].”
Denoising functional MR images: a comparison of wavelet denoising and Gaussian smoothing (2004, background): “In the MR literature, however, the noise in MR images is assumed to be Rician distributed [16], [2], [ 17 ].”
Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time (2003, unknown): “From [25] we know that Rician distribution approaches a Gaussian distribution at high SNR.”